Beamforming With Multi cell Coordination

In this work, we study the optimum multi-cell beamforming and we propose an optimized multi-cell downlink beamforming solution in which our objective is to maximize capacity of a cellular network. We first formulate an optimization problem, maximizing the received signal power of every active user in a cell, subjected to limiting the overall interference observed by other users below a specified level. In addition, we also put constraint on maximum transmit power of the serving base station. Next, we need robust downlink design against the imperfect channel state information. So in order to compute robust beamforming vector we accommodate channel estimation error in our formulation. To model the uncertainity between the true and estimated channel coefficients, we consider channel imperfection as error between true and estimated channel coefficients and we assume error is bounded with in an ellipsoidal set. The resulting formulation is a non-convex optimization problem. Since it is very difficult to solve a non-convex optimization problem we tried to convert non convex problem in to convex optimization problem. So to get a tractable solution for resulting non convex optimization problem, we exploit linear matrix inequality based S-procedure. The final reformulation is solved by using semi definite relaxation. The efficacy of proposed solution in improving cellular capacity and efficient power transmission is shown by simulations

Robust Active and Passive Beamforming for RIS-Assisted Full-Duplex Systems under Imperfect CSI

The sixth-generation (6G) wireless technology recognizes the potential of reconfigurable intelligent surfaces (RIS) as an effective technique for intelligently manipulating channel paths through reflection to serve desired users. Full-duplex (FD) systems, enabling simultaneous transmission and reception from a base station (BS), offer the theoretical advantage of doubled spectrum efficiency. However, the presence of strong self-interference (SI) in FD systems significantly degrades performance, which can be mitigated by leveraging the capabilities of RIS. Moreover, accurately obtaining channel state information (CSI) from RIS poses a critical challenge. Our objective is to maximize downlink (DL) user data rates while ensuring quality-of-service (QoS) for uplink (UL) users under imperfect CSI from reflected channels. To address this, we introduce the robust active BS and passive RIS beamforming (RAPB) scheme for RIS-FD, accounting for both SI and imperfect CSI. RAPB incorporates distributionally robust design, conditional value-at-risk (CVaR), and penalty convex-concave programming (PCCP) techniques. Additionally, RAPB extends to active and passive beamforming (APB) with perfect channel estimation. Simulation results demonstrate the UL/DL rate improvements achieved considering various levels of imperfect CSI. The proposed RAPB/APB schemes validate their effectiveness across different RIS deployment and RIS/BS configurations. Benefited from robust beamforming, RAPB outperforms existing methods in terms of non-robustness, deployment without RIS, conventional successive convex approximation, and half-duplex systems