Semidefinite Relaxation for Large Scale MIMO Detection

Negrão, João Lucas; Mussi, Alex Miyamoto; Abrão, Taufik

doi:10.14209/sbrt.2016.211

Cited by 1 publication

(1 citation statement)

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“…The SDP detector is well-known in the literature and provides good performance results with an increased computational cost. The formulation in [17] is simple compared with other approaches [20]; it was tested for 16 × 16 MIMO system [17], for a 40 × 40 system [18], and for 128 × 128 in [19], however only considering a symmetric system with same number of antennas.…”

Section: Introductionmentioning

confidence: 99%

Linear, Quadratic, and Semidefinite Programming Massive MIMO Detectors: Reliability and Complexity

Fukuda

Abrão

2019

IEEE Access

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One of the downsides of the massive multiple-input-multiple-output (M-MIMO) system is its computational complexity. Considering that techniques and different algorithms proposed in the literature applied to conventional MIMO may not be well suited or readily applicable to M-MIMO systems, in this paper, the application of different formulations inside the convex optimization framework is investigated. This paper is divided into two parts. In the first part, linear programming, quadratic programming (QP), and semidefinite programming are explored in an M-MIMO environment with high-order modulation and under realistic channel conditions, i.e., considering spatial correlation, error in the channel estimation, as well as different system loading. The bit error rate is evaluated numerically through Monte Carlo simulations. In the second part, algorithms to solve the QP formulation are explored, and computational complexity in terms of floating-point operations (flops) is compared with linear detectors. Those algorithms have interesting aspects when applied to our specific problem (M-MIMO detection formulated as QP), such as the exploitation of the structure of the problem (simple constraints) and the improvement of the rate of convergence due to the well-conditioned Gram matrix (channel hardening). The number of iterations is higher when the number of users K becomes similar to the number of base station antennas M (i.e., K ≈ M) than the case K M ; the number of iterations increases slowly as the number of users K and base station antennas M increases while keeping a low system loading. The QP with projected algorithms presented better performance than minimum mean square error detector when K ≈ M and promising computational complexity for scenarios with increasing K and low system loading.

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