Performance of Cell-Free Massive MIMO With Rician Fading and Phase Shifts

Abstract

In this paper, we study the uplink (UL) and downlink (DL) spectral efficiency (SE) of a cell-free massive multipleinput-multiple-output (MIMO) system over Rician fading channels. The phase of the line-of-sight (LoS) path is modeled as a uniformly distributed random variable to take the phase-shifts due to mobility and phase noise into account. Considering the availability of prior information at the access points (APs), the phase-aware minimum mean square error (MMSE), non-aware linear MMSE (LMMSE), and least-square (LS) estimators are derived. The MMSE estimator requires perfectly estimated phase knowledge whereas the LMMSE and LS are derived without it.In the UL, a two-layer decoding method is investigated in order to mitigate both coherent and non-coherent interference. Closedform UL SE expressions with phase-aware MMSE, LMMSE, and LS estimators are derived for maximum-ratio (MR) combining in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. In the DL, two different transmission modes are studied: coherent and non-coherent. Closed-form DL SE expressions for both transmission modes with MR precoding are derived for the three estimators. Numerical results show that the LSFD improves the UL SE performance and coherent transmission mode performs much better than non-coherent transmission in the DL. Besides, the performance loss due to the lack of phase information depends on the pilot length and it is small when the pilot contamination is low.

Prospective Multiple Antenna Technologies for Beyond 5G

“…The original papers [37], [38] considered singleantenna APs, single-antenna UEs, Rayleigh fading channels, and infinite capacity error-free fronthaul connections. Later works have studied more realistic setups, such as singleantenna APs with Rician fading channels [41], [42], multiantenna APs with uncorrelated [43], [44] or correlated [45], [46] fading, multi-antenna UEs [47], [48], and hardware impairments [49], [50]. The impact of finite-resolution fronthaul connections (i.e., when both CSI and the received signal must be quantized) was considered in [44].…”

“…, K) [53]. Different channel estimators can be used depending on the channel model, but we will not cover those details to keep the description general and short; we refer the interested readers to [42], [48], [54]. Deep learning can also be used to estimate the channel [55], [56].…”

“…This method maximizes the received signal power without taking the existence of other UEs into account. One key benefit of using this method is that expectations in (9) can often be computed in closed form; for example, under uncorrelated [37] or correlated [46] Rayleigh fading and for Rician fading [42]. However, higher spectral efficiencies are achieved by local minimum mean-square error (L-MMSE) combining, for which the combining vector can be expressed as 1…”

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

“…Deep Learning [176][177][178][179][180] Channel Model Rician fading [181][182][183] Spatially correlated Rayleigh fading [184][185][186] Miscellaneous Full-duplex [187][188][189] Channel non-reciprocity [190] Asynchronous reception [191] System information broadcast [192] Over-the-air signaling [193,194] Multi-antenna users [195,196] Frequency division duplex [197] Stochastic geometry [198] Dynamic Resource allocation [199] Wireless power transfer [200]…”

Large-Scale Fading Precoding for Maximizing the Product of SINRs

“…However compared to most of the existing literature, we consider a more realistic scenario in which phase of the LOS component varies at the same pace as the small-scale fading, due movement, and the phase is unknown. We assume the random phase shifts {θ r lk } are distributed uniformly on [0, 2π) [13].…”