Dynamic Metasurface Antennas for Energy Efficient Massive MIMO Uplink Communications

Future wireless communications are largely inclined to deploy a massive number of antennas at the base stations (BS) by exploiting energy-efficient and environmentally friendly technologies. An emerging technology called dynamic metasurface antennas (DMAs) is promising to realize such massive antenna arrays with reduced physical size, hardware cost, and power consumption. This paper aims to optimize the energy efficiency (EE) performance of DMAs-assisted massive MIMO uplink communications. We propose an algorithmic framework for designing the transmit precoding of each multi-antenna user and the DMAs tuning strategy at the BS to maximize the EE performance, considering the availability of the instantaneous and statistical channel state information (CSI), respectively. Specifically, the proposed framework includes Dinkelbach's transform, alternating optimization, and deterministic equivalent methods. In addition, we obtain a closed-form solution to the optimal transmit signal directions for the statistical CSI case, which simplifies the corresponding transmission design. The numerical results show good convergence performance of our proposed algorithms as well as considerable EE performance gains of the DMAs-assisted massive MIMO uplink communications over the baseline schemes

Joint beamforming design for secure RIS-assisted IoT networks

This paper studies secure communication in an internet-of-things (IoT) network, where the confidential signal is sent by an active refracting reconfigurable intelligent surface (RIS)-based transmitter, and a passive reflective RIS is utilized to improve the secrecy performance of users in the presence of multiple eavesdroppers. Specifically, we aim to maximize the weighted sum secrecy rate by jointly designing the power allocation, transmit beamforming (BF) of the refracting RIS, and the phase shifts of the reflective RIS. To solve the non-convex optimization problem, we propose a linearization method to approximate the objective function into a linear form. Then, an alternating optimization (AO) scheme is proposed to jointly optimize the power allocation factors, BF vector and phase shifts, where the first one is found using the Lagrange dual method, while the latter two are obtained by utilizing the penalty dual decomposition method. Moreover, considering the demands of green and secure communications, by applying the Dinkelbach’s method, we extend our proposed scheme to solving a secrecy energy maximization problem. Finally, simulation results demonstrate the effectiveness of the proposed design

Uplink Transceiver Design and Optimization for Transmissive RMS Multi-Antenna Systems

In this paper, a novel uplink communication for the transmissive reconfigurable metasurface (RMS) multi-antenna system is investigated. Specifically, a transmissive RMS-based receiver equipped with a single receiving antenna is first proposed, and a far-near field channel model is also given. Then, in order to maximize the system sum-rate, we formulate a joint optimization problem over subcarrier allocation, power allocation and RMS transmissive coefficient design. Since the coupling of optimization variables, the problem is non-convex, so it is challenging to solve it directly. In order to tackle this problem, the alternating optimization (AO) algorithm is used to decouple the optimization variables and divide the problem into two subproblems to solve. Numerical results verify that the proposed algorithm has good convergence performance and can improve system sum-rate compared with other benchmark algorithms.Comment: arXiv admin note: text overlap with arXiv:2109.0546

Beamforming Design for Multiuser Transmission Through Reconfigurable Intelligent Surface

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 BS. 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.Comment: RIS as transmitter for multiuser transmission, accepted in IEEE Transactions on Communication

Outage-Constrained Robust Beamforming for Intelligent Reflecting Surface Aided Wireless Communication

In intelligent reflecting surface (IRS) aided wireless communication systems, channel state information (CSI) is crucial to achieve its promising passive beamforming gains. However, CSI errors are inevitable in practice and generally correlated over the IRS reflecting elements due to the limited training with discrete phase shifts, which degrade the data transmission rate and reliability. In this paper, we focus on investigating the effect of CSI errors to the outage performance in an IRS-aided multiuser downlink communication system. Specifically, we aim to jointly optimize the active transmit precoding vectors at the access point (AP) and passive discrete phase shifts at the IRS to minimize the AP's transmit power, subject to the constraints on the maximum CSI-error induced outage probability for the users. First, we consider the single-user case and derive the user's outage probability in terms of the mean signal power (MSP) and variance of the received signal at the user. Since there is a trade-off in tuning these two parameters to minimize the outage probability, we propose to maximize their weighted sum with the optimal weight found by one-dimensional search. Then, for the general multiuser case, since the users' outage probabilities are difficult to obtain in closed-form due to the inter-user interference, we propose a novel constrained stochastic successive convex approximation (CSSCA) algorithm, which replaces the non-convex outage probability constraints with properly designed convex surrogate approximations. Simulation results verify the effectiveness of the proposed robust beamfoming algorithms and show their significant performance improvement over various benchmark schemes.Comment: 15 pages, 14 figures, accepted for publication in IEEE Transactions on Signal Processin

Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems

Channel estimation in the RIS-aided massive multiuser multiple-input single-output (MU-MISO) wireless communication systems is challenging due to the passive feature of RIS and the large number of reflecting elements that incur high channel estimation overhead. To address this issue, we propose a novel cascaded channel estimation strategy with low pilot overhead by exploiting the sparsity and the correlation of multiuser cascaded channels in millimeter-wave massive MISO systems. Based on the fact that the phsical positions of the BS, the RIS and users may not change in several or even tens of consecutive channel coherence blocks, we first estimate the full channel state information (CSI) including all the angle and gain information in the first coherence block, and then only re-estimate the channel gains in the remaining coherence blocks with much less pilot overhead. In the first coherence block, we propose a two-phase channel estimation method, in which the cascaded channel of one typical user is estimated in Phase I based on the linear correlation among cascaded paths, while the cascaded channels of other users are estimated in Phase II by utilizing the partial CSI of the common base station (BS)-RIS channel obtained in Phase I. The total theoretical minimum pilot overhead in the first coherence block is $8J-2+(K-1)\left\lceil (8J-2)/L\right\rceil$, where $K$, $L$ and $J$ denote the numbers of users, paths in the BS-RIS channel and paths in the RIS-user channel, respectively. In each of the remaining coherence blocks, the minimum pilot overhead is $JK$. Moreover, the training phase shift matrices at the RIS are optimized to improve the estimation performance.Comment: Intelligent reflecting surface (IRS), reconfigurable intelligent surface (RIS), Millimeter wave, massive MIMO, AoA/AoD estimation, channel estimatio