1,093 research outputs found

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

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    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

    Channel Capacity Maximization using NQHN Approach at Heterogeneous Network

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    In present scenario, the high speed data transmission services has pushed limits for wireless communication network capacity, at same time multimedia transmission in real-time needs provision of QoS, therefore the network capacity and small cell coverage has comes with lots of challenges. Improving the channel capacity and coverage area within the available bandwidth is necessary to provide better QoS to users, and improved channel capacity for the FCUs and MCUs in network. In this paper, we are proposing an NQHN approach that incorporate with efficient power allocation, improving the channel capacity by optimized traffic scheduling process in a small cell HetNets scenario. This work efficiently handle the interference with maintaining the user QoS and the implemented power controller uses HeNB power as per the real time based approach for macro-cell and femto-cell. Moreover, we consider the real traffic scenario to check the performance of our proposed approach with respect to existing algorith

    Performance Enhancing of Heterogeneous Network through Optimisation and Machine Learning Techniques

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    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

    Content Placement in Cache-Enabled Sub-6 GHz and Millimeter-Wave Multi-antenna Dense Small Cell Networks

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    This paper studies the performance of cache-enabled dense small cell networks consisting of multi-antenna sub-6 GHz and millimeter-wave base stations. Different from the existing works which only consider a single antenna at each base station, the optimal content placement is unknown when the base stations have multiple antennas. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mmWave frequencies. The maximization of the successful content delivery probability is a challenging problem. To tackle it, we first propose a constrained cross-entropy algorithm which achieves the near-optimal solution with moderate complexity. We then develop another simple yet effective heuristic probabilistic content placement scheme, termed two-stair algorithm, which strikes a balance between caching the most popular contents and achieving content diversity. Numerical results demonstrate the superior performance of the constrained cross-entropy method and that the two-stair algorithm yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and mmWave systems reveal an interesting tradeoff between caching capacity and density for the mmWave system to achieve similar performance as the sub-6 GHz system.Comment: 14 pages; Accepted to appear in IEEE Transactions on Wireless Communication
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