The abundant degrees of freedom provided by a large number of antennas at the base station are harnessed in MU-MIMO-OFDM downlink systems. We formulate the OFDM modulation, MU precoding, and peak-to-average power ratio (PAPR) constraints into a non-convex optimization problem for transmit power minimization. This problem is subject to predefined thresholds for PAPR of each antenna and multi- user interference (MUI). Instead of using existing relaxation- based convex optimization methods, we directly address this non-convex PAPR-aware precoding problem using the linearized alternating direction method of multipliers (LADMM). Simulation experiments confirm that the proposed LADMM method significantly reduces PAPR and minimizes symbol error rate (SER). Importantly, compared to existing methods, our LADMM method exhibits faster convergence speed and lower complexity
In this paper, we propose an efficient joint precoding design method to maximize the weighted sum-rate in Intelligent Reflecting Surface (IRS)-assisted cell-free networks by jointly optimizing the active beamforming of base stations and the passive beamforming of IRS. To address the high-dimensional non-convex optimization problem, we employ a fractional programming approach to decouple the non-convex problem into subproblems for alternating optimization between active and passive beamforming. The active beamforming subproblem is addressed using the Consensus Alternating Direction Method of Multipliers (CADMM) algorithm, while the passive beamforming subproblem is tackled using the Accelerated Projection Gradient (APG) method. Simulation results demonstrate that our proposed approach achieves significant improvements in weighted sum-rate under various performance metrics compared to Primal-Dual Subgradient (PDS).
As 5G communication networks are now putting into commercialization, technologies for 6G communications assisted by intelligent reflecting surface (IRS) are also being explored in order to obtain faster and more reliable data transmissions. This paper studies the weighted sum-rate (WSR) maximization problem of users in an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. Aiming at the above problem, we propose a low-complexity linear alternating direction multiplier method (LADMM) that can be operated in parallel to solve this problem. The numerical results show that only adjusting the parameters in the proposed algorithm can make the user's WSR have better performance.
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