Power Control for D2D Underlay in Multi-cell Massive MIMO Networks

This paper proposes a new power control and pilot allocation scheme for device-to-device (D2D) communication underlaying a multi-cell massive MIMO system. In this scheme, the cellular users in each cell get orthogonal pilots which are reused with reuse factor one across cells, while the D2D pairs share another set of orthogonal pilots. We derive a closed-form capacity lower bound for the cellular users with different receive processing schemes. In addition, we derive a capacity lower bound for the D2D receivers and a closed-form approximation of it. Then we provide a power control algorithm that maximizes the minimum spectral efficiency (SE) of the users in the network. Finally, we provide a numerical evaluation where we compare our proposed power control algorithm with the maximum transmit power case and the case of conventional multi-cell massive MIMO without D2D communication. Based on the provided results, we conclude that our proposed scheme increases the sum spectral efficiency of multi-cell massive MIMO networks.Comment: 6 Pages, 3 Figures, WSA 201

Dealing with Interference in Distributed Large-scale MIMO Systems: A Statistical Approach

This paper considers the problem of interference control through the use of second-order statistics in massive MIMO multi-cell networks. We consider both the cases of co-located massive arrays and large-scale distributed antenna settings. We are interested in characterizing the low-rankness of users' channel covariance matrices, as such a property can be exploited towards improved channel estimation (so-called pilot decontamination) as well as interference rejection via spatial filtering. In previous work, it was shown that massive MIMO channel covariance matrices exhibit a useful finite rank property that can be modeled via the angular spread of multipath at a MIMO uniform linear array. This paper extends this result to more general settings including certain non-uniform arrays, and more surprisingly, to two dimensional distributed large scale arrays. In particular our model exhibits the dependence of the signal subspace's richness on the scattering radius around the user terminal, through a closed form expression. The applications of the low-rankness covariance property to channel estimation's denoising and low-complexity interference filtering are highlighted.Comment: 12 pages, 11 figures, to appear in IEEE Journal of Selected Topics in Signal Processin

Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33\% compared with the one attained without optimizing power sharing factors

Power allocation and user selection in multi-cell: multi-user massive MIMO systems

Submitted in fulfilment of the academic requirements for the degree of Master of Science (Msc) in Engineering, in the School of Electrical and Information Engineering (EIE), Faculty of Engineering and the Built Environment, at the University of the Witwatersrand, Johannesburg, South Africa, 2017The benefits of massive Multiple-Input Multiple-Output (MIMO) systems have made it a solution for future wireless networking demands. The increase in the number of base station antennas in massive MIMO systems results in an increase in capacity. The throughput increases linearly with an increase in number of antennas. To reap all the benefits of massive MIMO, resources should be allocated optimally amongst users. A lot of factors have to be taken into consideration in resource allocation in multi-cell massive MIMO systems (e.g. intra-cell, inter-cell interference, large scale fading etc.) This dissertation investigates user selection and power allocation algorithms in multi-cell massive MIMO systems. The focus is on designing algorithms that maximizes a particular cell of interest’s sum rate capacity taking into consideration the interference from other cells. To maximize the sum-rate capacity there is need to optimally allocate power and select the optimal number of users who should be scheduled. Global interference coordination has very high complexity and is infeasible in large networks. This dissertation extends previous work and proposes suboptimal per cell resource allocation models that are feasible in practice. The interference is introduced when non-orthogonal pilots are used for channel estimation, resulting in pilot contamination. Resource allocation values from interfering cells are unknown in per cell resource allocation models, hence the inter-cell interference has to be modelled. To tackle the problem sum-rate expressions are derived to enable power allocation and user selection algorithm analysis. The dissertation proposes three different approaches for solving resource allocation problems in multi-cell multi-user massive MIMO systems for a particular cell of interest. The first approach proposes a branch and bound algorithm (BnB algorithm) which models the inter-cell interference in terms of the intra-cell interference by assuming that the statistical properties of the intra-cell interference in the cell of interest are the same as in the other interfering cells. The inter-cell interference is therefore expressed in terms of the intra-cell interference multiplied by a correction factor. The correction factor takes into consideration pilot sequences used in the interfering cells in relation to pilot sequences used in the cell of interest and large scale fading between the users in the interfering cells and the users in the cell of interest. The resource allocation problem is modelled as a mixed integer programming problem. The problem is NP-hard and cannot be solved in polynomial time. To solve the problem it is converted into a convex optimization problem by relaxing the user selection constraint. Dual decomposition is used to solve the problem. In the second approach (two stage algorithm) a mathematical model is proposed for maximum user scheduling in each cell. The scheduled users are then optimally allocated power using the multilevel water filling approach. Finally a hybrid algorithm is proposed which combines the two approaches described above. Generally in the hybrid algorithm the cell of interest allocates resources in the interfering cells using the two stage algorithm to obtain near optimal resource allocation values. The cell of interest then uses these near optimal values to perform its own resource allocation using the BnB algorithm. The two stage algorithm is chosen for resource allocation in the interfering cells because it has a much lower complexity compared to the BnB algorithm. The BnB algorithm is chosen for resource allocation in the cell of interest because it gives higher sum rate in a sum rate maximization problem than the two stage algorithm. Performance analysis and evaluation of the developed algorithms have been presented mainly through extensive simulations. The designed algorithms have also been compared to existing solutions. In general the presented results demonstrate that the proposed algorithms perform better than the existing solutions.XL201

Pilot Contamination and Mitigation Techniques in Massive MIMO Systems

A multi-antenna base station (BS) can spatially multiplex a few terminals over the same bandwidth, a technique known as multi-user, multiple-input multiple-output (MU-MIMO). A new idea in cellular MU-MIMO is the use of a large excess of BS antennas to serve several single-antenna terminals simultaneously. This so-called "massive MIMO" promises attractive gains in spectral efficiency with time-division duplex operation. Within a cell, the BS estimates the channel from mutually orthogonal reverse-link pilot sequences to formulate a receiver for the reverse link and (assuming reciprocity) a precoder for the forward link. The channel coherence is typically constrained in time as well as frequency, leading to a trade-off between the resources spent on pilots and those available for data symbols. This pilot overhead can be reduced by reusing pilot sequences in nearby cells, however this potentially introduces interference in the channel estimation phase, the so-called "pilot contamination" effect. In this thesis, we study the impact of pilot contamination in realistic environments and investigate schemes to mitigate it. We evaluate the mean squared error (MSE) of channel estimates in case of a plain-vanilla least-squares (LS) estimator and a minimum MSE (MMSE) estimator that exploits prior knowledge of second-order channel statistics. Next, we introduce a pilot open-loop power control (pilot OLPC) scheme to improve the SINR-fairness of received pilot signals at the BS. We evaluate the effect of relaxing the pilot reuse factor and also implement a soft pilot reuse (SPR) scheme to distribute pilot sequences efficiently. To study the trade-off between pilot and data symbols, we evaluate the achievable rate in forward link with maximum-ratio and zero-forcing precoding at the BS. We evaluate an inter-cell coordination scheme that exploits prior knowledge of all cross-channel covariance matrices to reuse pilots among spatially well-separated terminals. We simulate a 21-cell MU-MIMO setup with up to 100-antenna BSs and up to 24 single-antenna terminals per cell in an outdoor urban macro environment. We find that pilot reuse 1 causes severe impairment of the channel estimates, which can be improved with pilot OLPC. Pilot reuse 1/3 effectively mitigates pilot contamination, and can improve the achievable rate in the forward link. SPR also mitigates contamination but with a smaller increase in pilot overhead. Inter-cell coordinated pilot allocation, implemented using a greedy approach, provides gains over random allocation only for the initial few pilots. In general, maximum ratio precoding is more robust against pilot contamination than zero-forcing.A multi-antenna base station (BS) can be used to improve cellular communication performance. The signal at each antenna can be designed in a way that it increases received energy at the desired terminals, and attenuates it at other locations (reducing interference). This technique can be used to serve several terminals over the same time and frequency using independent data streams, known as multi-user MIMO (MU-MIMO). In this thesis, we investigate MU-MIMO approach for very large BS antenna arrays, also called massive MIMO. The performance of such systems depends critically on the quality of channel estimates the BS. We simulate realistic channel conditions in a multi-cell setup, which gives rise to interference during channel estimation. We evaluate the system performance in terms of quality of BS channel estimates and the achievable data rate within a cell. We evaluate different techniques for channel estimation, and for generating data streams from the BS to the terminals. Next, we evaluate schemes to improve the channel estimation. We conclude by noting the trade-offs involved in the various schemes and the conditions under which certain schemes might provide performance improvements

Channel estimation in massive MIMO systems

Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference. The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity. This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes. System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance