Rate Splitting for MIMO Wireless Networks: A Promising PHY-Layer Strategy for LTE Evolution

MIMO processing plays a central part towards the recent increase in spectral and energy efficiencies of wireless networks. MIMO has grown beyond the original point-to-point channel and nowadays refers to a diverse range of centralized and distributed deployments. The fundamental bottleneck towards enormous spectral and energy efficiency benefits in multiuser MIMO networks lies in a huge demand for accurate channel state information at the transmitter (CSIT). This has become increasingly difficult to satisfy due to the increasing number of antennas and access points in next generation wireless networks relying on dense heterogeneous networks and transmitters equipped with a large number of antennas. CSIT inaccuracy results in a multi-user interference problem that is the primary bottleneck of MIMO wireless networks. Looking backward, the problem has been to strive to apply techniques designed for perfect CSIT to scenarios with imperfect CSIT. In this paper, we depart from this conventional approach and introduce the readers to a promising strategy based on rate-splitting. Rate-splitting relies on the transmission of common and private messages and is shown to provide significant benefits in terms of spectral and energy efficiencies, reliability and CSI feedback overhead reduction over conventional strategies used in LTE-A and exclusively relying on private message transmissions. Open problems, impact on standard specifications and operational challenges are also discussed.Comment: accepted to IEEE Communication Magazine, special issue on LTE Evolutio

A review on green caching strategies for next generation communication networks

© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching

On the Load Balancing of Edge Computing Resources for On-Line Video Delivery

Online video broadcasting platforms are distributed, complex, cloud oriented, scalable, micro-service-based systems that are intended to provide over-the-top and live content to audience in scattered geographic locations. Due to the nature of cloud VM hosting costs, the subscribers are usually served under limited resources in order to minimize delivery budget. However, operations including transcoding require high-computational capacity and any disturbance in supplying requested demand might result in quality of experience (QoE) deterioration. For any online delivery deployment, understanding user's QoE plays a crucial role for rebalancing cloud resources. In this paper, a methodology for estimating QoE is provided for a scalable cloud-based online video platform. The model will provide an adeptness guideline regarding limited cloud resources and relate computational capacity, memory, transcoding and throughput capability, and finally latency competence of the cloud service to QoE. Scalability and efficiency of the system are optimized through reckoning sufficient number of VMs and containers to satisfy the user requests even on peak demand durations with minimum number of VMs. Both horizontal and vertical scaling strategies (including VM migration) are modeled to cover up availability and reliability of intermediate and edge content delivery network cache nodes

Proactive content caching in future generation communication networks: Energy and security considerations

The proliferation of hand-held devices and Internet of Things (IoT) applications has heightened demand for popular content download. A high volume of content streaming/downloading services during peak hours can cause network congestion. Proactive content caching has emerged as a prospective solution to tackle this congestion problem. In proactive content caching, data storage units are used to store popular content in helper nodes at the network edge. This contributes to a reduction of peak traffic load and network congestion. However, data storage units require additional energy, which offers a challenge to researchers that intend to reduce energy consumption up to 90% in next generation networks. This thesis presents proactive content caching techniques to reduce grid energy consumption by utilizing renewable energy sources to power-up data storage units in helper nodes. The integration of renewable energy sources with proactive caching is a significant challenge due to the intermittent nature of renewable energy sources and investment costs. In this thesis, this challenge is tackled by introducing strategies to determine the optimal time of the day for content caching and optimal scheduling of caching nodes. The proposed strategies consider not only the availability of renewable energy but also temporal changes in network trac to reduce associated energy costs. While proactive caching can facilitate the reduction of peak trac load and the integration of renewable energy, cached content objects at helper nodes are often more vulnerable to malicious attacks due to less stringent security at edge nodes. Potential content leakage can lead to catastrophic consequences, particularly for cache-equipped Industrial Internet of Things (IIoT) applications. In this thesis, the concept of \trusted caching nodes (TCNs) is introduced. TCNs cache popular content objects and provide security services to connected links. The proposed study optimally allocates TCNs and selects the most suitable content forwarding paths. Furthermore, a caching strategy is designed for mobile edge computing systems to support IoT task offloading. The strategy optimally assigns security resources to offloaded tasks while satisfying their individual requirements. However, security measures often contribute to overheads in terms of both energy consumption and delay. Consequently, in this thesis, caching techniques have been designed to investigate the trade-off between energy consumption and probable security breaches. Overall, this thesis contributes to the current literature by simultaneously investigating energy and security aspects of caching systems whilst introducing solutions to relevant research problems