Low complexity interference aware distributed resource allocation for multi-cell OFDMA cooperative relay networks

In this paper we focus on the subcarrier allocation for the uplink OFDMA based cooperative relay networks. Multiple cells were considered, each composed of a single base station (destination), multiple amplify and forward (AF) relay stations and multiple subscriber stations (sources). The effects of inter-cell interference (ICI) have been considered to optimize the subcarrier allocation with low complexity. The optimization problem aims to maximize the sum rate of all sources and at the same time maintain the fairness among them. Full channel state information (CSI) is assumed to be available at the base station. In the proposed algorithm the subcarrier allocation is performed in three steps; firstly the subcarriers are allocated to the Relay Stations (RSs) by which the received ICI on each RS is minimized. Then, the pre-allocated subcarriers are allocated to subscribers to achieve their individual rate requirements. Finally the remaining subcarriers are allocated to subscribers with the best channel condition to maximize the total sum of their data rates. The results show that the proposed algorithm significantly reduces the complexity with almost the same achievable rate of the optimal allocation in a single cell case. In case of multi-cell, the proposed algorithm outperforms the conventional algorithm in terms of total network achievable data rate and overall network complexity. ©2010 IEEE

Energy-Efficient Power Control: A Look at 5G Wireless Technologies

This work develops power control algorithms for energy efficiency (EE) maximization (measured in bit/Joule) in wireless networks. Unlike previous related works, minimum-rate constraints are imposed and the signal-to-interference-plus-noise ratio takes a more general expression, which allows one to encompass some of the most promising 5G candidate technologies. Both network-centric and user-centric EE maximizations are considered. In the network-centric scenario, the maximization of the global EE and the minimum EE of the network are performed. Unlike previous contributions, we develop centralized algorithms that are guaranteed to converge, with affordable computational complexity, to a Karush-Kuhn-Tucker point of the considered non-convex optimization problems. Moreover, closed-form feasibility conditions are derived. In the user-centric scenario, game theory is used to study the equilibria of the network and to derive convergent power control algorithms, which can be implemented in a fully decentralized fashion. Both scenarios above are studied under the assumption that single or multiple resource blocks are employed for data transmission. Numerical results assess the performance of the proposed solutions, analyzing the impact of minimum-rate constraints, and comparing the network-centric and user-centric approaches.Comment: Accepted for Publication in the IEEE Transactions on Signal Processin

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

Flexible resource allocation for joint optimization of energy and spectral efficiency in OFDMA multi-cell networks

The radio resource allocation problem is studied, aiming to jointly optimize the energy efficiency (EE) and spectral efficiency (SE) of downlink OFDMA multi-cell networks. Different from existing works on either EE or SE optimization, a novel EE-SE tradeoff (EST) metric, which can capture both the EST relation and the individual cells’ preferences for EE or SE performance, is introduced as the utility function for each base station (BS). Then the joint EE-SE optimization problem is formulated, and an iterative subchannel allocation and power allocation algorithm is proposed. Numerical results show that the proposed algorithm can exploit the EST relation flexibly and optimize the EE and SE simultaneously to meet diverse EE and SE preferences of individual cells.<br/