arXiv subjects

Seyyed MohammadMahdi Shahabi

Publications and source records attributed to Seyyed MohammadMahdi Shahabi.

3 recordsLinked to original sources

arXiv2021

AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning

In this paper, the problem of pilot contamination in a multi-cell massive multiple input multiple output (M-MIMO) system is addressed using deep reinforcement learning (DRL). To this end, a pilot assignment strategy is designed that adapts to the channel variations while maintaining a tolerable pilot contamination effect. Using the angle of arrival (AoA) information of the users, a cost function, portraying the reward, is presented, defining the pilot contamination effects in the system. Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity.

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arXiv2021

Low-Complexity Robust Beamforming Design for IRS-Aided MISO Systems with Imperfect Channels

In this paper, large-scale intelligent reflecting sur-face (IRS)-assisted multiple-input single-output (MISO) system is considered in the presence of channel uncertainty. To maximize the average sum rate of the system by jointly optimizing the active beamforming at the BS and the passive phase shifts at the IRS, while satisfying the power constraints, a novel robust beamforming design is proposed by using the penalty dual decomposition (PDD) algorithm. By applying the upper bound maximization/minimization (BSUM) method, in each iteration of the algorithm, the optimal solution for each variable can be obtained with closed-form expression. Simulation results show that the proposed scheme achieves high performance with very low computational complexity.

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arXiv2020

IRS-aided Large-Scale MIMO Systems with Passive Constant Envelope Precoding

In this paper, an intelligent reflecting surface (IRS)-aided large-scale MIMO system is investigated in which constant envelope precoding (CEP) is utilized at each base station (BS). It is both cost-effective and energy-efficient to implement CEP in a large-scale antenna array. We aim to optimize the discrete phase shifts at both the BS and the IRS to minimize the sum power of multi-user interference (MUI) in the system via our proposed three algorithms. For the sake of simplicity, a simple single-cell scenario is considered, where the optimization of the BS and IRS phase shifts is solved by a low-complexity trellis-based algorithm. Then, this algorithm is extended to a multi-cell scenario, where the precoding operation in each BS is performed individually. With the aid of stochastic optimization method, a low-overhead trellis-based solution is proposed which has better performance than the first one. Finally, we solve the optimization problem via the semi-definite relaxation (SDR) scheme, to serve as a performance benchmark for the proposed algorithms. Meanwhile, interference and complexity analysis is provided for the proposed algorithms. Numerical results demonstrate that while the performance of the trellis-based algorithms is negligibly lower than that of the continuous-phase SDR-based solution, the computational complexity and the implementation cost of the former is much lower than the latter, which is appealing for practical applications.

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