Efficient handover mechanism for radio access network slicing by exploiting distributed learning

Network slicing is identified as a fundamental architectural technology for future mobile networks since it can logically separate networks into multiple slices and provide tailored quality of service (QoS). However, the introduction of network slicing into radio access networks (RAN) can greatly increase user handover complexity in cellular networks. Specifically, both physical resource constraints on base stations (BSs) and logical connection constraints on network slices (NSs) should be considered when making a handover decision. Moreover, various service types call for an intelligent handover scheme to guarantee the diversified QoS requirements. As such, in this paper, a multi-agent reinforcement LEarning based Smart handover Scheme, named LESS, is proposed, with the purpose of minimizing handover cost while maintaining user QoS. Due to the large action space introduced by multiple users and the data sparsity caused by user mobility, conventional reinforcement learning algorithms cannot be applied directly. To solve these difficulties, LESS exploits the unique characteristics of slicing in designing two algorithms: 1) LESS-DL, a distributed Q-learning algorithm to make handover decisions with reduced action space but without compromising handover performance; 2) LESS-QVU, a modified Q-value update algorithm which exploits slice traffic similarity to improve the accuracy of Q-value evaluation with limited data. Thus, LESS uses LESS-DL to choose the target BS and NS when a handover occurs, while Q-values are updated by using LESS-QVU. The convergence of LESS is theoretically proved in this paper. Simulation results show that LESS can significantly improve network performance. In more detail, the number of handovers, handover cost and outage probability are reduced by around 50%, 65%, and 45%, respectively, when compared with traditional methods

Location Privacy-Preserving Strategies for Secondary Spectrum Use

The scarcity of wireless spectrum resources and the overwhelming demand for wireless broadband resources have prompted industry, government agencies and academia within the wireless communities to develop and come up with effective solutions that can make additional spectrum available for broadband data. As part of these ongoing efforts, cognitive radio networks (CRNs) have emerged as an essential technology for enabling and promoting dynamic spectrum access and sharing, a paradigm primarily aimed at addressing the spectrum scarcity and shortage challenges by permitting and enabling unlicensed or secondary users (SUs) to freely search, locate and exploit unused licensed spectrum opportunities. Despite their great potentials for improving spectrum utilization efficiency and for addressing the spectrum shortage problem, CRNs suffer from serious location privacy issues, which essentially tend to disclose the location information of the SUs to other system entities during their usage of these open spectrum opportunities. Knowing that their whereabouts may be exposed, SUs can be discouraged from joining and participating in the CRNs, potentially hindering the adoption and deployment of this technology. In this thesis, we propose frameworks that are suitable for CRNs, but also preserve the location privacy information of these SU s. More specifically, 1. We propose location privacy-preserving protocols that protect the location privacy of SUs in cooperative sensing-based CRNs while allowing the SUs to perform their spectrum sensing tasks reliably and effectively. Our proposed protocols allow also the detection of malicious user activities through the adoption of reputation mechanisms. 2. We propose location privacy-preserving approaches that provide information-theoretic privacy to SU s’ location in database-driven CRNs through the exploitation of the structured nature of spectrum databases and the fact that database-driven CRNs, by design, rely on multiple spectrum databases. 3. We propose a trustworthy framework for new generation of spectrum access systems in the 3.5 GHz band that not only protects SUs’ privacy, but also ensures that they comply with the unique system requirements, while allowing the detection of misbehaving users

Bootstrapping Cognitive Radio Networks

Cognitive radio networks promise more efficient spectrum utilization by leveraging degrees of freedom and distributing data collection. The actual realization of these promises is challenged by distributed control, and incomplete, uncertain and possibly conflicting knowledge bases. We consider two problems in bootstrapping, evolving, and managing cognitive radio networks. The first is Link Rendezvous, or how separate radio nodes initially find each other in a spectrum band with many degrees of freedom, and little shared knowledge. The second is how radio nodes can negotiate for spectrum access with incomplete information. To address the first problem, we present our Frequency Parallel Blind Link Rendezvous algorithm. This approach, designed for recent generations of digital front-ends, implicitly shares vague information about spectrum occupancy early in the process, speeding the progress towards a solution. Furthermore, it operates in the frequency domain, facilitating a parallel channel rendezvous. Finally, it operates without a control channel and can rendezvous anywhere in the operating band. We present simulations and analysis on the false alarm rate for both a feature detector and a cross-correlation detector. We compare our results to the conventional frequency hopping sequence rendezvous techniques. To address the second problem, we model the network as a multi-agent system and negotiate by exchanging proposals, augmented with arguments. These arguments include information about priority status and the existence of other nodes. We show in a variety of network topologies that this process leads to solutions not otherwise apparent to individual nodes, and achieves superior network throughput, request satisfaction, and total number of connections, compared to our baselines. The agents independently formulate proposals based upon communication desires, evaluate these proposals based upon capacity constraints, create ariii guments in response to proposal rejections, and re-evaluate proposals based upon received arguments. We present our negotiation rules, messages, and protocol and demonstrate how they interoperate in a simulation environment

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks

DR9.3 Final report of the JRRM and ASM activities

Deliverable del projecte europeu NEWCOM++This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.Postprint (published version

Interference mitigation in cognitive femtocell networks

“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Femtocells have been introduced as a solution to poor indoor coverage in cellular communication which has hugely attracted network operators and stakeholders. However, femtocells are designed to co-exist alongside macrocells providing improved spatial frequency reuse and higher spectrum efficiency to name a few. Therefore, when deployed in the two-tier architecture with macrocells, it is necessary to mitigate the inherent co-tier and cross-tier interference. The integration of cognitive radio (CR) in femtocells introduces the ability of femtocells to dynamically adapt to varying network conditions through learning and reasoning. This research work focuses on the exploitation of cognitive radio in femtocells to mitigate the mutual interference caused in the two-tier architecture. The research work presents original contributions in mitigating interference in femtocells by introducing practical approaches which comprises a power control scheme where femtocells adaptively controls its transmit power levels to reduce the interference it causes in a network. This is especially useful since femtocells are user deployed as this seeks to mitigate interference based on their blind placement in an indoor environment. Hybrid interference mitigation schemes which combine power control and resource/scheduling are also implemented. In a joint threshold power based admittance and contention free resource allocation scheme, the mutual interference between a Femtocell Access Point (FAP) and close-by User Equipments (UE) is mitigated based on admittance. Also, a hybrid scheme where FAPs opportunistically use Resource Blocks (RB) of Macrocell User Equipments (MUE) based on its traffic load use is also employed. Simulation analysis present improvements when these schemes are applied with emphasis in Long Term Evolution (LTE) networks especially in terms of Signal to Interference plus Noise Ratio (SINR)