Generalized Beamspace Modulation Using Multiplexing: A Breakthrough in mmWave MIMO

Abstract

Spatial multiplexing (SMX) multiple-input multiple-output (MIMO) over the best beamspace was considered as the best solution for millimeter wave (mmWave) communications regarding spectral efficiency (SE), referred as the best beamspace selection (BBS) solution. The equivalent MIMO water-filling (WF-MIMO) channel capacity was treated as an unsurpassed SE upper bound. Recently, researchers have proposed various schemes trying to approach the benchmark and the performance bound. But, are they the real limit of mmWave MIMO systems with reduced radio-frequency (RF) chains? In this paper, we challenge the benchmark and the corresponding bound by proposing a better transmission scheme that achieves higher SE, namely the Generalized Beamspace Modulation using Multiplexing (GBMM).Inspired by the concept of spatial modulation, besides the selected beamspace, the selection operation is used to carry information. We prove that GBMM is superior to BBS in terms of SE and can break through the well known "upper bound". That is, GBMM renews the upper bound of the SE. We investigate SE-oriented precoder activation probability optimization, fully-digital precoder design, optimal power allocation and hybrid precoder design for GBMM. A gradient ascent algorithm is developed to find the optimal solution, which is applicable in all signal-to-noise-ratio (SNR) regimes. The best solution is derived in the high SNR regime. Additionally, we investigate the hybrid receiver design and deduce the minimum number of receive RF chains configured to gain from GBMM in achievable SE. We propose a coding approach to realize the optimized precoder activation. An extension to mmWave broadband communications is also discussed. Comparisons with the benchmark (i.e., WF-MIMO channel capacity) are made under different system configurations to show the superiority of GBMM. Index TermsMillimeter wave MIMO, beamspace modulation, precoder, hybrid precoder and combiner, power allocation, spectral efficiency

On the Capacity of Reconfigurable Intelligent Surface Assisted MIMO Symbiotic Communications

“…Since the constant is independent of the three optimization variables, i.e., reflecting activation probability α α α, reflecting patterns Ψ Ψ Ψ, and the transmit covariance matrix Q, the optimization objective function can be both R L (α α α, Ψ Ψ Ψ, Q) given in ( 18) and R L (α α α, Ψ Ψ Ψ, Q) + N r (log 2 e − 1). The upper bound is tight in the high SNR regime, which has been proven in [65] and [66]. The tightness of both capacity bounds will also be numerically investigated and verified via simulations in the sequel.…”

“…It should be noted that the searching steps may be small if some entries of α α α and ω ω ω approach zero in the iteration process, while leads to local optima of α α α and ω ω ω. To avoid being trapped in local optima, we adopt a gradient modification scheme proposed in [65].…”

Joint Transmit Precoding and Reconfigurable Intelligent Surface Phase Adjustment: A Decomposition-Aided Channel Estimation Approach

“…The effective mutual information (MI) achieved by Gaussian signaling can be adopted as the optimization objective of our precoding design [38]- [41], which is expressed as…”

BER Performance of Spatial Modulation Systems Under a Non-Stationary Massive MIMO Channel Model

“…where λ i is the eigenvalues of R R and µ is the eigenvalue of ee H R T . Substituting ( 22) and ( 23) into (20), the final expression of the theoretical BER expression for the k-th user of a TDMA-MU-SM is…”

“…In [19], a practical implementation of SM using a hardware testbed was demonstrated. The concept of SM is deployed as the generalized beamspace modulation in [20], [21], for mmWave massive MIMO systems. In addition to conventional radio frequency wireless communications SM has also been used in optical wireless communication systems [22], [23] where light emitting diode (LED) devices rather than antennas are used to transmit data.…”