SQUID: Sparse Recovery‐Based Efficient Detection for Signed Quadrature Spatial Modulation in Massive MIMO Systems

Efficient data‐detection techniques for massive multiple‐input multiple‐output (mMIMO) systems with low complexity remain a major research focus, aiming to achieve high performance without excessive computational cost. Therefore, this article introduces signed quadrature integrated detection (SQUID), a framework that integrates spatial modulation (SM) with a compressive sensing (CS)‐based approach. By relying on the sparse property of signed quadrature SM, CS algorithms enable the detection with lower computational cost than the optimum method, while maintaining strong performance. Numerical results demonstrate that SQUID achieves superior performance compared to traditional methods while further reducing computational complexity. The proposed method is thoroughly evaluated under both perfect and imperfect channel state information conditions, and in each case, the SQUID method delivers robust and accurate detection, confirming its suitability for realistic mMIMO deployments.