Manijeh Bashar

Robust User Scheduling with COST 2100 Channel Model for Massive MIMO Networks

This paper considers a Massive multiple-input multiple-output (MIMO) network, where the base station (BS) with a large number of antennas communicates with a smaller number of users. The signals are transmitted using frequency division duplex (FDD) mode. The problem of user scheduling with reduced overhead of channel estimation in the uplink of Massive MIMO systems has been investigated. We consider the COST 2100 channel model. In this paper, we first propose a new user selection algorithm based on knowledge of the geometry of the service area and of location of clusters, without having full channel state information (CSI) at the BS. We then show that the correlation in geometry-based stochastic channel models (GSCMs) arises from the common clusters in the area. In addition, exploiting the closed-form Cramer-Rao lower bounds (CRLB)s, the analysis for the robustness of the proposed scheme to cluster position errors is presented. It is shown by analysing the capacity upper-bound that the capacity strongly depends on the position of clusters in the GSCMs and users in the system. Simulation results show that although the BS receiver does not require the channel information of all users, by the proposed geometry-based user scheduling (GUS) algorithm the sum-rate of the system is only slightly less than the well-known greedy weight clique (GWC) scheme \cite{SUSGoldsmithGlobcom,ITC09_Userselection_GWC}. {Finally, the robustness of the proposed algorithm to cluster localization is verified by the simulation results.

Mixed Quality of Service in Cell-Free Massive MIMO

A mixed quality-of-service (QoS) problem is investigated in the uplink of a cell-free massive multiple-input multiple-output system where the minimum rate of non-real time users is maximized with per user power constraints while the rates of the real-time users (RTUs) meet their target rates. The original mixed QoS problem is formulated in terms of receiver filter coefficients and user power allocations, which can iteratively be solved through two sub-problems, namely, receiver filter coefficient design and power allocation. Numerical results show that, while the rates of RTUs meet the QoS constraints, the 90%-likely throughput improves significantly, compared with a simple benchmark scheme.

On convolutional lattice codes and lattice decoding using Trellis structure

Constructing hypercubic lattices from convolutional codes based on Constructions A and D is investigated in this paper and their error performance in a point-to-point communication system is studied. Moreover, analogous to Construction A/D, single/multilayer code lattices are proposed. As Construction D requires certain minimum Euclidean distance criteria, we propose methods to guarantee the distance requirements of Construction D, which results in a superior lattice construction compared with Construction A. Due to the key role of soft input soft out decoding algorithms in improving the performance of a code, lattice decoding based on the Bahl, Cocke, Jelinek and Raviv (BCJR) algorithm for lattices constructed from convolutional codes is also proposed in this paper. Moreover, as the BCJR algorithm requires knowledge of the statistical characteristics of modulo lattice additive noise (MLAN), the probability density function of MLAN is derived in closed form.