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... Moreover, the authors analyzed to underlay and overlay modes of D2D communication. Considering imperfect hardware including both hardware impairment at the transceivers and phase noise at the RISs, the authors optimized the phase shift to maximize the achievable rate for both continuous phase shifts and discrete phase shifts in [16]. With goal of maximizing the overall spectrum efficiency and energy efficiency of the network, the resource reuse indicators, the transmit power and the RIS's passive beamforming were optimized in [17]. ...

... Fig. 2 shows the OP comparison of V ϑ between OA and UA. Several observations are obtained: (i) the theoretical analyses (i.e., the curves with continuous and dash-lines) in (16) and (28) agreeably match with the simulation results (i.e., markers), corroborating the accuracy of our analysis. (ii) For the different locations of paired D2D users and fixed P 0 at 5 dBm in Fig. 2(a), we notice that the OP of V ϑ linearly decreases as P S ϑ increases and reduces significantly when paired D2D users are located near the RIS and descend at 6 VOLUME 4, 2022 This article has been accepted for publication in IEEE Access. ...

This paper investigates performance of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled multiple two-way full-duplex device-to-device (D2D) communication systems over Rayleigh fading channels under optimal and uncertain phase shift alignments. We derive closed-form expressions for outage probability (OP), sum throughput, ergodic capacity (EC) and energy efficiency. To gain insights, we quantify and reveal some useful guidelines for the performance behavior of the OP and the EC, such as diversity order and ergodic slope from high transmit power configuration. In addition, some critical points also deduced for the sum throughput and the system energy efficiency. Moreover, the impacts of the transmit power configurations, RIS deployments, allocating target data rate transmission, and the number of user deployments on the system performance are examined. Finally, we present some extensive simulations using Monte-Carlo method to corroborate the accuracy of the theoretical analysis.

... Recently, the joint beamforming and phase shift design was studied in a RIS-aided physical layer security system in [11]. Besides the transceiver hardware impairment, the authors of [12] further considered the impact of the phase noise at the RIS and derived the closedform data rate expression, based on which the genetic algorithm was adopted to solve the phase shift optimization problem. In [13], the RIS-aided communication system for serving a mobile user was studied, and the authors proposed an interesting algorithm to predict the positions of the user under HWI. ...

... Therefore, the k-th user's instantaneous signal-to-interference-plus-noise ratio (SINR) is given by (12) and (13) on the next page. Based on (12) and (13), the instantaneous data rate of the k-th user can be expressed as ...

In this paper, we study a reconfigurable intelligent surface (RIS)-aided multiuser MISO system with imperfect hardware, where the transceiver design is based on the statistical channel state information (CSI). Considering the transceiver hardware impairments (HWI), we aim to maximize the minimum average user data rate, where the precoding matrices at the base station (BS) and the reflecting phase shifts at the RIS are jointly optimized. Since the problem is nonconvex and the objective function cannot be derived in closed form, we adopt the deep deterministic policy gradient (DDPG) algorithm to deal with this challenging optimization problem, where we generate a set of CSI vectors in an offline way, and then these data sets are used to train the neural networks. The simulation results demonstrate the rapid convergence speed of the adopted DDPG algorithm and also emphasize that it is crucial to consider the HWI when optimizing the transceiver.

... In [18], a resource allocation design for the IRS-aided joint processing coordinated multipoint (JP-CoMP) system with underlaying D2D network was investigated. The authors in [19] studied an IRS-aided D2D communication system over Rician fading channels with the consideration of practical hardware impairments at both the terminals and IRSs. However, the optimization of most of these works is based on Shannon capacity with assumptions of infinite blocklength and zero error probability. ...

Intelligent reflecting surface (IRS) and device-to-device (D2D) communication are two promising technologies for improving transmission reliability between transceivers in communication systems. In this paper, we consider the design of reliable communication between the access point (AP) and actuators for a downlink multiuser multiple-input single-output (MISO) system in the industrial IoT (IIoT) scenario. We propose a two-stage protocol combining IRS with D2D communication so that all actuators can successfully receive the message from AP within a given delay. The superiority of the protocol is that the communication reliability between AP and actuators is doubly augmented by the IRS-aided first-stage transmission and the second-stage D2D transmission. A joint optimization problem of active and passive beamforming is formulated, which aims to maximize the number of actuators with successful decoding. We study the joint beamforming problem for cases where the channel state information (CSI) is perfect and imperfect. For each case, we develop efficient algorithms that include convergence and complexity analysis. Simulation results demonstrate the necessity and role of IRS with a well-optimized reflection matrix, and the D2D network in promoting reliable communication. Moreover, the proposed protocol can enable reliable communication even in the presence of stringent latency requirements and CSI estimation errors.

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.

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.

In this work we seek to characterise the performance of spatial modulation
(SM) and spatial multiplexing (SMX) with an experimental test bed. Two National
Instruments (NI)-PXIe devices are used for the system testing, one for the
transmitter and one for the receiver. The digital signal processing that
formats the information data in preparation of transmission is described along
with the digital signal processing that recovers the information data. In
addition, the hardware limitations of the system are also analysed. The average
bit error ratio (ABER) of the system is validated through both theoretical
analysis and simulation results for SM and SMX under line of sight (LoS)
channel conditions.

Intelligent reflecting surface (IRS), consisting of low-cost passive elements, is a promising technology for improving the spectral and energy efficiency of the fifth-generation (5G) and beyond networks. It is also noteworthy that an IRS can shape the reflected signal propagation. Most works in IRS-assisted systems have ignored the impact of the inevitable residual hardware impairments (HWIs) at both the transceiver hardware and the IRS while any relevant works have addressed only simple scenarios, e.g., with single-antenna network nodes and/or without taking the randomness of phase noise at the IRS into account. In this work, we aim at filling up this gap by considering a general IRS-assisted multi-user (MU) multiple-input single-output (MISO) system with imperfect channel state information (CSI) and correlated Rayleigh fading. In parallel, we present a general computationally efficient methodology for IRS reflect beamforming (RB) optimization. Specifically, we introduce an advantageous channel estimation (CE) method for such systems accounting for the HWIs. Moreover, we derive the uplink achievable spectral efficiency (SE) with maximal-ratio combining (MRC) receiver, displaying three significant advantages being: 1) its closed-form expression, 2) its dependence only on large-scale statistics, and 3) its low training overhead. Notably, by exploiting the first two benefits, we achieve to perform optimization with respect to the that can take place only at every several coherence intervals, and thus, reduces significantly the computational cost compared to other methods which require frequent phase optimization. Among the insightful observations, we highlight that uncorrelated Rayleigh fading does not allow optimization of the SE, which makes the application of an IRS ineffective. Also, in the case that the phase drifts, describing the distortion of the phases in the RBM, are uniformly distributed, the presence of an IRS provides no advantage. The analytical results outperform previous works and are verified by Monte-Carlo (MC) simulations.

Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), are regarded as one of the most promising and revolutionizing techniques for enhancing
the spectrum and/or energy efficiency of wireless systems. These devices are capable of reconfiguring the wireless propagation environment by carefully tuning the phase shifts of a large number of low-cost passive reflecting elements. In this article, we aim for answering four fundmental questions: 1) Why do we need RISs? 2) What is an RIS? 3) What are RIS’s applications? 4) What are the relevant challenges and future research directions? In response, eight promising research directions are pointed out.

In this paper, we investigate a reconfigurable intelligent surface (RIS) aided multi-pair communication system, in which multi-pair users exchange information via an RIS. We derive an approximate expression for the achievable rate by assuming that statistical channel state information (CSI) is available. A genetic algorithm (GA) to solve the rate maximization problem is proposed as well. In particular, we consider implementations of RISs with continuous phase shifts (CPSs) and discrete phase shifts (DPSs). Simulation results verify the obtained results and show that the proposed GA method has almost the same performance as the globally optimal solution. In addition, numerical results show that three quantization bits can achieve a large portion of the achievable rate for the CPSs setup.

Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements. This paper proposes deploying an IRS to cover the dead zone of cellular multiuser full-duplex (FD) two-way communication links while suppressing user-side self-interference (SI) and co-channel interference (CI). Based on information exchanged by the base station (BS) and all users, this approach can potentially double the spectral efficiency. To ensure network fairness, we jointly optimize the precoding matrix of the BS and the reflection coefficients of the IRS to maximize the weighted minimum rate (WMR) of all users, subject to maximum transmit power and unitmodulus constraints. We reformulate this non-convex problem and decouple it into two subproblems. Then the optimization variables in the equivalent problem are alternately optimized by adopting the block coordinate descent (BCD) algorithm. In order to further reduce the computational complexity, we propose the minorization-maximization (MM) algorithm for optimizing the precoding matrix and the reflection coefficient vector by defining minorizing functions in the surrogate problems. Finally, simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to improve coverage in blind areas.

This paper investigates the problem of resource allocation for multiuser communication networks with a reconfigurable intelligent surface (RIS)-assisted wireless transmitter. In this network, the sum transmit power of the network is minimized by controlling the phase beamforming of the RIS and transmit power of the base station. This problem is posed as a joint optimization problem of transmit power and RIS control, whose goal is to minimize the sum transmit power under signal-to-interference-plus-noise ratio (SINR) constraints of the users. To solve this problem, a dual method is proposed, where the dual problem is obtained as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form, while the optimal transmit power is obtained by using the standard interference function. Simulation results show that the proposed scheme can reduce up to 94% and 27% sum transmit power compared to the maximum ratio transmission (MRT) beamforming and zero-forcing (ZF) beamforming techniques, respectively.

In this letter, we investigate cooperative device-to-device (D2D) communication in an uplink cellular network, where D2D users act as relays for cellular users. We derive the outage probability of a cellular user and the average achievable rate from a D2D transmitter to a D2D receiver in analytic form. We obtain optimal spectrum and power allocation to maximize the total average achievable rate under the outage probability constraint. The validity of the analysis is verified by computer simulations.

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.

We investigate transmission optimization for intelligent reflecting surface (IRS) assisted multi-antenna systems from the physical-layer security perspective. The design goal is to maximize the system secrecy rate subject to the source transmit power constraint and the unit modulus constraints imposed on phase shifts at the IRS. To solve this complicated non-convex problem, we develop an efficient alternating algorithm where the solutions to the transmit covariance of the source and the phase shift matrix of the IRS are achieved in closed form and semi-closed forms, respectively. The convergence of the proposed algorithm is guaranteed theoretically. Simulations results validate the performance advantage of the proposed optimized design.

This paper provides a general analytical framework for multiple-input multiple-output space shift keying systems considering hardware impairments, at the transmitter and receiver sides, and co-channel interference. Specifically, a closedform expression for the average bit error probability (ABEP) in the case of two transmit antennas and arbitrary number of receive antennas is derived. As well, a tight upper bound ABEP expression in the general case of arbitrary number of transmit and receive antennas is obtained. Besides, an asymptotic simple expression is found over Rayleigh fading channels. Analytical results, which are validated via simulation ones, explicitly demonstrate that non-zero bounds of the ABEP exist in the high power region, which is in contrast to the case of ideal hardware, where the ABEP asymptotically goes to zero.

This paper investigates the uplink achievable rates of massive multiple-input
multiple-output (MIMO) antenna systems in Ricean fading channels, using
maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming perfect
and imperfect channel state information (CSI). In contrast to previous relevant
works, the fast fading MIMO channel matrix is assumed to have an arbitrary-rank
deterministic component as well as a Rayleigh-distributed random component. We
derive tractable expressions for the achievable uplink rate in the
large-antenna limit, along with approximating results that hold for any finite
number of antennas. Based on these analytical results, we obtain the scaling
law that the users' transmit power should satisfy, while maintaining a
desirable quality of service. In particular, it is found that regardless of the
Ricean $K$-factor, in the case of perfect CSI, the approximations converge to
the same constant value as the exact results, as the number of base station
antennas, $M$, grows large, while the transmit power of each user can be scaled
down proportionally to $1/M$. If CSI is estimated with uncertainty, the same
result holds true but only when the Ricean $K$-factor is non-zero. Otherwise,
if the channel experiences Rayleigh fading, we can only cut the transmit power
of each user proportionally to $1/\sqrt M$. In addition, we show that with an
increasing Ricean $K$-factor, the uplink rates will converge to fixed values
for both MRC and ZF receivers.