A Real–Complex Hybrid Modulation Approach for Scaling Up Multiuser MIMO Detection

Abstract

In this paper, a novel approach, namely realcomplex hybrid modulation (RCHM), is proposed to scale up multiuser multiple-input multiple-output (MU-MIMO) detection with particular concern on the use of equal or approximately equal service antennas and user terminals. By RCHM, we mean that user terminals transmit their data sequences with a mix of real and complex modulation symbols interleaved in the spatial and temporal domain. It is shown, through the system outage probability, RCHM can combine the merits of real and complex modulations to achieve the best spatial diversity-multiplexing trade-off that minimizes the required transmit-power given a sum-rate. The signal pattern of RCHM is optimized with respect to the real-to-complex symbol ratio as well as power allocation. It is also shown that RCHM equips the successive interference canceling MU-MIMO receiver with near-optimal performances and fast convergence in Rayleigh fading channels. This result is validated through our mathematical analysis of the average biterror-rate as well as extensive computer simulations considering the case with single or multiple base-stations.

Quantum Annealing for Next-Generation MU-MIMO Detection: Evaluation and Challenges

“…The linear minimum mean square error (MMSE) estimate can offer improved performance over ZF, but can still suffer from a low error performance. Many other MIMO detection techniques have been proposed (e.g., [3], [4], [6], [7]), and their performance lies within a threeway tradeoff between complexity, reliability, and multiplexing gain [8].…”

“…However, this may not be possible with classical computation techniques. It was conjectured in [8] from empirical observations that MIMO detection methods are constrained to a three-way tradeoff between high reliability, low complexity, and high multiplexing gain (referred here as the RCM tradeoff ). This may not be surprising, as it was proven by Verdú that the underlying optimum multi-user detection problem is NP-hard [9].…”

A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection

“…Since h is the vector counterpart of H, the length of h is (N M ), and consequently the length of s is: K = N (M + 1). Note that the conversion from complex signals to their real-signal equivalent version doubles the size of corresponding vectors or matrices [32] (can be also found in Section IV). However, we do not use the doubled size for the sake of mathematical notation simplicity.…”

“…It is perhaps worth noting that communication signals are normally considered as complex-valued symbols, but most of the existing deep learning algorithms are based on real-valued operations. To facilitate the learning and communication procedure, it is usual practice to convert complex signals to their real signal equivalent version using (31) (see [5]- [7], [17], [32]). For instance, a (K) × (1) complex-valued input vector s is converted into a (2K) × (1) real-valued vector s real by concatenating its real and imaginary parts, which is given by…”

Constellation-Oriented Perturbation for Scalable-Complexity MIMO Nonlinear Precoding

“…On the other hand, advanced NLP techniques such as vector perturbation (VP) [2] and Tomlinson-Harashima precoding (THP) [3] have their computational complexities scaling exponentially with the size of MIMO networks. A wise way of mitigating the scalability problem is to adopt the massive-MIMO [4] or asymmetric MIMO architecture [5]. When wireless channels are wide-sense stationary uncorrelated scattering (WSSUS), those highly over-determined MIMO systems are well conditioned and linear precoding techniques become near-optimum [6].…”