On Improving Throughput of Multichannel ALOHA using Preamble-based Exploration

Machine-type communication (MTC) has been extensively studied to provide connectivity for devices and sensors in the Internet-of-Thing (IoT). Thanks to the sparse activity, random access, e.g., ALOHA, is employed for MTC to lower signaling overhead. In this paper, we propose to adopt exploration for multichannel ALOHA by transmitting preambles before transmitting data packets in MTC, and show that the maximum throughput can be improved by a factor of 2 - exp(-1) = 1.632, In the proposed approach, a base station (BS) needs to send the feedback information to active users to inform the numbers of transmitted preambles in multiple channels, which can be reliably estimated as in compressive random access. A steady-state analysis is also performed with fast retrial, which shows that the probability of packet collision becomes lower and, as a result, the delay outage probability is greatly reduced for a lightly loaded system. Simulation results also confirm the results from analysis.Comment: 10 pages, 7 figures, to appear in the Journal of Communications and Networks. arXiv admin note: substantial text overlap with arXiv:2001.1111

Energy Efficient Resource and Topology Management for Heterogeneous Cellular Networks

This thesis investigates how resource and topology management techniques can be applied to achieve energy efficiency while maintaining acceptable quality of service (QoS) in heterogeneous cellular networks comprising high power macrocells and dense deployment of low power small cells. Partially centralised resource and topology management algorithms involving the sharing of decision making responsibilities regarding resource utilization and activation or deactivation of small cells among macrocells, small cells and a central node are developed. Resource management techniques are proposed to enable mobile users to be served by resources of a few small cells. A topology management scheme is applied to switch off idle small cells and switch on sleeping cells in accordance with traffic load and QoS. Resource management techniques, when combined with the topology management technique, achieve significant energy efficiency. A choice restriction technique that restricts users to resources from only a subset of suitable small cells is proposed to mitigate interference and improve QoS. A good balance between energy efficiency and QoS is achieved through this approach. Furthermore, energy saving under different generations of small cell base stations is investigated to provide insights to guide the design of energy saving strategies and the enhancement of existing ones. Also, an online, adaptive energy efficient joint resource and topology management technique is developed to correct deteriorating QoS conditions automatically by using a novel confidence level strategy to estimate QoS and regulate decision making epochs at the central node. Finally, a novel linear search scheme is applied together with database records of performance metrics to select appropriate resource and topology management policies for different traffic loads. This approach achieves better balance between QoS and energy efficiency than previous schemes proposed in the literature