Wide beam is necessary for ensuring the main lobe direction in a mobile communication scenario. A small dynamic range ratio (DRR) of excitations is crucial for simple-array and energy-saving design. The power gain pattern synthesis (PGPS) problem is aimed to maximize the minimum power gain in the wide main lobe and solving the PGPS problem can form a wide beam. To control the DRR, an upper bound constraint of DRR is imposed on the PGPS problem. The new problem is concave and can't be solved effectively by the conventional convex optimization method. We convert the DRR constraint into a group of stricter inequalities and transform the concave constraints to be convex with some convex approximations. The general PGPS problem with DRR constraints can be solved by the successive convex approximation (SCA) technique. The weights obtained by the proposed method can converge to the stationary point and we present the convergence proofs. Simulation results show that the proposed algorithm has better performance on increasing the minimum power gain in the main lobe (PGML) and suppressing the sidelobe level (SLL). Meanwhile, the DRR of excitations can be controlled below a given upper bound. INDEX TERMS Adaptive beamforming, array pattern synthesis, successive convex approximation, power gain optimization, wide main lobe beam, dynamic range ratio
Due to the high power consumption and hardware cost of radio frequency (RF) chains, the conventional fully-digital beamforming will be impractical for large-scale antenna systems (LSAS). To address this issue, hybrid beamforming has been proposed to reduce the number of RF chains. However, the fullyconnected structure assumed in most hybrid beamforming schemes is still cost-intensive. Recently, the partially-connected structure employing notably fewer phase shifters has received considerable attention in both academia and industry. But the design of partially-connected hybrid beamforming has not been fully understood, especially in multiuser systems. In this paper, we directly address the challenging non-convex non-smooth partially-connected hybrid beamforming design problem with individual signal-to-interferenceplus-noise ratio (SINR) constraints and unit-modulus constraints in a multiuser massive multiple-input multiple-output (MIMO) system. An iterative alternating algorithm based on a penalty method is proposed to obtain a stationary point, which inevitably has relatively high computational complexity. Thus, two lowcomplexity algorithms are then proposed by utilizing matrix approximation. Numerical results demonstrate significant performance gains of the proposed algorithms over existing hybrid beamforming algorithms. Moreover, the proposed low-complexity algorithms can achieve near-optimal performance with dramatically reduced computational complexity. INDEX TERMS Massive MIMO, hybrid beamforming, SINR constraints, penalty method, penalty dual decomposition.
This paper considers designing hybrid beamformer (HBF) to enhance communication performance and reduce inter-cell interference for massive MIMO system. The goal is minimizing the transmission power under individual signal-to-interference-plus-noise ratio (SINR) and sidelobe level (SLL) constraints, which is a non-convex optimization problem. To tackle the SLL over continuous intervals, we convert it into a finite number of constraints. Then we give the optimal solution of the problem for different RF chain to user number ratio. Simulation results show proposed algorithm not only effectively reduce inter-cell interference but also exhibits high numerical stability in multiuser massive MIMO system.
This paper considers the scenario where the base station and legitimate user are blocked by obstacles and uses an intelligent reflecting surface (IRS) to assist communication. To improve physical layer security, we model the eavesdropper channel as the Rican channel and establish a mathematical model with the goal of minimizing eavesdropper's rate subject to eavesdropper's outage probability constraint and legitimate user's secrecy rate constraint. The resulting problem is very challenging due to the continuous angle range of the eavesdropper's outage probability constraint and the coupling constraints imposed by the IRS. We first use a Bernstein-Type inequality to transform the continuous constraints into discrete constraints and then propose an alternating algorithm to obtain a suboptimal solution. Numerical results show that the proposed algorithm can reduce the eavesdropper's communication rate in different cases, which verifies the effectiveness of the proposed algorithm.
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