Priority Scheduling Algorithm for Traffic in a LTE-based Defence Mesh Network Incorporating Centralised Scheduling Architecture

Wireless mesh networks (WMN) are the networks of future and can operate on multiple protocols ranging from WiFi, WiMax to long term evolution (LTE). As a recent trend defence networks are incorporating off-the-shelf, state of the art commercial protocols to enhance the capability of their networks. LTE is one such commercially available protocol which is easy to deploy and provide high data rate which can be ideally implemented in WMN for defence networks. To enable these high data rate services LTE-based defence mesh networks (DMN) are the requirement of the day and future. However, LTE-based DMN are prone to congestion at times of active operations or full-fledged war. The congestion scenarios may lead to LTE packet loss. Hence, it is pertinent that these networks amalgamate information grooming algorithms to alleviate the throughput of the network in peak hour conditions. An efficient priority scheduling algorithm based on class of service prioritisation, data rate consumption and location of origin of traffic in the DMN is proposed. The simulations demonstrate that by incorporating the proposed priority scheduling algorithm, the overall packet loss of priority packets in the DMN reduces substantially

Capacity Enhancement Approaches for Long Term Evolution networks: Capacity Enhancement-Inspired Self-Organized Networking to Enhance Capacity and Fairness of Traffic in Long Term Evolution Networks by Utilising Dynamic Mobile Base-Stations

The long-term evolution (LTE) network has been proposed to provide better network capacity than the earlier 3G network. Driven by the market, the conventional LTE (3G) network standard could not achieve the expectations of the international mobile telecommunications advanced (IMT-Advanced) standard. To satisfy this gap, the LTE-Advanced was introduced with additional network functionalities to meet up with the IMT-Advanced Standard. In addition, due to the need to minimize operational expenditure (OPEX) and reduce human interventions, the wireless cellular networks are required to be self-aware, self-reconfigurable, self-adaptive and smart. An example of such network involves transceiver base stations (BTSs) within a self-organizing network (SON). Besides these great breakthroughs, the conventional LTE and LTE-Advanced networks have not been designed with the intelligence of scalable capacity output especially in sudden demographic changes, namely during events of football, malls, worship centres or during religious and cultural festivals. Since most of these events cannot be predicted, modern cellular networks must be scalable in terms of capacity and coverage in such unpredictable demographic surge. Thus, the use of dynamic BTSs is proposed to be used in modern and future cellular networks for crowd and demographic change managements. Dynamic BTSs are complements of the capability of SONs to search, determine and deploy less crowded/idle BTSs to densely crowded cells for scalable capacity management. The mobile BTSs will discover areas of dark coverages and fill-up the gap in terms of providing cellular services. The proposed network relieves the LTE network from overloading thus reducing packet loss, delay and improves fair load sharing. In order to trail the best (least) path, a bio-inspired optimization algorithm based on swarm-particle optimization is proposed over the dynamic BTS network. It uses the ant-colony optimization algorithm (ACOA) to find the least path. A comparison between an optimized path and the un-optimized path showed huge gain in terms of delay, fair load sharing and the percentage of packet loss