In a multicast scenario, the performance is usually determined, and therefore limited, by the weakest link present in the system. With multiple co-channel multicast groups, the problem is further exacerbated due to interference from other transmissions. In this work we investigate an alternative communication scheme, in which additional coding at the application layer is used, spanning over a number of channel realizations. Aiming at maximization of the weighted sum of rates achieved in each group, we show that the optimal transmission strategy depends only on the current channel realization, which, assuming multiple antennas at the base station, allows for formulation of an interesting transmit beamforming problem. In order to find the solution of the problem, we show that the utility-based power control framework, developed for a network consisting of a number of point-to-point wireless links, can be generalized to the case of multigroup multicast. Building upon this framework, we propose iterative beamforming algorithms which can be applied in scenarios both with and without additional coding at the application layer. Numerical experiments are included in the paper to demonstrate the performance of the proposed algorithms
In this paper, we study the problem of reducing the energy consumption in a mobile communication network; we select the smallest set of active base stations that can preserve the quality of service (the minimum data rate) required by the users. In more detail, we start by posing this problem as an integer programming problem, the solution of which shows the optimal assignment (in the sense of minimizing the total energy consumption) between base stations and users. In particular, this solution shows which base stations can then be switched off or put in idle mode to save energy. However, solving this problem optimally is intractable in general, so in this study we develop a suboptimal approach that builds upon recent techniques that have been successfully applied to, among other problems, sparse signal reconstruction, portfolio optimization, statistical estimation, and error correction. More precisely, we relax the original integer programming problem as a minimization problem where the objective function is concave and the constraint set is convex. The resulting relaxed problem is still intractable in general, but we can apply the majorization-minimization algorithm to find good solutions (i.e., solutions attaining low objective value) with a low-complexity algorithm. In contrast to state-of-the-art approaches, the proposed algorithm can take into account inter-cell interference, is suitable for large-scale problems, and can be applied to heterogeneous networks (networks where base station consume different amounts of energy)
A communication scheme for the multiple antenna multicast fading channel is proposed, in which the transmission is coded at the application layer over a number of channel realizations. The scheme gives rise to a novel multicast transmit beamforming problem. The properties of the proposed scheme are presented, in particular the scaling of the achievable rate for the increasing number of users is investigated and the rate is shown not to decrease to zero, which is an improvement over multicast schemes without coding, e.g. so-called max-min transmit beamforming. Algorithms for solving the resulting beamforming problem are proposed and evaluated in simulation
We devise novel techniques to obtain the downlink power inducing a given load in long-term evolution (LTE) systems, where we define load as the fraction of resource blocks in the time-frequency grid being requested by users from a given base station. These techniques are particularly important because previous studies have proved that the data rate requirement of users can be satisfied with lower transmit energy if we allow the load to increase. Those studies have also shown that obtaining the power assignment inducing a desired load profile can be posed as a fixed point problem involving standard interference mappings, but so far the mappings have not been obtained explicitly. One of our main contributions in this study is to close this gap. We derive an interference mapping having as its fixed point the power assignment inducing a desired load, assuming that such an assignment exists. Having this mapping in closed form, we simplify the proof of the aforementioned known results, and we also devise novel iterative algorithms for power computation that have many numerical advantages over previous methods
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