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Intelligent Spectrum Learning for Wireless Networks With Reconfigurable Intelligent Surfaces


Abstract and Figures

Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL), which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the active/inactive binary status of the RIS elements. Simulation results validate the performance improvement offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach.
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Intelligent Spectrum Learning for Wireless Networks
with Reconfigurable Intelligent Surfaces
Bo Yang, Xuelin Cao, Chongwen Huang, Chau Yuen, Fellow, IEEE,
Lijun Qian, Senior Member, IEEE, and Marco Di Renzo, Fellow, IEEE
Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the relia-
bility of wireless communications, which is capable of reflecting the desired signals through appropriate
phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering
signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-
noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-
intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL),
which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help
the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a
distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the
active/inactive binary status of the RIS elements. Simulation results validate the performance improvement
offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach.
Index Terms
Reconfigurable intelligent surface, intelligent spectrum learning, convolutional neural network.
Due to the dynamic nature of the wireless environment that results in severe signal fluctuations caused
by multipath fading and the presence of large obstacles, the wireless link between a desired cellular
B. Yang, X. Cao, and C. Yuen are with the Engineering Product Development Pillar, Singapore University of Technology and
Design, Singapore 487372 (e-mail: bo yang, xuelin cao,
C. Huang is with Zhejiang Provincial Key Lab of information processing, communication and networking, Zhejiang University,
No.38 Zheda Road, Hangzhou, 310007, P.R. China (e-mail:
L. Qian is with the Department of Electrical and Computer Engineering and CREDIT Center, Prairie View A&M University,
Texas A&M University System, Prairie View, TX 77446, USA (e-mail:
M. Di Renzo is with Universit´
e Paris-Saclay, CNRS, CentraleSup´
elec, Laboratoire des Signaux et Syst`
emes, 3 Rue Joliot-Curie,
91192 Gif-sur-Yvette, France. (
RIS Controller 1
Reecting element
Reecting signal
CSI feedback
Reecting element
CSI feedback
RIS Controller K
Reecting signal
Reecting element
CSI feedback
Reecting element
CSI feedback
Reecting signal
Distributed RIS Controller 1
deployed with a trained CNN model
Distributed RIS Controller K
deployed with a trained CNN model
control and
phase shift
RF signal
RF signal
control and
phase shift
No reecting signal
Other user signals
Desired user signals
Fig. 1: In (a), a traditional multiple-user uplink RIS-assisted wireless communication system with KRISs is shown, where we
assume that Lusers transmit to the BS at same time and frequency, and all RISs serve one user at a time. So, the other users
are considered as interferers. In (b), the proposed ISL-enabled RIS-assisted wireless communication system is shown. Each RIS
controller is deployed with a trained CNN model to identify the interfering users from the incident RF signals, so as to optimize
the operation of the RISs in a distributed way.
user and a base station (BS) may not be reliable enough or may even undergo a complete outage. To
tackle this issue, reconfigurable intelligent surfaces (RISs) have been proposed to improve the received
signal-to-interference-plus-noise ratio (SINR) at the users by appropriately reflecting the incident signals
and generating directional beams [1].
In the literature, some preliminary works investigated the optimization of RIS-assisted wireless com-
munications. In [2], a joint transmit power allocation and phase shift design was developed to maximize
the energy efficiency. In [3], the authors considered a downlink RIS-assisted multiuser communication
system and studied a joint transmission and reflection beamforming problem to minimize the total transmit
power. In [4], the channel estimation problem was investigated for an RIS-aided wireless communication
system by jointly optimizing the training sequence of the transmitter and the reflection pattern of the RIS.
Furthermore, an RIS-assisted anti-jamming solution was proposed for securing wireless communications
via reinforcement learning [5]. In [6], the authors introduced RISs in mobile edge computing systems,
where a joint design of computing and communications was developed to minimize the computational
However, most of the existing works assume either that no interference exists, which rarely occurs
in practice, or that the interference is known and can be taken into account, which is not trivial to
estimate since the interference is usually dynamically changing. These issues are exacerbated in RIS-
aided systems, since they are nearly-passive surfaces with no active sensing capabilities for channel and
interference estimation [7]. In order to elucidate the problem at hand, let us consider the case study
depicted in Fig. 1(a), in which we consider a practical scenario where all the RISs serve one desired user
(e.g., U1). In the analyzed case study, the other users (e.g., Ul,l[2, L]) are considered as interfering
users for U1at each RIS. As a result, the signal reflected by each RIS is a mixture of the desired signal
from U1and the interfering signals from the other users. In this context, the received SINR at the BS
via an RIS that does not account for the interfering signals could be even worse than the SINR of the
direct link. This impact of the interference is, in particular, more severe if the interfering devices are
close to the desired user, e.g., in Fig. 1(a) U2may cause severe interference to U1at RIS1when the
angle between them (i.e., λ) is small.
To overcome these challenges, in this paper, we empower a conventional RIS-assisted wireless system
with ‘intelligent spectrum learning (ISL) capabilities’, by leveraging appropriately trained convolutional
neural networks (CNN) at the RIS controller in order to predict/estimate the interfering devices from the
incident signals, as highlighted in Fig. 1(b). In the proposed system, the active-inactive (or ON-OFF) status
of each RIS1and the corresponding phase shifts need to be carefully optimized, since the interference
distribution at each RIS is, in general, different. The corresponding SINR maximization problem turns
out to be a mixed-integer nonlinear program (MINLP), which is usually difficult to solve. To tackle this
issue, we decompose the original problem into two subproblems, which are solved independently and in a
distributed manner. The proposed solution equips a conventional RIS with the capability to dynamically
‘think-and-decide’ whether reflecting or not the incident signals through the proposed distributed ISL
We consider an RIS-assisted uplink wireless system that consists of one BS, a set Kof KRISs, and
a set Lof Lusers, where K1and L>Kusually hold. We assume that the BS allocates all the
RISs to serve one user at a time in order to improve the quality of the wireless link. Also, the direct
links between the users and the BS are available. Under these assumptions, all the other users act as
interferers for the intended user either through the direct links or through the links reflected by the RISs.
Each RIS operates as a nearly-passive surface, i.e., the RIS elements are passive but the RIS controller
1In this paper, the ON-OFF status is referred to having the entire RIS ON or OFF, i.e., either all the elements of the RIS are
turned ON or all the elements of the RIS are turned OFF.
may consume power [8]. In particular, the RIS controller is equipped with a CNN, as shown in Fig. 1(b).
The CNN is discussed in the next sections.
A. RIS-Assisted Communication Model
Each RIS, denoted as Rk,k∈ K, is equipped with Nkreflecting elements, which can be appropriately
configured by the RIS controller to reflect the signals of the users towards the BS. In general, the RISs can
be appropriately deployed so that line-of-sight (LoS) links can be established with the BS and, possibly,
the users. We assume that the channel state information (CSI) of all channels involved is perfectly known
at the BS2, which, in turn, can feed back the CSI to the RIS controller via a dedicated control channel [3],
Each RIS can be in two possible states: ON and OFF. We introduce a binary variable βk∈ {0,1}
to indicate the ON-OFF state of Rk.βk= 1 indicates that Rkis ON, which means that it reflects the
incident signals, while βk= 0 indicates Rkis OFF, which means that it does not reflect any signals.
As for the kth RIS that is ON, the amplitude reflection coefficient is assumed to be equal to one for
all the Nkreflecting elements and the phase reflection matrix is
Φk= diag ek
1, ek
2, ..., ek
where Θk=θk
1, θk
2, ..., θk
Nkdenotes the vector of phase shifts that can be optimized by Rk.
B. Wireless Channel Model
We consider an RIS-aided uplink wireless system, where the channels from the desired user (denoted as
Ul) to Rk, from Rkto the BS, and from the mth interfering user (denoted as Um,m∈ L, m 6=l) to Rk, are
hl,k CNk×1,gkC1×Nk, and hm,k CNk×1, respectively. The channel gains of the direct links from
Ulto BS and from Umto BS are denoted by hd,l and hd,m, respectively. These channels are assumed to
be perfectly estimated and quasi-static, hence remaining nearly-constant during the transmission time [3].
Without loss of generality, we assume that the users are randomly distributed and have time-varying
traffic demands. This implies that the users may not be all active during the considered transmission
time3. In particular, the total number of interfering users for Ulis given by ωl=Pm∈L,m6=lαm, where
αm∈ {0,1}is a binary variable that indicates that Umis active (αm= 1) or inactive (αm= 0) and
therefore can cause or not interference to Ul, respectively.
2Many papers in the literature have tackled the issue of estimating and reporting the CSI, e.g., [7], [9]. Therefore, this problem
is not addressed in this paper and it is left to a future research work.
3We assume that the active interfering users remain unchanged during the transmission time of the desired user.
Accordingly, the set of active interfering users is W={Um|αm= 1}, where m6=land m∈ L.
Since the number of active interferers is dynamic, it is not easy to estimate the interference distribution
over time. The received signal at the BS depends on the desired signal sent from Ul(including the direct
link and the link reflected by the RIS), the interference from the interfering users in W, and the white
Gaussian noise:
yl= hd,l +
| {z }
Desired signal from Ul
m∈W hd,m +
m,lβkgkΦkhm,k !pmsm
| {z }
Interference from other users
where pland pmdenote the transmit power of Uland Um, respectively, sland smare the unit-power
information signals sent from Uland Um, respectively, and nl∼ CN(0, σ2)is the white Gaussian noise.
In addition, ξk
m,l [0,1] accounts for the impact of the interference caused by Umto Uland that is
associated to the kth RIS, as clarified in Assumption 1.
Assumption 1. We assume that the impact of the strength of the interference reflected by an RIS is
inversely proportional to the difference of the angles of incidence between the desired user and the
interfering users at the RIS (see λin Fig. 1(b) for an example).
C. Problem Formulation and Analysis
Let B={β1, β2, ..., βK}denote the RIS binary decision vector that collects the binary variables that
identify the ON-OFF status of the RISs. The set of RISs that are active is R={Rk|βk=1},k∈ K.
Accordingly, the SINR at the BS for the intended user Ulis
plhd,l +PK
k=1 βkgkΦkhl,k
m=1 pmhd,m +PK
k=1 ξk
Our objective is to maximize the SINR in (3), by optimizing the RIS binary activation vector (B), and
the phase shifts matrix of the active RISs which is denoted as Θ={Θ1,Θ2, ..., ΘK}. To this end, we
need to solve the following optimization problem:
P1: max
s.t. βk∈ {0,1},k∈ K,(4b)
n= 1,n[1, Ni],i∈ R.(4c)
Constraint (4b) indicates that the kth RIS can only be ON (i.e., βk= 1) or OFF (i.e., βk= 0) at one
time. Constraint (4c) indicates that each RIS reflecting element can only provide a phase shift θi
without amplifying the signals.
Conv+ReLU layer
FC layer
FC layer
Feature Extraction
Trained CNN Model
Conv+ReLU layer
users set
RIS ON/OFF state
Distributed RIS
Binary Control
Incident Signal
Trained CNN
model #1
Trained CNN
model #2
Trained CNN
model #L
devices set
Fig. 2: RF trace collection scenario is shown in (a), where Ltransceivers (users) are scheduled for transmission towards a
receiver (BS). The proposed ISL-aided RIS control structure is shown in (b), where the input to the CNN is the incident RF
signals and the output is the set of interfering users.
Our Proposal: We observe that P1is an MINLP, which is NP-hard and whose global optimal solution
is, in general, difficult to obtain. In addition, traditional optimization methods may be computationally
intensive. An emerging approach to tackle this issue is to apply deep learning methods to solve P1at
a reduced computational complexity [10], [11]. P1, however, may not be easy to solve even using deep
learning methods, since the number of interfering users is a random variable that is unknown. In addition,
during the channel estimation phase, the RISs cannot estimate on their own the active interferers because
they only reflect the incident signals in a passive manner. This makes the solution of P1even more
difficult. To address this challenge, we propose a distributed control mechanism that solves P1with the
aid of the ISL algorithm.
In this section, the ISL-aided distributed RIS control mechanism is introduced. The proposed approach
leverages appropriately trained CNNs at the controller of the RISs, which can identify the active interfering
users in a distributed way.
A. Intelligent Spectrum Learning
ISL is a multi-class classification algorithm that, based on a CNN, returns the set of interfering users
for each intended user Ul. To design the ISL algorithm, three main aspects need to be discussed: 1) RF
traces collection, 2) offline CNN training, and 3) online CNN inference.
1) RF Traces Collection: As far as the RF data collection phase is concerned, historical RF traces are
collected using a universal software radio peripheral (USRP2) testbed, which is wired connected (e.g.,
Gigabit Ethernet) to a host PC with an implementation of the GNU Radio, as illustrated in Fig. 2(a). In
particular, the users are emulated through a laptop that is mainly responsible for baseband processing while
a USRP2 platform is used for the up-conversion, the digital-to-analog (D/A) conversion, and wireless
transmission of the signals. As far as the BS is concerned, another USRP2 module first receives the
signals from the radio interface and then performs A/D and down-conversion. Subsequently, the laptop
receives the signals from the USRP2 via the Ethernet and executes the baseband processing. Finally, the
Inphase (I) and Quadrature (Q) sequences are stored as a file. In particular, the experimental setup for RF
data collection using the USPR2 is performed by using signals at 2.4GHz carrier frequency with 1MHz
bandwidth. In order to collect realistic RF signals in the presence of interference, we let multiple USRP2
units transmit RF signals to an USRP2 receiver. The RF traces have been collected as I/Q sequences, by
including the wireless channel, for a wide range of SNR (e.g., from 0to 20 dB with interval of 5dB)
in order to account for different interfering cases [12].
2) CNN Offline Training: The acquired RF traces have been used for training the CNN architecture
illustrated in Fig. 2(b), where each convolutional layer is followed by a rectified linear units activation
function for feature extraction. Fully-connected (FC) layers are used to classify the signals by using
the softmax activation function for the output layer [13]. The training algorithms is based on the Adam
algorithm that uses the cross entropy as the loss function. The CNN model is trained offline using
TensorFlow on a GPU cluster (NVIDIA Tesla P100-PCIE-16GB).
Even though the training of the CNN does not account for all possible channel conditions, the
generalization property of deep learning enables the trained CNN to infer channel conditions not included
in the training dataset [14]. It is also noteworthy that several methods have been proposed to scale up the
training process of deep neural networks across GPU clusters, which helps to further reduce the runtime
of the offline training. Once the CNN model is appropriately trained, it can directly infer incident signals
in near real-time. In other words, the proposed ISL-based framework moves the complexity from online
computation to offline training.
3) CNN Online Inference: The CNN is, in particular, trained in order to return the set of active
interfering users based on different input signals at each RIS. Specifically, the received RF signals first
undergo A/D conversion and frequency down-conversion. Then the baseband I/Q sequences are fed into
the trained CNN model to perform online inference at the RIS controller.
By performing feed-forward calculation via the CNN model (i.e., online inference), the intefering users
set for the desired user Ulis obtained as
{Um|eαm= 1},m∈ L,m6=l, If eωl1,
,If eωl= 0,
where eαmdenotes the inferred state flag of Um, and eωlindicates the inferred total number of intefering
users in the incident signal.
4) An Illustrative ISL Example: To better understand the proposed ISL algorithm, we illustrate an
example with only two users (denoted as U1and U2), and each user has a binary state, i.e., ‘active’/‘ON’
and ‘inactive’/‘OFF’. In this case, there exist four combinations of signals from the perspective of each
RIS: (1) ‘Idle’ (indicating that both U1and U2are inactive), (2) ‘Only U1’ (indicating that only U1is
active), (3) ‘Only U2’ (indicating that only U2is active), and (4) ‘U1+U2’ (indicating that both U1and
U2are active). Based on the superimposed incident signal(s), the RIS needs to identify the composition
of the signal(s), i.e., to identify the correct class out of the four possible classes of signals. Therefore,
this signal identification boils down to a four-class classification problem, as illustrated in Table I.
TABLE I: An illustrative ISL example with two users
Inferred Class Description
Class-1: Idle The collected RF traces include only the noise
Class-2: Only U1The collected RF traces include only U1
Class-3: Only U2The collected RF traces include only U2
Class-4: U1+U2The collected RF traces include both U1and U2
B. Distributed RIS Binary Control
By feeding the inferred set of interfering users into the distributed RIS binary control algorithm, the
corresponding phase shifts and the binary ON-OFF status of the RISs can be obtained, as illustrated in
Fig. 2.
1) Optimal Phase Shifts Calculation: To calculate the phase shifts at the RIS, we denote the obtained
CSI associated to the kth RIS as Ck={hl,k,hm,k,gk, hd,l , hd,m}. Based on the inferred interfering users
(including e
Iland eωl) obtained via the trained CNN, the received SINR of the signal sent from Ulis
given by
plhd,l +PK
k=1 βkgkΦkhl,k
m=1 pmhd,m +PK
k=1 ξk
Based on (6), we first obtain the phase shifts of each RIS under the assumption βk= 1,k∈ K, and
then optimize the optimum binary ON-OFF vector Bbased on the obtained phase shifts. The first step,
in particular, can be formulated as
P2: max
s.t. βk= 1,k∈ K,(7b)
n= 1,n[1, Nk],k∈ K.(7c)
We observe that P2is a non-convex problem, which can be tackled by using several methods, such as the
semidefinite relaxation (SDR) method [3] and the successive convex approximation (SCA) method [15].
The optimal solution for the kth RIS is denoted by Θ
k, and the corresponding reflection-coefficient
matrix is Φ
k. With the obtained reflection-coefficient matrix, each RIS ON-OFF status is optimized via
the following RIS binary control algorithm.
2) Distributed RIS Binary Control Algorithm: Assuming that only the kth RIS is ON, i.e., βk= 1,
the received SINR of the signal sent from Ulto the BS via the kth RIS is
l=pl|hd,l +gkΦkhl,k|2
m=1 pmhd,m +ξk
If, on the other hand, all the KRISs are OFF, i.e., βk= 0 for k∈ K, the received SINR of the signal
sent from Ulto the BS via the direct link is
m=1 pm|hd,m|2+σ2.(9)
Based on (8) and (9), the kth RIS decides whether to be ON or OFF as detailed in Remark 1.
Remark 1. The kth RIS should be ON if eγk
lholds. This indicates, in fact, that the desired signal
from Ulcan be enhanced via the kth RIS. Otherwise, the kth RIS should be OFF to avoid the degradation
of the desired signal. In this case, the incident angle between the signals of the desired user and the
interfering signals at the RIS is in general small.
The approach for solving P2is summarized in Algorithm 1, which is executed at each RIS controller
when the incident signal is received. Specifically, upon receiving the incident signal, each RIS controller
identifies the set of interfering devices by extracting the I/Q samples from a copy of the incident signal and
feeding them into the trained CNN. Based on the classification outcome of the CNN, the RIS controller
can set the RIS ON-OFF state in a distributed manner.
3) Computational Complexity: The total computational complexity includes the online inference via
the CNN and the iterative algorithm to solve the phase shift optimization problem P2and the RIS ON-OFF
optimization problem.
Complexity for CNN online inference: The CNN is trained offline in a supervised fashion, therefore
the complexity of training can be ignored. The trained CNN model has a quadratic time complexity
during the inference process, i.e., O(M2CK), where Cdenotes the number of layers, Mdenotes
the number of neurons, and Kdenotes the total number of RISs.
Complexity for solving the phase shift optimization problem P2: To solve the problem P2, the
complexity lies in computing the optimal phase shift at each iteration of the optimization method,
Algorithm 1: Distributed RIS Binary Control
Input: e
Il,eωl, and Ck;
Output: B;
1: Initialize k= 0, all the RISs are ON;
2: while k < K do
3: kk+ 1;
4: Calculate Θ
kand Φ
kby solving problem P2;
5: Calculate eγk
land eγD
lvia (8) and (9), respectively;
6: if eγk
7: Keep the kth RIS ON, i.e., βk= 1;
8: else if eγk
9: Turn the kth RIS OFF, i.e., βk= 0;
10: end if
11: Add the kth RIS binary decision βkto B.
12: end while
e.g., the SCA method [15] whose complexity is O(Qz), where Q=PK
k=1 Nkdenotes the total
number of elements of all the RISs, and zis the total number of the iterations required.
Complexity for solving the RIS ON-OFF optimization problem: Since eγk
land eγD
lneed to be
calculated via (8) and (9), respectively, the computational complexity of solving the RIS ON-OFF
optimization problem required at each RIS controller is O(K).
As a result, the total complexity is O(M2CK +Qz +K), which grows linearly with the total number
of RISs.
In this section, we first evaluate the inference accuracy of the trained CNN model and the computational
complexity of the proposed ISL-based DRBC algorithm. Then we validate the benefits of deploying the
ISL-based DRBC algorithm.
A. CNN Testing Results
We trained the CNN with the 80% of collected RF data set which contains about 800 million I and Q
samples (training set), validated it by using 10% of the dataset (validation set), and tested it by using 10%
of the dataset (testing set) each corresponding to about 100 million of the I and Q samples. The trained
CNN model consists of two convolutional (Conv) layers with ReLU activation functions, followed by
TABLE II: Inference accuracy of the trained CNN model.
Scenarios w= 32 w= 128 w= 512
Idle 100.00%100.00%100.00%
Only U198.35%98.04%96.21%
Only U296.09%96.12%95.64%
two dense fully connected (FC) layers. In particular, the trained CNN model contain 256 filters (1×3) in
the first Conv layer, 128 filters (1×3) in the second Conv layer, 256 neurons in the first FC layer, and 9
neurons in the second FC layer (output).
The classification accuracy of the trained CNN is analyzed in Table II, by considering a two-user
scenario. The window size (i.e., the number of time steps of the collected RF data) is 32,128, and 512,
respectively. We observe from Table II that the online inference accuracy is, in general, greater than 95%
in the considered scenario. Compared to other classes, the ‘Idle’ class has the main characteristic that
no user transmits and only background noise exists. Due to the distinguishable pattern compared to the
other three classes, the CNN model predicts the ‘Idle’ class perfectly.
B. Computation Time
The proposed ISL-based DRBC algorithm allows us to obtain the optimal ON-OFF status of each RIS.
As detailed in previous text, the optimal ON-OFF status of the RISs can be formulated as the solution
of a non-convex MINLP, which is usually challenging to solve [15]. In this section, we compare the
proposed ISL-based DRBC algorithm against the spatial branch and bound (sBB) method, which is often
employed to solve non-convex MINLP [16], in terms of computation time.
The comparison of the average computation time (defined as t=Total time consumption
Total number of computations ) between
the proposed ISL-based DRBC algorithm and the traditional sBB method is conducted on the same
hardware platform that consists of an Intel Xeon(R) CPU E5-2650@2.0 GHz x 16. The obtained results
are illustrated in Table III. Compared to the traditional sBB method, the ISL-based DRBC algorithm
results in much lower computation time while still yielding the optimal ON-OFF status for each RIS.
The computation time of the proposed algorithm is less than one-thousandth of the computation time of
the sBB method when the number of RISs varies from 2to 5.
C. Performance Evaluation
1) Simulation Setting: The simulation model consists of KRISs, one desired user (U1), and one
interfering user (U2). Each RIS consists of 256 elements and all the KRISs are equally spaced by 5m
in vertical direction. The distances from BS and U1to the RIS center are 80 m and 60 m, respectively.
TABLE III: Computation time (ms) of the proposed ISL-based DRBC algorithm and the traditional sBB method
tTraditional sBB method Proposed DRBC algorithm
214.1 2.60 ×103
314.2 2.55 ×103
414.5 2.40 ×103
515.2 5.15 ×103
The incident angle between the BS and U1at the RIS is 150o, and the incident angle between U2and
U1is λ[0,150o]. We assume that ξk
m,l is linearly inversely proportional to λ. The channel parameters
are selected according to the 3GPP Urban Micro standard [17], which describes the path loss for both
line-of-sight and non-line-of-sight components [18]. The transmission power of U1is 20 dBm, the noise
power σ2is -94 dBm, the carrier frequency is 3GHz, and the reflection amplitude is equal to one.
2) Simulation Results: We evaluate the performance of the proposed ISL-aided algorithm by comparing
it with two benchmarks: ‘RIS always ON’ and ‘RIS always OFF’. Figs. 3(a)-(b) depict the achievable
SINR versus the angle of incidence (λ) for K= 1 and pm= 10,15,20 dBm. We observe that the SINR
first gradually decreases as λincreases due to the reduction of the distance between U2and BS, and
then increases due to the perfect interference elimination at the RIS. When λis small in particular, the
RIS is prone to be OFF since the interference reflected via the RIS is more pronounced. If λis large, on
the other hand, the impact of the interference is reduced and it is more probable that the RIS is ON. In
general terms, however, the impact of λon the system performance is still an open issue, whose analysis
is postponed to a future research work.
In Figs. 4(a)-(c), the SINR versus Kis illustrated, where pm= 10 dBm, the distance between U2and
the RIS is 5m. We observe that the RISs are always OFF if λ= 0, since the impact of the interference
is too high. If λis very large, e.g., λ= 150oin Fig. 4(b), the impact of the interference is low and
the RISs are always ON. When λis randomly selected, e.g., λ[30o,120o]in Fig. 4(c), the SINR
obtained by the ISL algorithm increases with Kand outperforms the two benchmarks, by about 100%
with respect to ‘RIS always OFF’ and by nearly 300% with respect to ‘RIS always ON’ when K= 5.
From Figs 4(a)-(c), we conclude that the performance of the proposed ISL algorithm largely depends on
λ, which impacts the interference cancellation at the RISs.
0 30 60 90 120 150
1.5 RIS always ON
RIS always OFF
Proposed RIS control
Pm=10, 15, 20 dBm
0 30 60 90 120 150
RIS always ON
RIS always OFF
Proposed RIS control
Pm=10, 15, 20 dBm
Fig. 3: Achievable SINR vs. λfor K= 1. The distance between the interfering user (U2) and the RIS is 10 m in (a), and 5m
in (b), respectively.
RIS always ON
RIS always OFF
Proposed RIS control
RIS always ON
RIS always OFF
Proposed RIS control
2RIS always ON
RIS always OFF
Proposed RIS control
Fig. 4: SINR vs. Kfor pm= 10 dBm. λ= 0 is shown in (a), λ= 150ois shown in (b), and λrandomly selected in [30o,120o]
is shown in (c).
In this paper, we introduced an ISL algorithm that uses appropriately trained CNNs for the interference
management in RIS-aided multi-user uplink networks. With the aid of the ISL algorithm, the RISs
are capable of inferring the interfering signals directly from the incident signals. A distributed control
algorithm was proposed to maximize the received SINR by dynamically configuring the binary status
of the RIS elements. Simulation results validated the performance improvement offered by the proposed
ISL-aided RIS approach. Offline training is only a candidate way to train a CNN, which may need to
be retrained when RF data distribution changes significantly. This issue may be avoided by using online
training methods, such as federated learning, which is a promising method for application in dynamical
wireless environments.
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... However, the performance of conventional ISAC is still constrained by unfavorable wireless environments, especially in the absence of LoS links. Recently, RIS has been incorporated into ISAC networks to improve the system performance via efficiently reconfiguring the communication environment [68][69][70][71][72][73][74][75]. ...
... The incident signals towards an RIS are usually mixed with interfering signals, leading to a degraded received signal-to-interference-plus-noise ratio (SINR) at the desired receiver. To address this issue, a trained convolutional neural network (CNN) has been exploited at the RIS controller to infer the interfering signals from the incident signals [73]. By utilizing federated spectrum learning, the wireless bandwidth allocation, user-RIS association, and phase shift configuration have been jointly optimized in [74]. ...
Reconfigurable intelligent surface (RIS) has been recognized as an essential enabling technique for the sixth-generation (6G) mobile communication network. Specifically, an RIS is comprised of a large number of small and low-cost reflecting elements whose parameters are dynamically adjustable with a programmable controller. Each of these elements can effectively reflect a phase-shifted version of the incident electromagnetic wave. By adjusting the wave phases in real time, the propagation environment of the reflected signals can be dynamically reconfigured to enhance communication reliability, boost transmission rate, expand cellular coverage, and strengthen communication security. In this paper, we provide an overview on RIS-assisted wireless communications. Specifically, we elaborate on the state-of-the-art enabling techniques of RISs as well as their corresponding substantial benefits from the perspectives of RIS reflection and RIS modulation. With these benefits, we envision the integration of RIS into emerging applications for 6G. In addition, communication security is of unprecedented importance in the 6G network with ubiquitous wireless services in multifarious verticals and areas. We highlight potential contributions of RIS to physical-layer security in terms of secrecy rate and secrecy outage probability, exemplified by a typical case study from both theoretical and numerical aspects. Finally, we discuss challenges and opportunities on the deployment of RISs in practice to motivate future research.
... In the conventional RIS-empowered wireless communication systems, the interfering signals tend to dynamically fluctuate and a conventional RIS 'blindly' reflects both the desired and interfering signals. In this context, due to the unpredictable nature of interfering signals, undesired reflections via RISs are becoming a critical challenge, which is known to severely degrade the desired signal at the receiver [1]. In contrast, if the conventional RISs were to be empowered with computational capabilities to perform active sensing for interference estimation, such technical challenges could be mitigated. ...
The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications. To realize the concept of the smart radio environment, reconfigurable intelligent surfaces (RISs) are a promising technology for offering programmable propagation of impinging electromagnetic signals via external control. However, the purely reflective nature of conventional RISs induces significant challenges in supporting computation-based applications, e.g., wave-based calculation and signal processing. To fulfil future communication and computing requirements, new materials are needed to complement the existing technologies of metasurfaces, enabling further diversification of electronics and their applications. In this event, we introduce the concept of reconfigurable intelligent computational surface (RICS), which is composed of two reconfigurable multifunctional layers: the `reconfigurable beamforming layer' which is responsible for tunable signal reflection, absorption, and refraction, and the `intelligence computation layer' that concentrates on metamaterials-based computing. By exploring the recent trends on computational metamaterials, RICSs have the potential to make joint communication and computation a reality. We further demonstrate two typical applications of RICSs for performing wireless spectrum sensing and secrecy signal processing. Future research challenges arising from the design and operation of RICSs are finally highlighted.
... The dataset is used for training to create an SL-based model for an optimal IRS interaction, as shown in Figure 8. In [76], authors proposed a SL convolutional neural network (CNN) model in the MISO system for obtaining the sum-rate maximization in the IRS wireless communication system. The CNN model is deployed in the IRS, where the input is an incident RF signal and the output is the set of interfering users. ...
Full-text available
An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication.
... In order to address this issue, the authors of [2] proposed an alternating optimization (AO) method to minimize the transmit power of the RIS-assisted multiuser multiple-input single-output (MISO) systems. By exploiting a convolutional neural network (CNN), the authors of [5] inferred the interference directly from the incident signals, thus improving the signal-to-interference-plus-noise ratio (SINR) H J. An and C. Yuen are with Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore (E-mail: jiancheng, ...
Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology enabling next-generation wireless networks. In this paper, we develop an improved index modulation (IM) scheme by utilizing RIS to convey information. Specifically, we study an RIS-aided multiple-input single-output (MISO) system, in which the information bits are conveyed by reflection patterns of RIS rather than the conventional amplitude-phase constellation. Furthermore, the K-means algorithm is employed to optimize the reflection constellation to improve the error performance. Also, we propose a generalized Gray coding method for mapping information bits to an appropriate reflection constellation and analytically evaluate the error performance of the proposed scheme by deriving a closed-form expression of the average bit error rate (BER). Finally, numerical results verify the accuracy of our theoretical analysis as well as the substantially improved BER performance of the proposed RIS-based IM scheme.
Full-text available
Intelligent metasurfaces have gained significant importance in recent years due to their ability to dynamically manipulate electromagnetic (EM) waves. Their multifunctional characteristics, realized by incorporating active elements into the metasurface designs, have huge potential in numerous novel devices and exciting applications. In this article, recent progress in the field of intelligent metasurfaces are reviewed, focusing particularly on tuning mechanisms, hardware designs, and applications. Reconfigurable and programmable metasurfaces, classified as space gradient, time modulated, and space–time modulated metasurfaces, are discussed. Then, reconfigurable intelligent surfaces (RISs) that can alter their wireless environments, and are considered as a promising technology for sixth-generation communication networks, are explored. Next, the recent progress made in simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) that can achieve full-space EM wave control are summarized. Finally, the perspective on the challenges and future directions of intelligent metasurfaces are presented.
The emerging technology of reconfigurable intelligent surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces with limited, or even the absence of, computing hardware. In this article, we consider multiuser and multi-RIS-empowered wireless systems and present a thorough survey of the online machine learning approaches for the orchestration of their various tunable components. Focusing on the sum-rate maximization as a representative design objective, we present a comprehensive problem formulation based on deep reinforcement learning (DRL). We detail the correspondences among the parameters of the wireless system and the DRL terminology, and devise generic algorithmic steps for the artificial neural network training and deployment while discussing their implementation details. Further practical considerations for multi-RIS-empowered wireless communications in the sixth-generation (6G) era are presented along with some key open research challenges. Different from the DRL-based status quo, we leverage the independence between the configuration of the system design parameters and the future states of the wireless environment, and present efficient multiarmed bandits approaches, whose resulting sum-rate performances are numerically shown to outperform random configurations, while being sufficiently close to the conventional deep $Q$ network (DQN) algorithm, but with lower implementation complexity.
Conference Paper
Full-text available
Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLF-PHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the the IRS-assisted system rate and transmission protection level compared with existing solutions.
Full-text available
Future wireless networks are expected to evolve toward an intelligent and software reconfigurable paradigm enabling ubiquitous communications between humans and mobile devices. They will also be capable of sensing, controlling, and optimizing the wireless environment to fulfill the visions of low-power, high-throughput, massively-connected, and low-latency communications. A key conceptual enabler that is recently gaining increasing popularity is the HMIMOS that refers to a low-cost transformative wireless planar structure comprised of sub-wavelength metallic or dielectric scattering particles, which is capable of shaping electromagnetic waves according to desired objectives. In this article, we provide an overview of HMIMOS communications including the available hardware architectures for reconfiguring such surfaces, and highlight the opportunities and key challenges in designing HMIMOS-enabled wireless communications.
Full-text available
Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communications link used for off-loading computational tasks is hostile. Fortunately, the propagation-induced impairments may be mitigated by intelligent reflecting surfaces (IRS), which are capable of enhancing both the spectral-and energy-efficiency. Specifically, an IRS comprises an IRS controller and a large number of passive reflecting elements, each of which may impose a phase shift on the incident signal, thus collaboratively improving the propagation environment. In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS. Pertinent latency-minimization problems are formulated for both single-device and multi-device scenarios, subject to practical constraints imposed on both the edge computing capability and the IRS phase shift design. To solve this problem, the block coordinate descent (BCD) technique is invoked to decouple the original problem into two subproblems, and then the computing and communications settings are alternatively optimized using low-complexity iterative algorithms. It is demonstrated that our IRS-aided MEC system is capable of significantly outperforming the conventional MEC system operating without IRSs. Quantitatively, about 20 % computational latency reduction is achieved over the conventional MEC system in a single cell of a 300 m radius and 5 active devices, relying on a 5-antenna access point.
This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the singleuser case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33% and 68% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.
Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to report the optimized phase shifts to the surface. This issue has not been addressed by previous works, which do not explicitly consider the overhead during the resource allocation phase. This work aims at filling this gap, by developing an overhead-aware resource allocation framework for wireless networks where reconfigurable intelligent surfaces are used to improve the communication performance. An overhead model is proposed and incorporated in the expressions of the system rate and energy efficiency, which are then optimized with respect to the phase shifts of the reconfigurable intelligent surface, the transmit and receive filters, the power and bandwidth used for the communication and feedback phases. The bi-objective maximization of the rate and energy efficiency is investigated, too. The proposed framework characterizes the trade-off between optimized radio resource allocation policies and the related overhead in networks with reconfigurable intelligent surfaces.
Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology for application to wireless communications. RISs can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scattering elements have sizes and inter-distances much smaller than the wavelength. Compared with other transmission technologies, e.g., phased arrays, multi-antenna transmitters, and relays, RISs require the largest number of scattering elements, but each of them needs to be backed by the fewest and least costly components. Also, no power amplifiers are usually needed. For these reasons, RISs constitute a promising software-defined architecture that can be realized at reduced cost, size, weight, and power (C-SWaP design), and are regarded as an enabling technology for realizing the emerging concept of smart radio environments (SREs). In this paper, we (i) introduce the emerging research field of RIS-empowered SREs; (ii) overview the most suitable applications of RISs in wireless networks; (iii) present an electromagnetic-based communication-theoretic framework for analyzing and optimizing metamaterial-based RISs; (iv) provide a comprehensive overview of the current state of research; and (v) discuss the most important research issues to tackle. Owing to the interdisciplinary essence of RIS-empowered SREs, finally, we put forth the need of reconciling and reuniting C. E. Shannon’s mathematical theory of communication with G. Green’s and J. C. Maxwell’s mathematical theories of electromagnetism for appropriately modeling, analyzing, optimizing, and deploying future wireless networks empowered by RISs.
Recently, the reconfigurable intelligent surface (RIS), benefited from the breakthrough on the fabrication of programmable meta-material, has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology to achieve smart radio environments. Employed as reflecting arrays, RIS is able to assist MIMO transmissions without the need of radio frequency chains resulting in considerable reduction in power consumption. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL). We first develop a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action. Unlike the most reported works utilizing the alternating optimization techniques to alternatively obtain the transmit beamforming and phase shifts, the proposed DRL based algorithm obtains the joint design simultaneously as the output of the DRL neural network. Simulation results show that the proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks. It is also observed that, appropriate neural network parameter settings will improve significantly the performance and convergence rate of the proposed algorithm.
We investigate channel estimation for an intelligent reflecting surface (IRS) aided wireless communication system with a multi-antenna access point (AP), an multi-element IRS, and a set of multiple users, each with a single antenna. Our goal is to minimize mean square error (MSE) of estimating the channels of direct and cascaded links from the AP to each user via the IRS elements by jointly optimizing the training sequence of the AP and reflection pattern of the IRS. To solve this challenging problem, we first find the joint optimal structure and reflection pattern for the training sequence, and then, we convert the problem into a convex form to design the optimal training sequence efficiently. To reduce the computational complexity, we also propose a suboptimal training sequence in closed form. Numerical results show that the proposed scheme outperforms baseline schemes.
Reconfigurable intelligent surface~(RIS) has drawn a great attention worldwide as it can create favorable propagation conditions by controlling the phase shifts of the reflected signals at the surface to enhance the communication quality. However, the practical RIS only has limited phase shifts, which will lead to the performance degradation. In this letter, we evaluate the performance of an uplink RIS assisted communication system by giving an approximation of the achievable data rate, and investigate the effect of limited phase shifts on the data rate. In particular, we derive the required number of phase shifts under a data rate degradation constraint. Numerical results verify our analysis.