PreprintPDF Available
Preprints and early-stage research may not have been peer reviewed yet.

Abstract and Figures

Perfect channel state information (CSI) is challenging to obtain due to the limited signal processing capability at the intelligent reflection surface (IRS). In this paper, we study the robust beamforming design for the IRS-aided multiple-user multiple-input single-output (MU-MISO) systems under the assumption of imperfect CSI. We aim for minimizing the transmit power while ensuring the achievable rate of each user above a threshold for all possible channel error realizations. With unit-modulus constraint and rate constraints, this problem is non-convex and the CSI uncertainties additionally increase the difficulty of solving this problem. Using certain approximation and transformation techniques, we convert this problem into a squence of semidefinite programming (SDP) subproblems. Numerical results show that the proposed robust beamforming design can guarantee the required quality of service (QoS) for all the users. Index Terms-Intelligent reflecting surface (IRS), large intelligent surface (LIS), robust design, imperfect channel state information (CSI), semidefinite programming (SDP).
Content may be subject to copyright.
Robust Beamforming Design for Intelligent
Reflecting Surface Aided MISO Communication
Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Marco Di Renzo, Fellow, IEEE, and
Arumugam Nallanathan, Fellow, IEEE
Abstract—Perfect channel state information (CSI) is chal-
lenging to obtain due to the limited signal processing capability
at the intelligent reflection surface (IRS). This is the first work
to study the worst-case robust beamforming design for an
IRS-aided multiuser multiple-input single-output (MU-MISO)
system under the assumption of imperfect CSI. We aim for min-
imizing the transmit power while ensuring that the achievable
rate of each user meets the quality of service (QoS) requirement
for all possible channel error realizations. With unit-modulus
and rate constraints, this problem is non-convex. The imperfect
CSI further increases the difficulty of solving this problem.
By using approximation and transformation techniques, we
convert this problem into a squence of semidefinite program
(SDP) subproblems that can be efficiently solved. Numerical
results show that the proposed robust beamforming design can
guarantee the required QoS targets for all the users.
Index Terms—Intelligent reflecting surface (IRS), large in-
telligent surface (LIS), robust design, imperfect channel state
information (CSI), semidefinite program (SDP).
Intelligent reflecting surface (IRS) has recently been pro-
posed as a cost-effective and energy-efficient high data rate
communication technology due to the rapid development
of radio frequency (RF) micro-electro-mechanical systems
(MEMS) as well as the abundant applications of the pro-
grammable and reconfigurable metasurfaces [1]. It consists
of an passive array structure that is capable of adjusting the
phase of each passive element on the surface continuously or
discretely with low power consumption [2], [3]. The benefits
of IRS in enhancing the spectral and energy efficiency have
been demonstrated in various schemes (e.g., [4]–[9]) by the
joint design of active precoder at the base station (BS) and
passive reflection beamforming at the IRS.
However, all the existing contributions on IRS are based
on the assumption of perfect channel state information (CSI)
at the BS, which is too idealistic in IRS communications.
For the imperfect CSI, the authors in [10] studied the impact
of the channel error by adopting the performance analysis
G. Zhou, C. Pan, H. Ren and A. Nallanathan are with the School
of Electronic Engineering and Computer Science at Queen Mary Uni-
versity of London, London E1 4NS, U.K. (e-mail: g.zhou, c.pan,, K. Wang is with Department of Com-
puter and Information Sciences, Northumbria University, UK. (e-mail: M. Di Renzo is with Universit´
e Paris-
Saclay, CNRS, CentraleSup´
elec, Laboratoire des Signaux et Systemes, Gif-
sur-Yvette, France (e-mail:
technique in an uplink MISO system. There are three types
of channels in an IRS-aided system: the direct channel from
the BS to the user, the indirect channel from the BS to the
IRS and the reflection channel from the IRS to the user.
The first one can be obtained with high accuracy by using
conventional channel estimation methods. The accurate CSI
of the latter two, however, are challenging to obtain in
practice due to the fact that the reflective elements at the IRS
are passive and have limited signal processing capability.
Fortunately, the location of the IRS is fixed and is usually
installed in the building facades, ceilings, walls, etc. In
this case, the indirect channel can be accurately estimated
through calculating the angles of arrival and departure, which
vary slowly. In contrast, the reflection channel is more
challenging to acquire as the locations of users are changing
and their environmental conditions are varying.
Against the above background, this paper investigates the
robust active precoder and passive reflection beamforming
design for an IRS-aided downlink multiple-user multiple-
input single-output (MU-MISO) system based on the as-
sumption of imperfect reflection channel. An ellipsoid
model of the reflection channel uncertainties are adopted.
To the best of our knowledge, this is the first work to study
the worst-case robust beamforming design problem in IRS-
aided wireless systems. The contributions of this paper are
as follows: 1) We aim to minimize the transmit power of
the BS through the joint design of an active precoder at the
BS and a passive beamforming at the IRS while ensuring
that each user’s QoS target can be achieved for all possible
channel error realizations. This problem is non-convex and
difficult to solve due to the unit-modulus constraints and the
imperfect CSI. 2) To address this problem, we propose an
iterative algorithm based on approximation transformations
and a convex–concave procedure (CCP). Specifically, to han-
dle the non-convex rate expression and CSI uncertainties, we
first approximately linearize the rates by using the first-order
Taylor expansion, and then transform the resultant semi-
infinite constraints into linear matrix inequalities (LMIs).
The non-convex unit-modulus constraints of the reflection
beamforming are handled by the penalized CCP [11]. 3)
Numerical results confirm the effectiveness of the proposed
algorithms in guaranteeing the QoS targets of all users.
IRS controller
Wireless control link
ireless control link
Fig. 1: An IRS-aided multiuser communication system.
A. Signal Transmission Model
We consider an IRS-aided MISO broadcast (BC) com-
munication system shown in Fig. 1, in which there is a
BS equipped with Ntransmit antennas serving Ksingle-
antenna users. Denote by s= [s1,· · · , sK]TCK×1the
Gaussian data symbols, in which each element is an inde-
pendent random variable with zero mean and unit variance,
i.e., E[ssH] = I. Denote by F=[f1,· · · ,fK]CN×Kthe
corresponding precoding vectors for the users. Then, the
transmit signal at the BS is x=Fs, the transmit power
of which is E{Tr xxH}=||F||2
In the MISO BC system, we propose to employ an IRS
with the goal of enhancing the received signal strength of
the users by reflecting signals from the BS to the users. It
is assumed that the IRS has Mpassive reflection elements
e= [e1,· · · , eM]TCM×1, the modulus of each element
is |em|2= 1,1mM. Then, the reflection beamforming
at the IRS is modeled as a diagonal matrix E=ιdiag(e)
CM×Mwhere ι[0,1] indicates the reflection efficiency.
The channels from the BS to user k, from the BS to the IRS,
and from the IRS to user kare denoted by hd,k CN×1,
Hdr CM×N, and hr,k CM×1, respectively.
The BS is responsible for designing the reflection beam-
forming at the IRS and sending it to the IRS controller [4].
Let us define the set of all users as K={1,2, ..., K}, then
the received signal of the users is
yk= (hH
d,k +hH
r,kEHdr )Fs +nk,kK,(1)
where nkis the received noise at user k, which is an additive
white Gaussian noise (AWGN) with distribution CN(0, σ2
The achievable data rate (bit/s/Hz) at user kis given by
Rk(F,e) = log21 + hH
d,k +hH
r,kEHdr fk2k(2)
where βk=||(hH
d,k +hH
r,kEHdr )Fk||2
resent the interference-plus-noises (INs) term with Fk=
[f1,· · · ,fk1,fk+1,· · · ,fK].
In the IRS-aided communication system, there are three
types of channels: the direct channel from the BS to the
user, i.e., hd,k, the indirect channel from the BS to the IRS,
i.e., Hdr, and the reflection channel from the IRS to the
user, i.e., hr,k. As mentioned in the introduction section, the
reflection channel is much more challenging to obtain than
the other two channels. Hence, in this paper, we assume that
the third type of channel is imperfect. The reflection channel
{hr,k}kKcan be modeled as {hr,k =b
hr,k +4k}kK,
where {b
hr,k}kKdenote the contaminated channel vectors
and {4k}kKdenote the corresponding channel error
vectors. In this paper, we adopt the channel error bounded
model, i.e., {||4k||2εk}kK, where εkis the radius of
the uncertainty region known by the BS.
B. Problem Formulation
With imperfect CSI, we aim to minimize the total trans-
mit power via the joint design of the precoding matrix
Fand the reflection vector eunder the worst-case QoS
constraints, i.e., ensuring that the achievable rate of each
user is above a threshold for all possible channel error
realizations. Mathematically, the worst-case robust design
problem is formulated as
s.t. Rk(F,e)rk,∀ k4kk2εk,kK,(3b)
|em|2= 1,1mM. (3c)
Constraints (3b) are the minimum QoS targets for each
user, while constraints (3c) correspond to the unit-modulus
requirements of the reflection elements at the IRS.
Problem (3) is a non-convex problem and the main chal-
lenge lies in the non-convex QoS constraints (3b) over the
CSI uncertainty regions and the non-convex unit-modulus
constraints (3c). Since variables Fand eare coupled, we
propose an alternate optimization (AO) method to solve
Problem (3).
A. Problem Transformation
To start with, the non-convexity of constraints (3b) can
be addressed by firstly treating the INs β= [β1, ..., βK]Tas
auxiliary variables. Hence, constraints (3b) are rewritten as
d,k +hH
r,kEHdr fk2βk(2rk1),
∀ k4kk2εk,kK,(4a)
d,k +hH
r,kEHdr Fk
∀ k4kk2εk,kK.(4b)
We first handle the infinite inequalities in (4a), which are
non-convex. Specifically, the left hand side (LHS) of (4a) is
approximated as its lower bound, as shown below..
Lemma 1 Let f(n)
kand E(n)be the optimal solutions
obtained at iteration n, then the linear lower bound of
d,k +hH
r,kEHdr )fk|2in (4a) at (f(n)
k,E(n)) is
r,kXkhr,k +hH
khr,k +ck,(5)
drEH,(n)+E(n)Hdr f(n)
khd,k +E(n)Hdrf(n)
Proof :Let abe a complex scalar variable. By applying
Appendix B of [12], we have the inequality
for any fixed a(n). Then, (5) is obtained by replac-
ing aand a(n)with (hH
d,k +hH
r,kEHdr )fkand (hH
d,k +
r,kE(n)Hdr )f(n)
k, respectively. The proof is complete.
With hr,k =b
hr,k +4kand Lemma 1, the inequality (4a)
is reformulated as
r,kXk4hr,k + 2Re n(xH
r,kXk)4hr,k o+dk
βk(2rk1),∀ k4kk2εk,kK,(7)
where dk=b
hr,k +xH
hr,k +b
In order to tackle the CSI uncertainties, the S-Procedure
in [13] is used to transform (7) into equivalent LMIs as
r,kXk)dkβk(2rk1) $kε2
where $= [$1, ..., $K]T0are slack variables.
Now, we consider the uncertainties in {4k}kKof (4b).
To this end, we first adopt Schur’s complement [14] to
equivalently recast (4b) as
tkI0,∀ k4kk2εk,kK,(9)
where tk= ((hH
d,k +hH
r,kEHdr )Fk)H.
Then, by using Nemirovski lemma [15] and introducing
the slack variables ξ= [ξ1, ..., ξK]T0, (9) is rewritten as
tkI(K1) εk(EHdrFk)H
where b
tk= ((hH
d,k +b
r,kEHdr )Fk)H.
With (8) and (10), we obtain the following approximated
reformulation of Problem (3) as
It is difficult to optimize the variables Fand esi-
multaneously as they are coupled in the LMIs (8) and
(10). Therefore, the AO method is adopted to solve the
subproblems corresponding to different sets of variables
iteratively. Specifically, for a given reflection beamforming
e, the subproblem of Problem (11) corresponding to the
precoder Fis formulated as
F(n+1) = arg min
s.t.(8),(10),(11c), (12b)
where F(n+1) is the optimal solution obtained in the (n+1)-
th iteration. Problem (12) is a semidefinite program (SDP)
and can be solved by using the CVX tool.
On the other hand, for a given precoding matrix F,
the subproblem of Problem (11) corresponding to eis a
feasibility-check problem. According to the Problem (P4’) in
[16], the converged solution in the optimization of ecan be
improved by introducing slack variables a= [a1, ..., aK]T
which are interpreted as the “signal-to-interference-plus-
noise ratio (SINR) residual” of users. Please refer to [16]
about the theory of “SINR residual”. Thus, the feasibility-
check problem of eis formulated as follows
where the Modified-(8) constraints are LMIs obtained from
(8) by replacing βk(2rk1) with βk(2rk1) + akfor
However, the above problem cannot be solved directly
due to the non-convex constraint (3c). In addition, the
semidefinite relaxation (SDR) method used in [16] cannot
always guarantee a feasible solution due to the fact that the
QoS constraints may be violated when the SDR solution
is not rank one. To handle this issue, we apply the penalty
CCP [11] which is capable of finding a feasible solution that
meets the unit-modulus constraint and the QoS constraints.
In particular, the constraints |em|2= 1,1mMcan be
equivalently rewritten as 1≤ |em|21,1mM. The
non-convex parts of these constraints are again linearized
by |e[t]
m)≤ −1,1mMat fixed e[t]
Following the penalty CCP framework, we impose the use
of slack variables b= [b1, ..., b2M]Tover the equivalent
constraints of the unit-modulus constraints, which yields
|em|21 + bM+m,1mM(14d)
where λ[t]is the regularization factor to scale the impact of
the penalty term ||b||1, which controls the feasibility of the
constraints. At low λ, Problem (14) targets to maximize the
“SINR residual”, while Problem (14) seeks for a feasible
point rather than optimizing the “SINR residual” at high λ.
Problem (14) is an SDP and can be solved by using the
CVX tool. The algorithm for finding a feasible solution of e
is summarized in Algorithm 1. Some points are emphasized
as follows: a) The maximum value λmax is imposed to avoid
numerical problems, that is, a feasible solution may not be
found when the iteration converges under increasing large
values of λ[t];b) The stopping criteria ||b||1χguarantees
the unit-modulus constraints in the original Problem (13)
to be met for a sufficiently low χ;c) The stopping criteria
||e[t]e[t1]||1νcontrols the convergence of Algorithm 1;
d) As mentioned in [11], a feasible solution for Problem (14)
may not be feasible for Problem (13). Hence, the feasibility
of Problem (13) is guaranteed by imposing a maximum
number of iterations Tmax and, in case it is reached, we
restart the iteration based on a new initial point.
Algorithm 1 Penalty CCP optimization for reflection beam-
forming optimization
Initialize: Initialize e[0],γ[0] >1, and set t= 0.
1: repeat
2: if t<Tmax then
3: Update e[t+1] from Problem (14);
4: λ[t+1] = min{γλ[t], λmax };
5: t=t+ 1;
6: else
7: Initialize with a new random e[0], set γ[0] >1, and
t= 0.
8: end if
9: until ||b||1χand ||e[t]e[t1]||1ν.
10: Output e(n+1) =e[t].
B. Algorithm Description
Algorithm 2 summarizes the AO method for solving
Problem (11).
Algorithm 2 AO algorithm for Problem (11)
Initialize: Initialize e(0) and F(0), and set n= 0.
1: repeat
2: Update F(n+1) from Problem (12) with given e(n);
3: Update e(n+1) from Problem (13) with given F(n+1);
4: nn+ 1;
5: until The objective value ||F(n+1)||2
a) Convergence analysis: The convergence of Algo-
rithm 2 can be guaranteed. In particular, denoting the objec-
tive value of Problem (12) as F(F,e), it follows that
F(F(n),e(n))F(F(n),e(n+1))F(F(n+1) ,e(n+1)).
The above equality holds true because the objective value of
Problem (12) is independent of e, and also e(n+1) is feasible
for Problem (12) if it is a feasible solution for Problem
(13). The above inequality follows from the globally optimal
solution F(n+1) of Problem (12) for a given e(n+1). Hence,
the sequence {F(F(n),e(n))}is non-increasing and the
algorithm is guaranteed to converge.
b) Initial point: As for the method of initialization,
e(0) can be chosen as a full-1 vector for simplicity. Inspired
by [17], the initial point F(0) can be chosen as the optimal
solution to the following optimization problem
s.t. Rk(F,e)ϕkrk,kK(15b)
where ϕ= [ϕ1, ..., ϕK]Tis an auxiliary variable vector.
Problem (15) is guaranteed to be feasible since at least
{ϕk= 0,kK,F(n)=0}is a feasible solution.
Problem (15) can also be solved by reformulating it into an
alternative optimization problem that is similar to Problems
(12). Denote by {ϕ(opt)
k}kKthe solution of Problem (15),
then the corresponding optimal precoding matrix can be used
as the initial point for Algorithm 2 if ϕ(opt)
k= 1,kK.
In this section, numerical results are provided to evaluate
the performance of the proposed algorithm. We consider that
the BS is equipped with N= 6 transmit antennas serving
K= 4 users with the assistance of an IRS. The number of
the reflection elements is M= 16. We assume a rectangular
coordinate to discribe the system, i.e., the locations of the
BS and IRS are (0 m, 0 m) and (50 m, 10 m) respectively,
and users are distributed randomly on a circle centered at
(70 m, 0 m) with radius 5 m.
The large-scale path loss is PL = 3010αlog10 (d)dB,
where αis the path loss exponent and dis the link length
in meters. The path loss exponents for the BS-IRS link, BS-
user link, and the IRS-user link are equal to αBI = 2.2
[18], αBU = 4 and αIU = 2, respectively. The small-
scale fading of the channels [Hdr,{hd,k ,b
a Rician distribution with Ricean factor 5. The line-of-
sight (LoS) components are defined by the product of the
steering vectors of the transmitter and receiver and the non-
LoS components are drawn from a Rayleigh fading. The
CSI error bounds are defined as εk=δ||b
where δ[0,1) accounts for the relative amount of CSI
uncertainties. The power of the AWGN at all users is set to
100 dBm and the target rates of all users are the same,
i.e., r1=... =rK=r. The IRS and benchmark schemes
considered are the following: 1) “IRS, ι= 1(or 0.5)”. 2)
“Non-robust IRS”, in which the channel estimation error is
ignored when designing the beamformings. 3) “Non IRS”, in
which there is no IRS in the MU-MISO system. 4) “Relay”,
in which a full-duplex relay is located at the same place of
the IRS. The numbers of transmit and receive antennas at
the relay are both M.
Firstly, Fig. 2 shows the total transmit power and energy
efficiency versus the channel uncertainty level δwhen r= 4
bit/s/Hz. It is observed from Fig. 2(a) that the required
transmit power of the robust IRS beamforming is higher
than other schemes. This is the price to pay to have a
robust design and to employ passive reflection elements. In
0 0.01 0.02 0.03 0.04 0.05
Total transmit power (dBm)
(a) The transmit power
0 0.01 0.02 0.03 0.04 0.05
Energy efficiency (bit/J/Hz)
(b) The energy efficiency
Fig. 2: Performance versus the channel uncertainty level δ
under N= 6,M= 16,K= 4 and r= 4 bit/s/Hz.
0.01 0.02 0.03 0.04 0.05
Outage probability
Fig. 3: Outage probability of rate versus the channel uncer-
tainty level δunder N= 6,M= 16 and K= 4.
any case, it is less than the “Non IRS” case. The energy
efficiency (EE) reported in Fig. 2(b) is defined as the ratio
between the smallest achievable rate among the users and the
total power consumption. The total power consumption of
the IRS schemes is equal to ||F||2
F+NPactive +M Ppassive
and that of the relay scheme is equal to ||F||2
F+Prelay +
(N+ 2M)Pactive, where Pr elay is the relay transmit power.
We set the circuit power consumption of the active antennas
to Pactive = 10 mW and that of the passive antennas
as Pactive = 5 mW [19]. Fig. 2(b) illustrates the high
EE performance of the IRS-aided systems compared with
the relay system for the reason of the low circuit power
consumption of the passive elements in the IRS. In addition,
from Fig. 2(a) and Fig. 2(b) we come to the conclusion
that only when the reflection efficiency of the reflecting
metasurfaces is high (ιis nearly 1) and the estimation error
of the reflection channel is small (δis less than 0.03), the
IRS can show its advantages of enhancing the spectral and
energy efficiency.
Fig. 3 shows the outage probability of rate for the nonro-
bust design. Here, outage probability refers to the probability
that the target rate of at least one user is not satisfied. It
is observed that when the beamforming design ignores the
channel error, the target rate of at least one user is frequently
not met, especially at high value of ror δ. However, our
adopted worst-case robust design method can guatantee no
outage happens.
In this paper, we considered the robust beamforming
design for the IRS-aided MU-MISO system when the CSI
is imperfect. The CSI uncertainties were addressed by us-
ing approximation and transformation techniques, and the
non-convex unit-modulus constraints were solved under the
penalty CCP framework. Numerical results demonstrated the
robustness of our proposed algorithm.
[1] M. D. Renzo, M. Debbah, D.-T. Phan-Huy et al., “Smart radio
environments empowered by reconfigurable AI meta-surfaces: An idea
whose time has come,” J. Wireless Commun. Netw., 2019,129(2019).
[2] K. Ntontin, M. D. Renzo, J. Song et al., “Reconfigurable
intelligent surfaces vs. relaying: Differences, similarities,
and performance comparison,” 2019. [Online]. Available:
[3] E. Basar, M. Di Renzo, J. De Rosny, M. Debbah, M. Alouini,
and R. Zhang, “Wireless communications through reconfigurable
intelligent surfaces,” IEEE Access, vol. 7, pp. 116 753–116773, 2019.
[4] C. Pan, H. Ren, K. Wang, M. Elkashlan, A. Nallanathan, J. Wang, and
L. Hanzo, “Intelligent reflecting surface aided MIMO broadcasting
for simultaneous wireless information and power transfer,” 2019.
[Online]. Available:
[5] X. Yu, D. Xu, and R. Schober, “Enabling secure wireless communi-
cations via intelligent reflecting surfaces,” pp. 1–6, Dec 2019.
[6] G. Zhou, C. Pan, H. Ren, K. Wang, and A. Nallanathan,
“Intelligent Reflecting Surface Aided Multigroup Multicast
MISO Communication Systems,” 2019. [Online]. Available:
[7] S. Zhang and R. Zhang, “Capacity characterization for intelligent
reflecting surface aided MIMO communication,” 2019. [Online].
[8] T. Bai, C. Pan, Y. Deng, M. Elkashlan, and A. Nallanathan, “Latency
minimization for intelligent reflecting surface aided mobile edge
computing,” 2019. [Online]. Available:
[9] H. Han, J. Zhao, D. Niyato et al., “Intelligent reflecting surface
aided network: Power control for physical-layer broadcasting,” 2019.
[Online]. Available:
[10] M. Jung, W. Saad, Y. Jang, G. Kong, and S. Choi, “Performance
analysis of large intelligent surfaces (liss): Asymptotic data rate and
channel hardening effects,” IEEE Trans. Wireless Commun., pp. 1–1,
Jan. 2020.
[11] T. Lipp and S. Boyd, “Variations and extension of the convex-concave
procedure,” Optim. Eng., vol. 17, no. 2, pp. 263–287, 2016. [Online].
[12] C. Pan, H. Ren, M. Elkashlan, A. Nallanathan, and L. Hanzo,
“Robust Beamforming Design for Ultra-dense User-Centric C-RAN
in the Face of Realistic Pilot Contamination and Limited Feedback,”
2018. [Online]. Available:
[13] Z. Q. Luo, J. F. Sturm, and S. Zhang, “Multivariate nonnegative
quadratic mappings,” SIAM J. Optim., vol. 14, no. 4, pp. 1140–1162,
[14] S. Boyd and L. Vandenberghe, Convex optimization. Cambridge
Univ. Press, 2004.
[15] Y. Eldar, A. Ben-Tal, and A. Nemirovski, “Robust mean-squared error
estimation in the presence of model uncertainties,” IEEE Trans. Signal
Process., vol. 53, no. 1, pp. 168–181, Jan. 2005.
[16] Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless
network via joint active and passive beamforming,IEEE Trans.
Wireless Commun., vol. 18, no. 11, pp. 5394–5409, Nov. 2019.
[17] C. Pan, H. Zhu, N. J. Gomes, and J. Wang, “Joint user selection
and energy minimization for ultra-dense multi-channel C-RAN with
incomplete CSI,” IEEE J. Sel. Areas Commun., vol. 35, no. 8, pp.
1809–1824, Aug. 2017.
[18] W. Tang, M. Chen, X. Chen et al., “Wireless communications
with reconfigurable intelligent surface: Path loss modeling
and experimental measurement,” 2019. [Online]. Available:
[19] E. Bj¨
ornson, ¨
O. ¨
Ozdogan, and E. G. Larsson, “Intelligent reflecting
surface vs. decode-and-forward: How large surfaces are needed to
beat relaying?” IEEE Wireless Commun. Lett., pp. 1–1, 2019.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this paper, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.
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.
Full-text available
Intelligent reflecting surface (IRS) has recently been envisioned to offer unprecedented massive multiple-input multiple-output (MIMO)-like gains by deploying large-scale and low-cost passive reflection elements. By adjusting the reflection coefficients, the IRS can change the phase shifts on the impinging electromagnetic waves so that it can smartly reconfigure the signal propagation environment and enhance the power of the desired received signal or suppress the interference signal. In this paper, we consider downlink multigroup multicast communication systems assisted by an IRS. We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint. To tackle this non-convex problem, we propose two efficient algorithms under the majorization-minimization (MM) algorithm framework. Specifically, a concave lower bound surro-gate objective function of each user's rate has been derived firstly, based on which two sets of variables can be updated alternately by solving two corresponding second-order cone programming (SOCP) problems. Then, in order to reduce the computational complexity, we derive another concave lower bound function of each group's rate for each set of variables at every iteration, and obtain the closed-form solutions under these loose surrogate objective functions. Finally, the simulation results demonstrate the benefits in terms of the spectral and energy efficiency of the introduced IRS and the effectiveness in terms of the convergence and complexity of our proposed algorithms.
Full-text available
An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternatively optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.
Conference Paper
Full-text available
As a recently proposed idea for future wireless systems, intelligent reflecting surface (IRS) can assist communications between entities which do not have high-quality direct channels in between. Specifically, an IRS comprises many low-cost passive elements, each of which reflects the incident signal by incurring a phase change so that the reflected signals add coherently at the receiver. In this paper, for an IRS-aided wireless network, we study the problem of power control at the base station (BS) for physical-layer broadcasting under quality of service (QoS) constraints at mobile users, by jointly designing the transmit beamforming at the BS and the phase shifts of the IRS units. Furthermore, we derive a lower bound of the minimum transmit power at the BS to present the performance bound for optimization methods. Simulation results show that, the transmit power at the BS approaches the lower bound with the increase of the number of IRS units, and is much lower than that of the communication system without IRS.
Full-text available
The concept of a large intelligent surface (LIS) has recently emerged as a promising wireless communication paradigm that can exploit the entire surface of man-made structures for transmitting and receiving information. An LIS is expected to go beyond massive multiple-input multiple-output (MIMO) system, insofar as the desired channel can be modeled as a perfect line-of-sight. To understand the fundamental performance benefits, it is imperative to analyze its achievable data rate, under practical LIS environments and limitations. In this paper, an asymptotic analysis of the uplink data rate in an LIS-based large antenna-array system is presented. In particular, the asymptotic LIS rate is derived in a practical wireless environment where the estimated channel on LIS is subject to estimation errors, interference channels are spatially correlated Rician fading channels, and the LIS experiences hardware impairments. Moreover, the occurrence of the channel hardening effect is analyzed and the performance bound is asymptotically derived for the considered LIS system. The analytical asymptotic results are then shown to be in close agreement with the exact mutual information as the number of antennas and devices increase without bounds. Moreover, the derived ergodic rates show that hardware impairments, noise, and interference from estimation errors and the non-line-of-sight path become negligible as the number of antennas increases. Simulation results show that an LIS can achieve a performance that is comparable to conventional massive MIMO with improved reliability and a significantly reduced area for antenna deployment.
Full-text available
The rate and energy efficiency of wireless channels can be improved by deploying software-controlled metasurfaces to reflect signals from the source to destination, especially when the direct path is weak. While previous works mainly optimized the reflections, this letter compares the new technology with classic decode-and-forward (DF) relaying. The main observation is that very high rates and/or large metasurfaces are needed to outperform DF relaying, both in terms of minimizing the total transmit power and maximizing the energy efficiency, which also includes the dissipation in the transceiver hardware.
Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices are optimized for different subcarriers while only one common set of IRS reflection coefficients is designed to cater to all the subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.
In the intelligent reflecting surface (IRS)-enhanced wireless communication system, channel state information (CSI) is of paramount importance for achieving the passive beamforming gain of IRS, which, however, is a practically challenging task due to its massive number of passive elements without transmitting/receiving capabilities. In this letter, we propose a practical transmission protocol to execute channel estimation and reflection optimization successively for an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system. Under the unit-modulus constraint, a novel reflection pattern at the IRS is designed to aid the channel estimation at the access point (AP) based on the received pilot signals from the user, for which the channel estimation error is derived in closed-form. With the estimated CSI, the reflection coefficients are then optimized by a low-complexity algorithm based on the resolved strongest signal path in the time domain. Simulation results corroborate the effectiveness of the proposed channel estimation and reflection optimization methods.