A Load-Aware Clustering Model for Coordinated Transmission in Future Wireless Networks

Abstract

Coordinated multi-point (CoMP) transmission is one of the key features for long term evolution advanced (LTE-A) and a promising concept for interference mitigation in 5th generation (5G) and beyond future densely deployed wireless networks. Due to the cost of coordination among many transmission points (TP), radio access network (RAN) needs to be clustered into smaller groups of TPs for coordination. In this paper, we develop a novel, load-aware clustering model by employing a merge/split concept from coalitional game theory. A load-aware utility function is introduced to maximize both spectral efficiency (SE) and load balancing (LB) objectives. We show that proposed load-aware clustering model dynamically adapts into the network load conditions providing high SE in low-load conditions and results in better load distribution with significantly less unsatisfied users in overload conditions while keeping SE at comparable levels when compared to a greedy clustering model. Simulation results show that the proposed solution can reduce the number of unsatisfied users due to overload conditions by 68.5% when compared to the greedy clustering algorithm. Furthermore, we analyze the stability of the proposed solution and prove that it converges to a stable partition in both homogeneous network (HN) and random network (RN) with and without hotspot scenarios. In addition, we show the convergence of our algorithm into the unique clustering solution with the best payoff possible when such a solution exists. INDEX TERMS 5G, network MIMO, coordinated multi-point, SON, load balancing.

Distributed Cell Clustering Based on Multi-Layer Message Passing for Downlink Joint Processing Coordinated Multipoint Transmission

“…The simulation results compare the performance of the proposed algorithm with those of existing schemes. For this purpose, the proposed algorithm will be compared to existing JP-CoMP clustering methods, in this case novel static clustering [13], coalitional game theory [19], affinity propagation (AP) [26], and capacitated affinity propagation (CAP) [27]. The user throughput, network scalability and complexity are evaluated in the simulation results.…”

“…To this end, dynamic clustering is introduced here to realize additional performance improvements. These methods utilizes different approaches to achieve optimal performance goals, i.e., dynamic network-centric clustering [14], the blossom tree algorithm [15], graph-based clustering [16], the use of sub-cluster [17], a novel re-clustering [18], coalitional game theory [19], density-based spatial clustering [20], the use of channel state prediction [21], a weight traffic model [22], the exchange-matching algorithm [23], mixed-integer nonlinear programming [24], and the successive convex algorithm [25]. Dynamic clustering adapts to network changes, but these methods are designed based on centralized control on the network, which requires extensive information sharing and high computational complexity.…”

Coalition Formation Games for Coordinated Service in Realistic Small Cell Propagation Topologies

“…However, in these studies, JT-CoMP or the parallel service of all the UEs are not considered. In contrast with [25], a load-aware network-centric clustering algorithm is presented in [29], where load balancing and spectral efficiency objectives are jointly optimized through a coalition formation game based on merge and split operations in a JT-CoMP downlink heterogeneous network scenario. The algorithm accounts for various overhead costs and is capable of dynamically adjusting the cluster size to adapt to different network load.…”

“…As in [25], realistic channel parameters were not considered and clustering changes are proposed over longer time intervals meaning that small-scale phenomena are not considered. Furthermore, the proposed algorithm in [29] aims to form coalitions based on total utility improvement, ignoring possible individual payoff reductions. A study that compares static, dynamic distributed and dynamic game theoretic clustering approaches can be found in [30].…”

A QoS-based joint user association and resource allocation scheme in ultra-dense networks

“…Based on clustering results, a fair graph-coloring based intercell resource scheduling can be employed at the second step to maximize the resource utilization. [8,9] present load-aware user-centric CoMP clustering algorithms which consider trade-off between load balance and spectrum efficiency. In addition, some other studies focus on user-centric virtual cell, where each UE has some base stations associated with it to avoid low signal to interference plus noise ratio (SINR) [10,11] propose load-aware virtual cell schemes, which can meet the user QoS requirements.…”