A green scheduling with adaptive spatial division multiple access grouping for multi‐user coordinated multi‐point networks. Issue 6 (13th January 2015) - Chaabane, Imen Ben; Hamouda, Soumaya; Tabbane, Sami
Abstract: Multi‐User Coordinated Multi‐Point (MU‐CoMP), which couples CoMP with Multi User‐Multiple Input Multiple Output (MU‐MIMO), appears as a promising solution to enhance the Long Term Evolution‐Advanced (LTE‐A) system performance. However, some challenging issues in MU‐CoMP networks require more investigation. First, the set of users transmitting under CoMP mode should be properly identified. Secondly, time‐frequency resource should be efficiently partitioned between CoMP and non‐CoMP users in order to improve the system radio capacity. Thirdly, a fair and green scheduler is much needed for a more energy efficient system. This paper deals with these three issues. We propose an adaptive transmission mode selection according to the total load in the cluster and to users' quality of service. The adequate size of the Spatial Division Multiple Access users' groups is also analyzed. We finally propose a new scheduling algorithm to further enhance the radio capacity and the energy consumption in the cluster. Simulations results showed that significant improvements are obtained in terms of total system throughput and outage probability in the cluster with our proposed scheme. Moreover, energy efficiency has increased by four times with our proposed scheduling algorithm as compared to commonly used schedulers. Copyright © 2015 John Wiley & Sons, Ltd. Abstract : We address three main issues in Multi‐user (MU)‐Coordinated Multi‐point (CoMP) networks: identification of the set of CoMP users, time‐frequency allocation, and scheduling of CoMP user traffic. We propose an adaptive transmission mode selection according to cluster load and user quality of service. The adequate size of the Space‐Division Multiple Access user groups is then analyzed. Finally, we propose a new scheduler to further enhance radio capacity and energy consumption in the cluster. Simulations showed that energy efficiency has increased by four times as compared with common schedulers.