Energy-Efficient Task Offloading for Semantic-Aware Networks

The limited computation capacity of user equipments restricts the local implementation of computation-intense applications. Edge computing, especially the edge intelligence system enables local users to offload the computation tasks to the edge servers for reducing the computational energy consumption of user equipments and fast task execution. However, the limited bandwidth of upstream channels may increase the task transmission latency and affect the computation offloading performance. To overcome the challenge of the limited resource of wireless communications, we adopt a semantic-aware task offloading system, where the semantic information of tasks are extracted and offloaded to the edge servers. Furthermore, a proximal policy optimization based multi-agent reinforcement learning algorithm (MAPPO) is proposed to coordinate the resource of wireless communications and the computation, so that the resource management can be performed distributedly and the computational complexity of the online algorithm can be reduced.Comment: Have been accepted by IEEE ICC 202

Compressive Sensing Over TV White Space in Wideband Cognitive Radio

PhDSpectrum scarcity is an important challenge faced by high-speed wireless communications. Meanwhile, caused by current spectrum assignment policy, a large portion of spectrum is underutilized. Motivated by this, cognitive radio (CR) has emerged as one of the most promising candidate solutions to improve spectrum utilization, by allowing secondary users (SUs) to opportunistically access the temporarily unused spectrum, without introducing harmful interference to primary users. Moreover, opening of TV white space (TVWS) gives us the con dence to enable CR for TVWS spectrum. A crucial requirement in CR networks (CRNs) is wideband spectrum sensing, in which SUs should detect spectral opportunities across a wide frequency range. However, wideband spectrum sensing could lead to una ordably high sampling rates at energy-constrained SUs. Compressive sensing (CS) was developed to overcome this issue, which enables sub-Nyquist sampling by exploiting sparse property. As the spectrum utilization is low, spectral signals exhibit a natural sparsity in frequency domain, which motivates the promising application of CS in wideband CRNs. This thesis proposes several e ective algorithms for invoking CS in wideband CRNs. Speci cally, a robust compressive spectrum sensing algorithm is proposed for reducing computational complexity of signal recovery. Additionally, a low-complexity algorithm is designed, in which original signals are recovered with fewer measurements, as geolocation database is invoked to provide prior information. Moreover, security enhancement issue of CRNs is addressed by proposing a malicious user detection algorithm, in which data corrupted by malicious users are removed during the process of matrix completion (MC). One key spotlight feature of this thesis is that both real-world signals and simulated signals over TVWS are invoked for evaluating network performance. Besides invoking CS and MC to reduce energy consumption, each SU is supposed to harvest energy from radio frequency. The proposed algorithm is capable of o ering higher throughput by performing signal recovery at a remote fusion center

Capacity Analysis of Asymmetric Multi-Antenna Relay Systems Using Free Probability Theory

Random matrix theory (RMT) has been used to derive the asymptotic capacity of multiple-input-multiple-output (MIMO) channels by approximating the asymptotic eigenvalue distributions (AEDs) of the associated channel matrices. A novel methodology is introduced which enables the computation of the asymptotic capacity for a generalised system in which two relays cooperate to facilitate communication between two remote devices. It is computationally demanding to calculate this capacity using RMT when nodes are equipped with large-scale antenna arrays, and impossible in the case where asymmetry exists between channels within the system. This is because deriving the capacity across the combined channels from the relays to the receiver involves polynomials in large and non-commutative random matrix variables. This paper uses free probability theory (FPT) as an efficient alternative tool for analysis in these circumstances. The method described can be applied with no additional complexity for arbitrarily large antenna arrays. The minimum SNR required to achieve a given asymptotic capacity is computed and the simulation results verify the accuracy of the FPT approach

Meta Federated Reinforcement Learning for Distributed Resource Allocation

In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper explores a distributed resource allocation method that aims to maximize energy efficiency (EE) while ensuring the quality of service (QoS) for users. Specifically, in order to address wireless channel conditions, we propose a robust meta federated reinforcement learning (\textit{MFRL}) framework that allows local users to optimize transmit power and assign channels using locally trained neural network models, so as to offload computational burden from the cloud server to the local users, reducing transmission overhead associated with local channel state information. The BS performs the meta learning procedure to initialize a general global model, enabling rapid adaptation to different environments with improved EE performance. The federated learning technique, based on decentralized reinforcement learning, promotes collaboration and mutual benefits among users. Analysis and numerical results demonstrate that the proposed \textit{MFRL} framework accelerates the reinforcement learning process, decreases transmission overhead, and offloads computation, while outperforming the conventional decentralized reinforcement learning algorithm in terms of convergence speed and EE performance across various scenarios.Comment: Submitted to TW

Compression Ratio Learning and Semantic Communications for Video Imaging

Camera sensors have been widely used in intelligent robotic systems. Developing camera sensors with high sensing efficiency has always been important to reduce the power, memory, and other related resources. Inspired by recent success on programmable sensors and deep optic methods, we design a novel video compressed sensing system with spatially-variant compression ratios, which achieves higher imaging quality than the existing snapshot compressed imaging methods with the same sensing costs. In this article, we also investigate the data transmission methods for programmable sensors, where the performance of communication systems is evaluated by the reconstructed images or videos rather than the transmission of sensor data itself. Usually, different reconstruction algorithms are designed for applications in high dynamic range imaging, video compressive sensing, or motion debluring. This task-aware property inspires a semantic communication framework for programmable sensors. In this work, a policy-gradient based reinforcement learning method is introduced to achieve the explicit trade-off between the compression (or transmission) rate and the image distortion. Numerical results show the superiority of the proposed methods over existing baselines