Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

Performance Enhancing of Heterogeneous Network through Optimisation and Machine Learning Techniques

In the last two decades, by the benefit of advanced wireless technology, growing data service cause the explosive traffic demand, and it brings many new challenges to the network operators. In order to match the growing traffic demand, operators shall deploy new base stations to increase the total cellular network capacity. Meanwhile, a new type of low-power base stations are frequently deployed within the network, providing extra access points to subscribers. However, even the new base station can be operated in low power, the total network energy consumption is still increased proportional to the total number of base station, and considerable network energy consumption will become one of the main issues to the network operators. The way of reducing network energy consumption become crucial, especially in 5G when multiple antennas are deployed within one site. However, the base station cannot be always operated in low power because it will damage the network performance, and power can be only reduced in light-traffic period. Therefore, the way of balancing traffic demand and energy consumption will be come the main investigation direction in this thesis, and how to link the operated power of base station to the current traffic demand is investigated. In this thesis, algorithms and optimisations are utilised to reduce the network energy consumption and improve the network performance. To reduce the energy consumption in light-traffic period, base stations switch-off strategy is proposed in the first chapter. However, the network performance should be carefully estimated before the switch-off strategy is applied. The NP-hard energy efficiency optimisation problem is summarised, and it proposes the method that some of the base stations can be grouped together due to the limited interference from other Pico cells, reducing the complexity of the optimisation problem. Meanwhile, simulated annealing is proposed to obtain the optimal base stations combination to achieve optimal energy efficiency. By the optimisation algorithm, it can obtain the optimal PCs combination without scarifying the overall network throughput. The simulation results show that not only the energy consumption can be reduced but also the significant energy efficiency improvement can achieve by the switched-off strategy. The average energy efficiency improvement over thirty simulation is 17.06%. The second chapter will tackle the issue of how to raise the power of base stations after they are switched off. These base stations shall back to regular power level to prepare the incoming traffic. However, not all base stations shall be back to normal power due to the uneven traffic distribution. By analysing the information within the collected subscriber data, such as moving speed, direction, downlink and time, Naive Bayesian classifier will be utilised to obtain the user movement pattern and predict the future traffic distribution, and the system can know which base station will become the user's destination. The load adaptive power control is utilised to inform the corresponding base stations to increased the transmission power, base stations can prepare for the incoming traffic, avoiding the performance degradation. The simulation results show that the machine learning can accurately predict the destination of the subscriber, achieving average 90.8% accuracy among thirty simulation. The network energy can be saved without damage the network performance after the load adaptive function is applied, the average energy efficiency improvement among three scenarios is 4.3%, the improvement is significant. The significant improvement prove that the proposed machine learning and load adaptive power modification method can help the network reduce the energy consumption. In the last chapter, it will utilise cell range expansion to tackle the resources issue in cooperative base station in joint transmission, improving downlink performance and tackle the cell-edge problem. Due to the uneven traffic distribution, it will cause the insufficient resources problem in cooperative base station in joint transmission, and the system throughput will be influenced if cooperative base station executes joint transmission in high load. Therefore, the cell range expansion is utilised to solve the problem of unbalanced traffic between base station tier, and flow water algorithm is utilised to tackle the resources distribution issue during the traffic offloading. The simulation shows the NP-hard problem can be sufficiently solved by the flow water algorithm, and the downlink throughput gain can be obtained, it can obtain 26% gain in the M-P scenario, and the gain in P-M scenario is 24%. The result prove that the proposed method can provide significant gain to the subscriber without losing any total network throughput

Self-Organising Load Balancing for OFDMA Cellular Networks

In this thesis, self-organising load balancing is investigated to deal with the uneven load distribution in OFDMA based cellular networks. In single-hop cellular networks, a self- organising cluster-based cooperative load balancing (CCLB) scheme is proposed to overcome the ‘virtual partner’ and the ‘aggravating load’ problems confronted in the conventional mobility load balancing schemes. Theoretical analysis and simulation results show that the proposed scheme can effectively reduce the call blocking probability, the handover failure rate, and the hot-spot cell’s load. The proposed CCLB scheme consists of two stages: partner cell selection and traffic shifting. In the partner cell selection stage, a user-vote assisted clustering algorithm is proposed, which jointly considers the users’ channel condition and the surrounding cells’ load. This algorithm can select appropriate neighbouring cells as partners to construct the load balancing cluster, and deal with the ‘virtual partner’ problem. In the traffic shifting stage, a relative load response model (RLRM) is designed. RLRM coordinates multiple hot-spot cells’ shifting traffic towards their public partner, thus mitigating the ‘aggravating load’ problem of the public partner. Moreover, a traffic offloading optimisation algorithm is proposed to balance the hot-spot cell’s load within the load balancing cluster and to minimise its partners’ average call blocking probability. The CCLB scheme is modified to apply in multi-hop cellular networks with relays deployed. Both fixed relay and mobile user relay scenarios are considered. For fixed relay cellular networks, a relay-level user shifting algorithm is proposed. This algorithm jointly considers users’ channel condition and spectrum usage of fixed relay, in order to reduce the handover failure rate and deal with the ‘aggravating load’ problem of fixed relay. In the mobile user relay scenario, the user relaying assisted traffic shifting algorithm is proposed to improve the link quality of shifted edge users, which brings about an increase in the achievable rate of shifted edge users and decrease in the handover failure rate

D13.2 Techniques and performance analysis on energy- and bandwidth-efficient communications and networking

Deliverable D13.2 del projecte europeu NEWCOM#The report presents the status of the research work of the various Joint Research Activities (JRA) in WP1.3 and the results that were developed up to the second year of the project. For each activity there is a description, an illustration of the adherence to and relevance with the identified fundamental open issues, a short presentation of the main results, and a roadmap for the future joint research. In the Annex, for each JRA, the main technical details on specific scientific activities are described in detail.Peer ReviewedPostprint (published version

An analysis of social interaction between novice older adults when learning gesture-based skills through simple digital games

This paper reports three exploratory empirical studies with older adults that had little or no prior experience with interactive technologies. The participants were introduced to interactive technology by playing games on touchscreens, playing in pairs with the assistance of a mentor. We focus on two principle aspects, the peer-to-peer interaction during these sessions, and the role of the mentor in progressing the sessions. In the case of peer-to-peer interaction we looked for ways in which players supported each other during interaction to assess the role of peer interaction in this context. In the case of mentoring, we examined the efficacy of a minimalist approach where verbal encouragement, suggestions or (in the last resort) intervention are used to provide support to learners. The sessions showed that learners typically could play and learn basic manipulations independently after initial help and guidance from mentors. We also found that peer interaction, both in verbal and non-verbal communication and cooperative action was broadly a positive influence within sessions, suggesting that there is significant value in building confidence as well as in learning