Benchmarking Practical RRM Algorithms for D2D Communications in LTE Advanced

Device-to-device (D2D) communication integrated into cellular networks is a means to take advantage of the proximity of devices and allow for reusing cellular resources and thereby to increase the user bitrates and the system capacity. However, when D2D (in the 3rd Generation Partnership Project also called Long Term Evolution (LTE) Direct) communication in cellular spectrum is supported, there is a need to revisit and modify the existing radio resource management (RRM) and power control (PC) techniques to realize the potential of the proximity and reuse gains and to limit the interference at the cellular layer. In this paper, we examine the performance of the flexible LTE PC tool box and benchmark it against a utility optimal iterative scheme. We find that the open loop PC scheme of LTE performs well for cellular users both in terms of the used transmit power levels and the achieved signal-to-interference-and-noise-ratio (SINR) distribution. However, the performance of the D2D users as well as the overall system throughput can be boosted by the utility optimal scheme, because the utility maximizing scheme takes better advantage of both the proximity and the reuse gains. Therefore, in this paper we propose a hybrid PC scheme, in which cellular users employ the open loop path compensation method of LTE, while D2D users use the utility optimizing distributed PC scheme. In order to protect the cellular layer, the hybrid scheme allows for limiting the interference caused by the D2D layer at the cost of having a small impact on the performance of the D2D layer. To ensure feasibility, we limit the number of iterations to a practically feasible level. We make the point that the hybrid scheme is not only near optimal, but it also allows for a distributed implementation for the D2D users, while preserving the LTE PC scheme for the cellular users.Comment: 30 pages, submitted for review April-2013. See also: G. Fodor, M. Johansson, D. P. Demia, B. Marco, and A. Abrardo, A joint power control and resource allocation algorithm for D2D communications, KTH, Automatic Control, Tech. Rep., 2012, qC 20120910, http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10205

Flexible Power Modeling of LTE Base Stations

With the explosion of wireless communications in number of users and data rates, the reduction of network power consumption becomes more and more critical. This is especially true for base stations which represent a dominant share of the total power in cellular networks. In order to study power reduction techniques, a convenient power model is required, providing estimates of the power consumption in different scenarios. This paper proposes such a model, accurate but simple to use. It evaluates the base station power consumption for different types of cells supporting the 3GPP LTE standard. It is flexible enough to enable comparisons between state-of-the-art and advanced configurations, and an easy adaptation to various scenarios. The model is based on a combination of base station components and sub-components as well as power scaling rules as functions of the main system parameters

Distributed Optimization of Multi-Cell Uplink Co-operation with Backhaul Constraints

We address the problem of uplink co-operative reception with constraints on both backhaul bandwidth and the receiver aperture, or number of antenna signals that can be processed. The problem is cast as a network utility (weighted sum rate) maximization subject to computational complexity and architectural bandwidth sharing constraints. We show that a relaxed version of the problem is convex, and can be solved via a dual-decomposition. The proposed solution is distributed in that each cell broadcasts a set of {\em demand prices} based on the data sharing requests they receive. Given the demand prices, the algorithm determines an antenna/cell ordering and antenna-selection for each scheduled user in a cell. This algorithm, referred to as {\em LiquidMAAS}, iterates between the preceding two steps. Simulations of realistic network scenarios show that the algorithm exhibits fast convergence even for systems with large number of cells.Comment: IEEE ICC Conference, 201