Hybrid Precoding in Cooperative Millimeter Wave Networks

In this paper, we study the performance of cooperative millimeter wave (mmWave) networks with hybrid precoding architectures. Considering joint transmissions and BS silence strategy, we propose hybrid precoding algorithms which minimize the sum power consumption of the base stations (BSs), for both fully- and partially-connected hybrid precoding (FHP and PHP, respectively) schemes, for single-carrier and orthogonal frequency-division multiplexing systems. We reformulate the analog precoding part as an equal-gain transmission problem, which only depends on the channel information, and the digital precoding part as a relaxed convex semidefinite program subject to per-user quality-of-service constraints that gives the optimal sum power consumption in terms of the BS silence strategy. In order to reduce the complexity of the hybrid precoding algorithm with optimal BS silence strategy, we propose a sub-optimal hybrid precoding algorithm that iteratively put BSs with small power into the silent mode. The simulation results show that, depending on the parameter settings, the power consumption of the PHP may be dominated by the RF transmit power and it may result in a larger power consumption than the FHP. For the cases with 2 BSs and 4 users, implementation of the FHP and the PHP in cooperative networks reduces the required RF transmit power, compared to the case in a non-cooperative network, by 71% and 56%, respectively.Comment: 16 pages, 10 figures, accepted to IEEE Transactions on Wireless Communication

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

Joint Precoding and Load Balancing Optimization for Energy-Efficient Heterogeneous Networks

International audienceThis paper considers a downlink heterogeneous network , where different types of multiantenna base stations (BSs) communicate with a number of single-antenna users. Multiple BSs can serve the users by spatial multiflow transmission techniques. Assuming imperfect channel state information at both BSs and users, the precoding, load balancing, and BS operation mode are jointly optimized for improving the network energy efficiency. We minimize the weighted total power consumption while satisfying quality-of-service constraints at the users. This problem is non-convex, but we prove that for each BS mode combination, the considered problem has a hidden convexity structure. Thus, the optimal solution is obtained by an exhaustive search over all possible BS mode combinations. Furthermore, by iterative convex approximations of the nonconvex objective function, a heuristic algorithm is proposed to obtain a suboptimal solution of low complexity. We show that although multicell joint transmission is allowed, in most cases, it is optimal for each user to be served by a single BS. The optimal BS association condition is parameterized, which reveals how it is impacted by different system parameters. Simulation results indicate that putting a BS into sleep mode by proper load balancing is an important solution for energy savings

Resource Management in Cloud-based Radio Access Networks: a Distributed Optimization Perspective

University of Minnesota Ph.D. dissertation. 2015. Major: Electrical Engineering. Advisor: Zhi-Quan Luo. 1 computer file (PDF); ix, 136 pages.In this dissertation, we consider the base station (BS) and the resource management problems for the cloud-based radio access network (C-RAN). The main difference of the envisioned future 5G network architecture is the adoption of multi-tier BSs to extend the coverage of the existing cellular BSs. Each of the BS is connected to the multi-hop backhaul network with limited bandwidth. For provisioning the network, the cloud centers have been proposed to serve as the control centers. These differences give rise to many practical challenges. The main focus of this dissertation is the distributed strategy across the cloud centers. First, we show that by jointly optimizing the transceivers and determining the active set of BSs, high system resource utilization can be achieved with only a small number of BSs. In particular, we provide efficient distributed algorithms for such joint optimization problem, under the following two common design criteria: i) minimization of the total power consumption at the BSs, and ii) maximization of the system spectrum efficiency. In both cases, we introduce a nonsmooth regularizer to facilitate the activation of the most appropriate BSs, and the algorithms are, respectively, developed with Alternating Direction Method of Multipliers (ADMM) and weighted minimum mean square error (WMMSE) algorithm. In the second part, we further explicitly consider the backhaul limitation issues. We propose an efficient algorithm for joint resource allocation across the wireless links and the flow control over the entire network. The algorithm, which maximizes the utility function of the rates among all the transmitted commodities, is based on a decomposition approach leverages both the ADMM and the WMMSE algorithms. This algorithm is shown to be easily parallelizable within cloud centers and converges globally to a stationary solution. Lastly, since ADMM has been popular for solving large-scale distributed convex optimization, we further consider the issues of the network synchronization across the cloud centers. We propose an ADMM-type implementation that can handle a specific form of asynchronism based on the so-called BSUM-M algorithm, a new variant of ADMM. We show that the proposed algorithm converges to the global optimal solution