In this context, an optimization problem is formulated to optimize the RIS configuration, the transmit power of IoTDs and their clustering policy. On the other hand, the inner problem that determines the RIS configuration is solved through semi-definite relaxation (SDR).ĭue to the limitations of OMA techniques in terms of the number of served IoTDs and the spectral efficiency, the focus of this dissertation shifts to explore non-orthogonal multiple access (NOMA) scheme towards achieving the goal of minimizing the AoI in an uplink setting. To do so, the traffic stream scheduling is modeled as a Markov Decision Process (MDP) and Proximal Policy Optimization (PPO) is invoked to solve it. Owing to the stochastic nature of packet arrivals, a deep reinforcement learning (DRL) solution is employed to solve the outer problem. To evade the high coupling of the invoked optimization variables, the bi-level optimization technique is utilized, where the original problem is decomposed into an outer traffic stream scheduling problem and an inner RIS phase-shift matrix problem. The resulting problem is a mixed integer non-convex optimization problem. A joint user scheduling and phase-shift matrix (passive beamforming) optimization problem is formulated with the objective of minimizing the expected sum AoI of the coexisting multiple traffic streams. The considered multiple access technique is frequency division multiple access (FDMA), which is an orthogonal multiple access (OMA) technique. First, a wireless network consisting of a base station (BS) that is forwarding information updates of multiple real-time traffic streams to their destinations is studied. Towards addressing this challenge, reconfigurable intelligent surface (RIS) is leveraged to mitigate the propagation-induced impairments of the wireless environment and enhance the quality of wireless links to preserve the information freshness. In this context, an optimization problem is setup to determine the optimal scheduling policy with the goal of minimizing the expected sum AoI of multiple IoTDs, while considering the combined impact of unreliable channel conditions and random packet arrivals.Īnother acute challenge is the high randomness and uncontrollable behaviour of wireless communication environments, which may severely impede the timely and reliable delivery of information updates. In fact, MEC offers an expedited computation of resource-intensive tasks, which, if processed locally at the IoTDs, may experience excessive computational latency. To address this challenge, the first aim of this dissertation is to examine the capability of multi-access edge computing (MEC) towards minimizing the AoI.
In reality, the limited energy and computing resources of IoT devices (IoTDs) is a significant challenge towards realizing the timely delivery of information updates. AoI offers a rigorous way to quantify the information freshness as compared to other performance metrics and is deemed suitable for real-time IoT applications. Recently, information freshness has been investigated through defining a new performance metric termed as Age of Information (AoI). The conventional performance metrics such as delay and latency may not fully characterize the freshness of information for time-critical IoT applications. Out-dated or stale information updates are highly undesirable for these applications as they may call forth unreliable or erroneous decisions. These applications possess stringent requirements of fresh and timely information updates to make critical decisions. Such transformation will give rise to a wide range of propitious Internet-of-Things (IoT) applications such as intelligent transportation systems (ITS), tactile internet, augmented/virtual reality, industry 4.0, etc. Next-generation wireless networks (Beyond 5G, 6G) aim to provide tremendous improvements over previous generations by promising a massive connectivity, ultra-reliable and low-latency communications, and soaring broadband speeds. Once accepted, the candidate presents the thesis orally. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge. When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject.
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