1 penalty approach for finding a high-quality solution of the considered problem. Specifically, we first propose a novel negative ℓ1 penalty model, which penalizes the one-bit constraint into the objective with a negative ℓ1-norm term, and show the equivalence between (global and local) solutions of the original problem and the penalty problem when the penalty parameter is sufficiently large. We further transform the penalty model into an equivalent min-max problem and propose an efficient alternating optimization (AO) algorithm for solving it. The AO algorithm enjoys low periteration complexity and is guaranteed to converge to the stationary point of the min-max problem. Numerical results show that, compared against the state-of-the-art CI-based algorithms, the proposed algorithm generally achieves better bit-error-rate (BER) performance with lower computational cost.">

A Novel Negative ℓ1 Penalty Approach for Multiuser One-Bit Massive MIMO Downlink with PSK Signaling (original) (raw)

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