Cost-Efficient GPIP Processing for Large-Scale Multi-User MIMO Systems

This paper investigates a joint user selection, power allocation, and beamforming strategy for maximizing a weighted sum-spectral efficiency in a single-cell multi-user multiple-input multiple-output (MU-MIMO) system. Finding such a joint strategy is challenging due to the non-convexity of the sum-spectral efficiency function for the optimization variables. The recent algorithm, referred to as generalized power iteration precoding (GPIP), allows finding a stationary point in polynomial time for this non-convex problem. GPIP, however, is not scalable to a large-scale MU-MIMO system because of the overwhelming computational complexity. This high complexity makes the hardware energy efficiency worse as the number of antennas and users increases. This paper presents a low-cost simplified GPIP algorithm by jointly taking into account the implementation-level efficiency and algorithm-level performance. The proposed algorithm is to jointly harness the ideas of i) multiply-accumulate (MAC) operation reduction, ii) dynamic range reduction, and iii) low-complexity matrix inversion with approximate computing. Experimental results reveal that the proposed simplified GPIP algorithm reduces the total cost for solving the GPIP algorithm up to 99% while attaining a similar sum-spectral efficiency compared to that of a naive implementation method for GPIP in large-scale MU-MIMO systems.