الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
With the emerging of fifth generation (5G) wireless networks, industry and academia have been working on introducing powerful processing algorithms for massive data traffic, while providing ubiquitous connectivity and supporting plentiful ap- plications with different quality of service (QoS) demands. As the experimental deploying and running for 5G networks in some countries (e.g., USA, China, England, France, ...etc) has been initiated recently, more drastic improvements need to be made in cellular network architecture and related processing algo- rithms to meet the expectations of new generation investors and customers. Some
of these expectations (but not limited to) include massive data rates, huge capac- ity and coverage, diminished latency and improved quality of service (QoS). For- tunately, incorporating the Cloud Computing technology into the 5G radio access network (RAN) layer will contribute in making the management and processing of user data faster and more reliable than conventional 4G networks.
One of the issues that usually concerns cellular network operators is the cell- edge problem, where users existed at the edge of the cell, i.e., far away from the radio transceiver tower - known as base station (BS) - typically experience a low signal-to-interference-plus-noise-ratio (SINR), which leads to considerably low achievable throughputs and data rates. Increasing the BS transmit power in an attempt to improve the cell-edge users’ experience is typically limited by the anticipated of inter-cell-interference (ICI) among the adjacent cells. Hence, the need for smart solutions is crucial to deal with this issue. Another issue that network operators must handle is the traffic variations throughout the network area which change in a time-geometry manner based on the daytime (i.e., day or night) and cell/area type (i.e., business, residential, entertainment . . . etc). As a result, network cell resources maybe insufficient or dissipated according to the density of the area that each cell serves.
In this thesis, a new scheduling technique has been developed to increase the probability of assigning the available resource blocks (RBs) to the cell-edge users so that their achieved throughput would increase. A performance compari- son with state-of-the-art schedulers indicates that our proposed scheduling mech-
anism leads to a significant improvement in the average throughput for cell-edge users, with petty performance regression for cell-center users. On another front, a novel load-aware, network-centric, dynamic clustering (LANCDC) algorithm is proposed to increase the system performance in loaded cell-edge areas. To the best of our knowledge, LANCDC is the first network-centric clustering algo- rithm that adapts to spatio-temporal variations in user-density across the network. Our simulation experiments indicate that LANCDC outperforms state-of-the-art user-centric algorithms, especially in terms of user throughput and power sav- ings.