The present expansions in size and complexity of LTE networks is hindering their performance and their reliability. This hindrance is manifested in deteriorating performance in the User Equipment’s throughput and latency as a consequence to deteriorating the E-node B downlink throughput. This is leading to the need of smart E Node Base with various capabilities adapting to the changing communication environment. The proposed work aims at developing Self Organization (SO) techniques and... Show moreThe present expansions in size and complexity of LTE networks is hindering their performance and their reliability. This hindrance is manifested in deteriorating performance in the User Equipment’s throughput and latency as a consequence to deteriorating the E-node B downlink throughput. This is leading to the need of smart E Node Base with various capabilities adapting to the changing communication environment. The proposed work aims at developing Self Organization (SO) techniques and frameworks for LTE networks at the Resource Blocks (RB) scheduling management level. After reviewing the existing literature on Self Organization techniques and scheduling strategies that have been recently implemented in other wireless networks, we identify several contrasting needs that can jointly be addressed. The deployment of the introduced algorithms in the communication network is expected to lead to improved and upgraded overall network performance. The main feature of the LTE networks family is the feed-back that the cell receives from the users. The feedback includes the down link channel assessment based on the User Equipment (UE) measure Channel Quality Indicator (CQI) in the last Transmission Time Interval (TTI). This feed-back should be the main decision factor in allocating Resource Blocks (RBs) among users. The challenge is how could one maps the users’ data onto the RBs based on the CQI. The Thesis advances two approaches towards that end:- the allocation among the current users for the next TTI should be mapped, consistent with historical feed-back CQI received from users over prior transmission durations. This approach also aims at offering a solution to the bottle-neck capacity issue in the scheduling of LTE networks. To that end, we present an implementation of a modified Self Organizing Map (SOM) algorithm for mapping incoming data into RBs. Such an implementation can handle the collective cell enabling our cell to become smarter. The criteria in measuring the E-node Base performance include throughput, fairness and the trade-off between these attributes.- Another promising and complementary approach is to tailor Recurrent Neural Networks (RNNs) to implement optimal dynamic mappings of the Resource Blocks (RBs) in response to the history sequence of the Channel Quality Indicator CQI feedback. RNNs can successfully build its own internal state over the entire training CQI sequence and consequently make the prediction more viable. With this dynamic mapping technique, the prediction will be more accurate to changing time-varying channel environments. Overall, the collective cell management would become more intelligent and would be adaptable to changing environments. Consequently, a significant performance improvement can be achieved at lower cost. Moreover, a general tunability of the scheduling system becomes possible which would incorporate a trade-off between system complexity and QoS. Show less
