Massive MIMO wireless networks: an overview

Massive multiple-input-multiple-output (MIMO) systems use few hundred antennas to simultaneously serve large number of wireless broadband terminals. It has been incorporated into standards like long term evolution (LTE) and IEEE802.11 (Wi-Fi). Basically, the more the antennas, the better shall be the performance. Massive MIMO systems envision accurate beamforming and decoding with simpler and possibly linear algorithms. However, efficient signal processing techniques have to be used at both ends to overcome the signaling overhead complexity. There are few fundamental issues about massive MIMO networks that need to be better understood before their successful deployment. In this paper, we present a detailed review of massive MIMO homogeneous, and heterogeneous systems, highlighting key system components, pros, cons, and research directions. In addition, we emphasize the advantage of employing millimeter wave (mmWave) frequency in the beamforming, and precoding operations in single, and multi-tier massive MIMO systems. Keywords: 5G wireless networks; massive MIMO; linear precoding; encoding; channel estimation; pilot contamination; beamforming; HetNet

An Optimum User Association Algorithm in Heterogeneous 5G Networks Using Standard Deviation of the Load

Fifth-generation (5G) wireless networks and beyond will be heterogeneous in nature, with a mixture of macro and micro radio cells. In this scenario where high power macro base stations (MBS) coexist with low power micro base stations (mBS), it is challenging to ensure optimal usage of radio resources to serve users with a multitude of quality of service (QoS) requirements. Typical signal to interference and noise ratio (SINR)-based user allocation protocols unfairly assign more users to the high power MBS, starving mBS. There have been many attempts in the literature to forcefully assign users to mBS with limited success. In this paper, we take a different approach using second order statistics of user data, which is a better indicator of traffic fluctuations. We propose a new algorithm for user association to the appropriate base station (BS) by utilizing the standard deviation of the overall network load. This is done through an exhaustive search of the best user equipment (UE)-BS combinations that provide a global minimum to the standard deviation. This would correspond to the optimum number of UEs assigned to every BS, either macro or micro. We have also derived new expressions for coverage probability and network energy efficiency for analytical performance evaluation. Simulation results prove the validity of our proposed methods to balance the network load, improve data rate, average energy efficiency, and coverage probability with superior performance compared with other algorithms.</p

Interference Mitigation and Dynamic User Association for Load Balancing in Heterogeneous Networks

Viewing the communication system as three-dimensional (3-D) with various tiers cooperating among each other is a new trend to present 5G heterogeneous networks (HetNets). Base stations (BS) in each tier operate with different power levels, access methods, and unique topologies. A proper user (UE) association algorithm for HetNets is a great challenge. We develop a new real-time dynamic user (UE) association algorithm for multitier cooperating systems that considers users' mobility and traffic dynamics considering both overall network load and received signal to interference and noise ratio (SINR). Despite that our proposed UE association algorithm does not depend on an interference mitigation algorithm to improve its performance, we develop a location-based interference mitigation algorithm to mitigate co-tier and cross-tier interferences in the worst case scenario of spectrum sharing among various tier BSs to overcome some of the drawbacks of spectrum partitioning algorithms. Our new algorithms are studied and analyzed through simulation and they are proved to provide the best performance compared to other algorithms.  </p