This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/itl2.89 Data-Rate Performance Analysis of Massive MIMO in Fading Environment with Imperfect CSI Tasher Ali Sheikh, Joyatri Bora, Md. Anwar Hussain Department of Electronics and Communication Engineering North Eastern Regional Institute of Science and Technology, Arunachal Pradesh, India-791109 tasher372@gmail.com, jb@nerist.ac.in, ah@nerist.ac.in Abstract— Performance of Massive MIMO system is dependent on the fading channel type and its state information. The channel state information (CSI) is not always perfect and hence the user data-rate suffers. Further the sum data-rate of the system as well as the user are also dependendent on both small scale and large fading (SSF and LSF) of the channel. We explore in this paper the data-rate performance of a massive MIMO system with users uniformly distributed, for scheduling a specific number of users (8, 10, and 12) with variation of number of base station antennas, number of active users, SNRs, and considering zero forcing (ZF) precoding. We consider highly scattering Rayleigh fading channel for SSF, and also LSF due to distance of the user locations. We assume that CSI is imperfect and introduces a term that may be considered as causing interference or additional noise. The data rate performance is studied for Semi-orthogonal user scheduling (SUS), Random user scheduling (RUS), and Distance dependent user scheduling (DUS), whereas corresponding number of antennas are selected based on maximum SNR. Keywords— Massive MIMO;5G; User Scheduling and Antenna selection, Small scale fading (SSF); Large scale fading (LSF); Channel estimation error; I. INTRODUCTION Recently massive multiple input multiple output (MIMO) systems have drawn special attention in the cellular network data communication over conventional single input single output (SISO) systems. MIMO is seen as a key role player in terms of considerable improvement in the system capacity, spectrum efficiency, link reliability, energy efficiency, resistance to interference, providing quality of service, increase in the coverage area, and degree of freedom [1]. Massive MIMO also has appeared as the foundation and enabler element of the 5G cellular network data communication. The increase in the capacity results from parallel transmissions to large number of users with a common spectrum. The base station (BS) contains a large number of antennas transmitting signals to a large number of users each with a single antenna or more than one antenna with different path that enhance capacity and link reliably of the system. Also, the system is able to resist intentional jamming and interference in such massive MIMO network. For the beam-forming capacity with massive MIMO technology, the specified spectrum may be utilized [2]. The enhancement of system capacity and energy efficiency in massive MIMO system is totally dependent on joint user scheduling and base station antenna selection appropriately. But joint selection of optimal number of best users and antennas in terms of some performance index of cellular massive MIMO network is not easy to achieve. The strategy may require selecting a maximum number of users, each one allocated a base station antenna so that the system sum-rate is maximum possible. Also the system sum-rate for transmitting data to a specified number of best users, each allocated one best antenna in some sense may be required to be maximized. For user selection, mostly random user selection (RUS) and round robin algorithm (RRA) are widely used as it has very low computational cost for an approximate same capacity. As massive MIMO network has large number of users and antennas, the probability to obtain users with almost orthogonal channels is more. Hence semi-orthogonal user selection (SUS) is most popular and widely used method for very good system capacity and low interference and computational cost [3]. For efficient spectrum and energy efficiency, selections of the best users are required in conventional MIMO cellular network. In the same way it is also needed in the massive MIMO cellular network [4- 7]. In conventional MIMO network the numbers of users are usually more than the numbers of base station antennas. But in massive MIMO network, numbers of base station antennas are very larger than the number of users. With N no. of base station antennas and K no. of users in the massive MIMO network, the cost of computational complexity become very large that is commonly O(N 3 K) for the SUS algorithm. The massive MIMO network may use both FDD and TDD frame work for user scheduling and data communication. The TDD based massive MIMO network is most popular as it allocates the whole spectrum to each individual user so that the system capacity improves significantly. To obtain maximum system sum-rate and reduce computational cost within a limited backhaul capacity for downlink TDD based massive MIMO network, authors in [8] proposed three joint antenna and semi-orthogonal user selection (JASUS) algorithms. In these algorithms users contributing insignificantly to system sum-rate enhancement are removed iteratively. A swapping based and simplified JASUS algorithm is suggested in [9] for downlink massive MIMO network with ZF precoding improving the system sum-rate and reducing the computational complexity. A low complexity JASUS algorithm with optimal system sum-rate growth is reported in [10] where the users and antennas are removed step by step. Moreover, to achieve higher spectral efficiency in massive MIMO non-orthogonal multiple access system, for single-band two-users and multi-band multi-users JASUS algorithm is presented in [11]. For increase in the system sum-rate and minimization of feedback overhead for FDD based massive MIMO system, maximum channel gain based antenna group scheduling algorithm is suggested in [12]. In this paper we consider imperfect channel estimation by the base station from the pilot signal transmitted by the user antennas, each user has only one antenna for transmission and reception of signals. Hence there is channel estimation error which causes interference noise in the reception of the wanted signals. We also consider that the channel consists of both small scale fading (SSF) and large scale fading (LSF) components in its action on the transmitted signals. In such scenario of channel estimation error, we are interested to study performance of massive MIMO systems. We further consider that the users are distributed uniformly within a circular geographical area This article is protected by copyright. All rights reserved.