Low‐complexity linear massive MIMO detection based on the improved BFGS method

Abstract

Linear minimum mean square error (MMSE) detection achieves a good trade‐off between performance and complexity for massive multiple‐input multiple‐output (MIMO) systems. To avoid the high‐dimensional matrix inversion involved, MMSE detection can be transformed into an unconstrained optimization problem and then solved by efficient numerical algorithms in an iterative way. Three low‐complexity Broyden‐Fletcher‐Goldfarb‐Shanno (BFGS) quasi‐Newton methods are proposed to iteratively realize massive MIMO MMSE detection without matrix inversion. The complexity can be reduced from scriptOfalse(K3false)$\mathcal {O}(K^{3})$ to scriptOfalse(LK2false)$\mathcal {O}(LK^{2})$, where K and L denote the number of users and iterations, respectively. Leveraging the special properties of massive MIMO, the authors first explore a simplified BFGS method (named S‐BFGS) to alleviate the computational burden in the search direction. For lower complexity, BFGS method with the unit step size (named U‐BFGS) is presented subsequently. When the base station (BS)‐to‐user‐antenna ratio (BUAR) is large enough, the two proposed BFGS methods can be integrated (named U‐S‐BFGS) to further reduce complexity. In addition, an efficient initialization strategy is devised to accelerate convergence. Simulation results verify that the proposed detection scheme can achieve near‐MMSE performance with a small number of iterations L as low as 2 or 3.

Enhanced Signal Detection and Constellation Design for Massive SIMO Communications With 1-Bit ADCs

“…While the high SNR scenario, i.e., SNR = 20 dB, is provided in Figures 6(c) and (d). Through the abovementioned plots, the separate distributions of the estimated symbol components fit the normal distribution in the low and high SNR regions, which validate the assumption that s soft is normally distributed (26). In Figure 7 3 × 3 grid, the different rows refer to different SNRs, while the columns are associated to different transmit signal s k .…”

“…Numerous studies have been devoted to study merging the low-resolution and 1-bit ADCs with the communication systems [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]. The system performance analysis is considered in [7], [8], [9], and [10].…”

“…The mm-wave MIMO channel estimation is investigated in [21] and [22] for 1-bit and low-resolution ADCs, respectively, at the BS. For the data detection in systems with low-resolution ADCs, one side of the literature tackles the case of 1-bit ADCs [23], [24], [25], [26], and the other side has been developed for few-bit ADCs [9], [20], [27], [28].…”

A hardware-efficient massive MIMO detector using improved quasi-Newton method

“…To reduce the computational complexity of matrix inversion, quasi-newton family algorithms are introduced to MIMO detection problem [4,6,11,12,13,14], which treats Eq. ( 3) as to solve the minimization of the equation 𝑓 (𝑠) = 1 2 s ⊤ As− b ⊤ s by a iterative process in Eq.…”