Power Allocation Schemes for Multicell Massive MIMO Systems

This paper investigates the sum-rate gains brought by power allocation strategies in multicell massive multipleinput multiple-output systems, assuming time-division duplex transmission. For both uplink and downlink, we derive tractable expressions for the achievable rate with zero-forcing receivers and precoders respectively. To avoid high complexity joint optimization across the network, we propose a scheduling mechanism for power allocation, where in a single time slot, only cells that do not interfere with each other adjust their transmit powers. Based on this, corresponding transmit power allocation strategies are derived, aimed at maximizing the sum rate per-cell. These schemes are shown to bring considerable gains over equal power allocation for practical antenna configurations (e.g., up to a few hundred). However, with fixed number of users (N), these gains diminish as M turns to infinity, and equal power allocation becomes optimal. A different conclusion is drawn for the case where both M and N grow large together, in which case: (i) improved rates are achieved as M grows with fixed M/N ratio, and (ii) the relative gains over the equal power allocation diminish as M/N grows. Moreover, we also provide applicable values of M/N under an acceptable power allocation gain threshold, which can be used as to determine when the proposed power allocation schemes yield appreciable gains, and when they do not. From the network point of view, the proposed scheduling approach can achieve almost the same performance as the joint power allocation after one scheduling round, with much reduced complexity

Performance analysis of large scale MU-MIMO with optimal linear receivers

We consider the uplink of multicell multiuser MIMO (MU-MIMO) systems with very large antenna arrays at the base station (BS). We assume that the BS estimates the channel through uplink training, and then uses this channel estimate to detect the signals transmitted from a multiplicity of autonomous users in its cell. By taking the correlation between the channel estimate and the interference from other cells into account, we propose an optimal linear receiver (OLR) which maximizes the received signal-to-interference-plus-noise (SINR). Analytical approximations of the exact and lower bound on the achievable rate are then derived. The bound is very tight, especially at large number of BS antennas. We show that at low SINR, maximalratio combing (MRC) receiver performs as well as OLR, however at high SINR, OLR outperforms MRC. Compared with the typical minimum mean-square error receiver, our proposed OLR improves systematically the system performance, especially when the interference is large

Massive MIMO has Unlimited Capacity

The capacity of cellular networks can be improved by the unprecedented array gain and spatial multiplexing offered by Massive MIMO. Since its inception, the coherent interference caused by pilot contamination has been believed to create a finite capacity limit, as the number of antennas goes to infinity. In this paper, we prove that this is incorrect and an artifact from using simplistic channel models and suboptimal precoding/combining schemes. We show that with multicell MMSE precoding/combining and a tiny amount of spatial channel correlation or large-scale fading variations over the array, the capacity increases without bound as the number of antennas increases, even under pilot contamination. More precisely, the result holds when the channel covariance matrices of the contaminating users are asymptotically linearly independent, which is generally the case. If also the diagonals of the covariance matrices are linearly independent, it is sufficient to know these diagonals (and not the full covariance matrices) to achieve an unlimited asymptotic capacity.Comment: To appear in IEEE Transactions on Wireless Communications, 17 pages, 7 figure

Interference Alignment and Cancellation in Wireless Communication Systems

The Shannon capacity of wireless networks has a fundamental importance for network information theory. This area has recently seen remarkable progress on a variety of problems including the capacity of interference networks, X networks, cellular networks, cooperative communication networks and cognitive radio networks. While each communication scenario has its own characteristics, a common reason of these recent developments is the new idea of interference alignment. The idea of interference alignment is to consolidate the interference into smaller dimensions of signal space at each receiver and use the remaining dimensions to transmit the desired signals without any interference. However, perfect alignment of interference requires certain assumptions, such as perfect channel state information at transmitter and receiver, perfect synchronization and feedback. Today’s wireless communication systems, on the other and, do not encounter such ideal conditions. In this thesis, we cover a breadth of topics of interference alignment and cancellation schemes in wireless communication systems such as multihop relay networks, multicell networks as well as cooperation and optimisation in such systems. Our main contributions in this thesis can be summarised as follows: • We derive analytical expressions for an interference alignment scheme in a multihop relay network with imperfect channel state information, and investigate the impact of interference on such systems where interference could accumulate due to the misalignment at each hop. • We also address the dimensionality problem in larger wireless communication systems such as multi-cellular systems. We propose precoding schemes based on maximising signal power over interference and noise. We show that these precoding vectors would dramatically improve the rates for multi-user cellular networks in both uplink and downlink, without requiring an excessive number of dimensions. Furthermore, we investigate how to improve the receivers which can mitigate interference more efficiently. • We also propose partial cooperation in an interference alignment and cancellation scheme. This enables us to assess the merits of varying mixture of cooperative and non-cooperative users and the gains achievable while reducing the overhead of channel estimation. In addition to this, we analytically derive expressions for the additional interference caused by imperfect channel estimation in such cooperative systems. We also show the impact of imperfect channel estimation on cooperation gains. • Furthermore, we propose jointly optimisation of interference alignment and cancellation for multi-user multi-cellular networks in both uplink and downlink. We find the optimum set of transceivers which minimise the mean square error at each base station. We demonstrate that optimised transceivers can outperform existing interference alignment and cancellation schemes. • Finally, we consider power adaptation and user selection schemes. The simulation results indicate that user selection and power adaptation techniques based on estimated rates can improve the overall system performance significantly