Matching Theory for Backhaul Management in Small Cell Networks with mmWave Capabilities

Designing cost-effective and scalable backhaul solutions is one of the main challenges for emerging wireless small cell networks (SCNs). In this regard, millimeter wave (mmW) communication technologies have recently emerged as an attractive solution to realize the vision of a high-speed and reliable wireless small cell backhaul network (SCBN). In this paper, a novel approach is proposed for managing the spectral resources of a heterogeneous SCBN that can exploit simultaneously mmW and conventional frequency bands via carrier aggregation. In particular, a new SCBN model is proposed in which small cell base stations (SCBSs) equipped with broadband fiber backhaul allocate their frequency resources to SCBSs with wireless backhaul, by using aggregated bands. One unique feature of the studied model is that it jointly accounts for both wireless channel characteristics and economic factors during resource allocation. The problem is then formulated as a one-to-many matching game and a distributed algorithm is proposed to find a stable outcome of the game. The convergence of the algorithm is proven and the properties of the resulting matching are studied. Simulation results show that under the constraints of wireless backhauling, the proposed approach achieves substantial performance gains, reaching up to $30 \%$ compared to a conventional best-effort approach.Comment: In Proc. of the IEEE International Conference on Communications (ICC), Mobile and Wireless Networks Symposium, London, UK, June 201

The 5G Cellular Backhaul Management Dilemma: To Cache or to Serve

With the introduction of caching capabilities into small cell networks (SCNs), new backaul management mechanisms need to be developed to prevent the predicted files that are downloaded by the at the small base stations (SBSs) to be cached from jeopardizing the urgent requests that need to be served via the backhaul. Moreover, these mechanisms must account for the heterogeneity of the backhaul that will be encompassing both wireless backhaul links at various frequency bands and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. Self-organizing reinforcement learning algorithm is proposed and proved to converge to a unique Boltzmann-Gibbs equilibrium which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.Comment: Accepted for publication at Transactions on Wireless Communication

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

User Association in 5G Networks: A Survey and an Outlook

26 pages; accepted to appear in IEEE Communications Surveys and Tutorial

On three use cases of multi-connectivity paradigm in emerging wireless networks

As envisioned by global network operators, the increasing trend of data traffic demand is expected to continue with exponential growth in the coming years. To cope with this rapid increase, significant efforts from the research community, industry and even regulators have been focused towards improving two main aspects of the wireless spectrum: (i) spectrum capacity and (ii) spectral efficiency. Concerning the spectrum capacity enhancement, the multi-connectivity paradigm has been seen to be fundamentally important to solve the capacity problem in the next generation networks. Multi-connectivity is a feature that allows wireless devices to establish and maintain multiple simultaneous connections across homogeneous or heterogeneous technologies. In this thesis, we focus on identifying the core issues in applying the multi-connectivity paradigm for different use cases and propose novel solutions to address them. Specifically, this thesis studies three use cases of the multi-connectivity paradigm. First, we study the uplink/downlink decoupling problem in 4G networks. More specifically, we focus on the user association problem in the decoupling context, which is considered challenging due to the conflicting objectives of different entities (e.g., mobile users and base stations) in the system. We use a combination of matching theory and stochastic geometry to reconcile competing objectives between users in the uplink/downlink directions and also from the perspective of base stations. Second, we tackle the spectrum aggregation problem for wireless backhauling links in unlicensed opportunistic shared spectrum bands, specifically, TV White Space (TVWS) spectrum. In relation to this, we present a DIY mobile network deployment model to accelerate the roll-out of high-end mobile services in rural and developing regions. As part of this model, we highlight the importance of low-cost and high-capacity backhaul infrastructure for which TVWS spectrum can be exploited. Building on that, we conduct a thorough analytical study to identify the characteristics of TVWS in rural areas. Our study sheds light on the nature of TVWS spectrum fragmentation for the backhauling use case, which in turn poses requirements for the design of spectrum aggregation systems for TVWS backhaul. Motivated by these findings, we design and implement WhiteHaul, a flexible platform for spectrum aggregation in TVWS. Three challenges have been tackled in this work. First, TVWS spectrum is fragmented in that the spectrum is available in non-contiguous manner. To fully utilize the available spectrum, multiple radios should be enabled to work simultaneously. However, all the radios have to share only a single antenna. The key challenge is to design a system architecture that is capable of achieving different aggregation configurations while avoiding the interference. Second, the heterogeneous nature of the available spectrum (i.e., in terms of bandwidth and link characteristics) requires a design of efficient traffic distribution algorithm that takes into account these factors. Third, TVWS is unlicensed opportunistic shared spectrum. Thus, the coordination mechanism between the two nodes of backhauling link is essential to enable seamless channel switching. Third, we study the integration of multiple radio access technologies (RATs) in the context of 4G/5G networks. More specifically, we study the potential gain of enabling the Multi-RAT integration at the Packet Data Convergence Protocol (PDCP) layer compared with doing it at the transport layer. In this work, we consider ultra-reliable low-latency communication (URLLC) as one of the motivating services. This work tackles the different challenges that arise from enabling the Multi-RAT integration at the PDCP layer, including, packet reordering and traffic scheduling