Self-organised multi-objective network clustering for coordinated communications in future wireless networks

The fifth generation (5G) cellular system is being developed with a vision of 1000 times more capacity than the fourth generation (4G) systems to cope with ever increasing mobile data traffic. Interference mitigation plays an important role in improving the much needed overall capacity especially in highly interference-limited dense deployment scenarios envisioned for 5G. Coordinated multi-point (CoMP) is identified as a promising interference mitigation technique where multiple base stations (BS) can cooperate for joint transmission/reception by exchanging user/control data and perform joint signal processing to mitigate inter-cell interference and even exploit it as a useful signal. CoMP is already a key feature of long term evolution-advanced (LTE-A) and envisioned as an essential function for 5G. However, CoMP cannot be realized for the whole network due to its computational complexity, synchronization requirement between coordinating BSs and high backhaul capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. This PhD thesis aims to investigate optimum dynamic CoMP clustering solutions in 5G and beyond wireless networks with massive small cell (SC) deployment. Truly self-organised CoMP clustering algorithms are investigated, aiming to improve much needed spectral efficiency and other network objectives especially load balancing in future wireless networks. Low complexity, scalable, stable and efficient CoMP clustering algorithms are designed to jointly optimize spectral efficiency, load balancing and limited backhaul availability. Firstly, we provide a self organizing, load aware, user-centric CoMP clustering algorithm in a control and data plane separation architecture (CDSA) proposed for 5G to maximize spectral efficiency and improve load balancing. We introduce a novel re-clustering algorithm for user equipment (UE) served by highly loaded cells and show that unsatisfied UEs due to high load can be significantly reduced with minimal impact on spectral efficiency. Clustering with load balancing algorithm exploits the capacity gain from increase in cluster size and also the traffic shift from highly loaded cells to lightly loaded neighbours. Secondly, we develop a novel, low complexity, stable, network-centric clustering model to jointly optimize load balancing and spectral efficiency objectives and tackle the complexity and scalability issues of user-centric clustering. We show that our clustering model provide high spectral efficiency in low-load scenario and better load distribution in high-load scenario resulting in lower number of unsatisfied users while keeping spectral efficiency at comparably high levels. Unsatisfied UEs due to high load are reduced by $68.5\%$ with our algorithm when compared to greedy clustering model. In this context, the unique contribution of this work that it is the first attempt to fill the gap in literature for multi-objective, network-centric CoMP clustering, jointly optimizing load balancing and spectral efficiency. Thirdly, we design a novel multi-objective CoMP clustering algorithm to include backhaul-load awareness and tackle one of the biggest challenges for the realization of CoMP in future networks i.e. the demand for high backhaul bandwidth and very low latency. We fill the gap in literature as the first attempt to design a clustering algorithm to jointly optimize backhaul/radio access load and spectral efficiency and analyze the trade-off between them. We employ 2 novel coalitional game theoretic clustering methods, 1-a novel merge/split/transfer coalitional game theoretic clustering algorithm to form backhaul and load aware BS clusters where spectral efficiency is still kept at high level, 2-a novel user transfer game model to move users between clusters to improve load balancing further. Stability and complexity analysis is provided and simulation results are presented to show the performance of the proposed method under different backhaul availability scenarios. We show that average system throughout is increased by 49.9% with our backhaul-load aware model in high load scenario when compared to a greedy model. Finally, we provide an operator's perspective on deployment of CoMP. Firstly, we present the main motivation and benefits of CoMP from an operator's viewpoint. Next, we present operational requirements for CoMP implementation and discuss practical considerations and challenges of such deployment. Possible solutions for these experienced challenges are reviewed. We then present initial results from a UL CoMP trial and discuss changes in key network performance indicators (KPI) during the trial. Additionally, we propose further improvements to the trialed CoMP scheme for better potential gains and give our perspective on how CoMP will fit into the future wireless networks

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

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 over-load 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 over-load 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

Coordinated multi-point clustering schemes: a survey

Mobile data traffic grew by 74% in 2015 and it’s expected to grow 8-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating intercell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity and additional backhaul limitation. Hence small CoMP clusters will need to be formed in the network. This article surveys the stateof- the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organisation as an important concept for effective CoMP clustering to maximise CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organisation and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering.We present a future outlook on the utilisation of Big Data in cellular context to support proactive CoMP clustering based on prediction modelling. Finally we conclude this paper with a summary of lessons learnt in this field. This article aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks

Determining How SON (Self Organising Network) Solutions Can Help to Ensure QoS and QoE During the Transition to LTE

No abstract available

Determining How SON (Self Organising Network) Solutions Can Help to Ensure QoS and QoE During the Transition to LTE

No abstract available

Big Data Empowered Self Organization For Split Plane 5G Cellular Networks

No abstract available