Performance analysis of massive MIMO networks

Abstract

Mobile data tra_c is predicted to grow 1000x from now until 2030 [1, 2], and dense small cell networks (SCNs) and massive multiple input multiple output (mMIMO) are considered the major pilar technologies to meet this ever-increasing capacity demand in the years to come. Dense SCNs which is comprised of picocells, femtocells, metrocells, etc are considered to be one of the main approaches to signi_cantly increase the network capacity and meet the capacity demand in 5G network. Indeed, the orthogonal deployment of SCNs with existing macrocells has already been applied as one solution in 4thgeneration and 5-th generation networks by the 3rd Generation Partnership Project (3GPP). SCNs are able to enhance the network capacity because of the high spatial reuse, eg, network capacity could potentially grow linearly with the number of small cells. In this thesis, We _rst discuss the performance analysis of dense SCNs using stochastic geometry. On the other hand, massive multiple input and multiple output (mMIMO) is also considered as one of the most important candidate technologies to meet the everincreasing capacity demand in the years to come [2]. By exploiting its many antennas and thus degrees of freedom in the spatial domain, mMIMO can increase the per-cell and the area spectral e_ciency (ASE) through spatial multiplexing. The larger the number of antennas, the larger the number of degrees of freedom, and thus the more multiplexing opportunities. However, when a time division duplex (TDD) system is considered, the performance of mMIMO may be limited by inaccurate channel state information (CSI). Pilot contamination is one of the major bottlenecks, and occurs when the same set of uplink (UL) pilot sequences is reused across neighbouring cells. Other channel estimation impairments also play a role. In this thesis, we _rstly introduce the performance evaluation of dense SCNs using stochastic. Previously, performance analysis of cellular networks is under the assumption that the base stations(BSs) and user equipments(UEs) are placed randomly or deterministically. The models under these assumptions are highly idealized. Stochastic geometry is introduced as a very tractable approach to analyze the networks performance of cellular networks. However, it is notable that the results are base on considerable simpli_cation on network scenarios. In this thesis, more realworld features of SCNs are considered. The BSs are activated when there is UE connected to them which is called Idle Mode. moreover, Both piece-wise path loss function and probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) transmission are further considered. The analysis demonstrates that when the activated BS density is larger than a threshold, the coverage is su_er a decrease, which results in a slow growth or a decrease in ASE. Since LoS and NLoS transmission are considered, the probability of an interference path changing from NLoS to LoS becomes larger. As a result, the deployment of SCNs should be paid more attention as increasing BS density will probably lead to a small improvement of network performance or even a worse result. In addition to dense SCNs, mMIMO, considered as a scaled-up version of multiuser MIMO (MU-MIMO), it is important to note that the larger the number of antennas, the larger the number of degrees of freedom, and thus the more multiplexing opportunities. However, when time division duplex (TDD) systems are considered, due to a _nite channel coherent time, the performance of mMIMO systems may be limited by inaccurate channel state information (CSI). Pilot contamination is considered as a major bottleneck, which occurs when the same set of uplink training sequences is reused across neighbouring cells [3]. Other channel estimation impairments also play a role. In this thesis, we conduct performance analysis for uplink (UL) massive multiple input and multiple output (mMIMO) networks using stochastic geometry. With the consideration of practical system assumptions, such as sophisticated path loss model incorporating both LoS and NLoS transmissions and a _nite user equipment (UE) density, we derive the coverage probability and the area spectral e_ciency (ASE) performance.