Dual Multiple Access Channel Research Articles

On the Maximum Achievable Sum-Rate of the RIS-Aided MIMO Broadcast Channel

Reconfigurable intelligent surfaces (RISs) represent a new technology that can shape the radio wave propagation and thus offer a great variety of possible performance and implementation gains. Motivated by this, we investigate the achievable sum-rate optimization in a broadcast channel (BC) in the presence of RISs. We solve this problem by exploiting the well-known duality between the Gaussian multiple-input multiple-output (MIMO) BC and the multiple-access channel (MAC), and we correspondingly derive three algorithms which optimize the users' covariance matrices and the RIS phase shifts in the dual MAC. The users' covariance matrices are optimized by a dual decomposition method with block coordinate maximization (BCM), or by a gradient-based method. The RIS phase shifts are either optimized sequentially by using a closed-form expression, or are computed in parallel by using a gradient-based method. We present a computational complexity analysis for the proposed algorithms. Simulation results show that the proposed algorithms tend to converge to the same achievable sum-rate overall, but may produce different sum-rate performance for some specific situations, due to the non-convexity of the considered problem. Also, the gradient-based optimization methods are generally more time efficient. In addition, we demonstrate that the proposed algorithms can provide a significant gain in the RIS-assisted BC assisted by multiple RISs and that the gain depends on the placement of the RISs.

QoS constrained power minimization in the MISO broadcast channel with imperfect CSI

In this paper we consider the design of linear precoders and receivers in a Multiple-Input Single-Output (MISO) Broadcast Channel (BC). We aim to minimize the transmit power while meeting a set of per-user Quality-of-Service (QoS) constraints expressed in terms of per-user average rate requirements. The Channel State Information (CSI) is assumed to be known perfectly at the receivers but only partially at the transmitter. To solve this problem we convert the QoS constraints into Minimum Mean Square Error (MMSE) constraints. We then leverage MSE duality between the BC and the Multiple Access Channel (MAC), as well as standard interference functions in the dual MAC, to perform power minimization by means of an Alternating Optimization (AO) algorithm. Problem feasibility is also studied to determine whether the QoS constraints can be met or not. Finally, we present an algorithm to balance the average rates and manage situations that may be unfeasible, or lead to an unacceptably high transmit power.