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Machine learning-based semantic communication is a promising enabler for future-generation wireless network systems such as 6G networks. In practice, effective semantic communication requires online training for unknown content. In highly mobile vehicular networks, however, reliable, and efficient model training becomes significantly challenging. The existing distributed learning approaches are also unable to effectively operate in highly dynamic vehicular semantic communication networks. To address these challenges, we propose a novel mobility-aware split-federated with transfer learning (MSFTL) framework based on vehicle task offloading scenarios in this paper. To enable adaptation to the complex vehicle semantic communication, the proposed framework divides the training of the model into four parts and uses the proposed new splitfederated learning. Furthermore, to improve training efficiency, model accuracy, and the ability to adapt in highly mobile environments, we also present a new transfer learning approach integrated into the proposed framework. Particularly, we propose a high-mobility training resource optimisation mechanism based on a Stackelberg game for MSFTL to further reduce training costs and adapt vehicle mobility scenarios. We also investigate the performance of the proposed schemes through extensive simulations. The results validate the proposed approach and indicate its superiority compared to the conventional learning frameworks for semantic communication in vehicular networks.

The sixth generation (6G) wireless communication is anticipated as a threedimensional (3D) network with full support of aerial edge and space edge. Moreover, semantic communication (SemCom) based on machine learning (ML) is also considered a significant enabling technology for 6G systems. Nevertheless, integrating SemCom into future 3D networks introduces emerging semantic coder updating requirements and new functional challenges considering, e.g. latency, energy, and privacy. Motivated by the above observations, in this thesis, the challenges of SemCom in various 6G edge-enable network architectures are investigated. Firstly, a terrestrial vehicular SemCom system is investigated for vehicle task offloading in vehicular networks (VNs). A novel mobility-aware split-federated with transfer learning (MSFTL) framework for SemCom coder updating is then proposed. Moreover, to incorporate vehicle mobility and training delays I propose a highmobility training resource optimisation mechanism based on a Stackelberg game for MSFTL. Secondly, an air-terrestrial SemCom system is proposed for energy-efficient implementation of SemCom in aerial-aided edge networks (AENs). An energyefficient game theoretic incentive mechanism (EGTIM) is proposed for improving the energy efficiency of the AEN for SemCom. To update SemCom coders accurately and efficiently in AENs, I further present a game theoretic efficient distributed learning (GEDL) framework based on the renewed EGTIM. Finally, a space-air-terrestrial (SAT) SemCom system is proposed for the computation offloading of resource-limited users in SAT networks. An adaptive pruning-split federated learning (PSFed) method for updating the SemCom coder is then proposed. Furthermore, the users processing computational tasks strategy in presented systems is formulated as an incomplete information mixed integer nonlinear programming (MINLP). A new computational task processing scheduling (CTPS) mechanism is also proposed based on the Rubinst

Semantic communication holds promise for integration into future wireless networks, offering a potential enhancement in network spectrum efficiency. However, implementing semantic communication in aerial-aided edge networks (AENs) introduces unique challenges. Within AENs, semantic communication strategically substitutes part of the communication load with the computation load, aiming to boost spectrum efficiency. This departure from traditional communication paradigms introduces novel challenges, particularly in terms of energy efficiency. Furthermore, by adding complexity, the use of a semantic coder based on machine learning (ML) in AENs encounters real-time updating challenges, further amplifying energy costs in these complex and energy-limited environments. To address these challenges, we propose an energy-efficient semantic communication system tailored for AENs. Our approach includes a mathematical analysis of semantic communication energy consumption within AENs. To enhance energy efficiency, we introduce an energy-efficient game-theoretic incentive mechanism (EGTIM) designed to optimize semantic transmission within AENs. Moreover, considering the accurate and energy-efficient updating of semantic coders in AENs, we present a game-theoretic efficient distributed learning (GEDL) framework, building upon the foundations of the renewed EGTIM. Simulation results validate the effectiveness of our proposed EGTIM in improving energy efficiency. Additionally, the presented GEDL framework exhibits remarkable performance by increasing model training accuracy and concurrently decreasing training energy consumption.

The combination of simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple (NOMA) brings the necessary full-space degrees of freedom and spatial multiplexing gains for the Internet of Things (IoT) networks. The inherent network heterogeneity and sharing of wireless channels may however increase the exposure of the information interactions to the third party. To address this issue, we propose a covert communication scheme in STAR-RIS assisted NOMA networks over Nakagami-m fading channels, where both downlink and uplink IoT scenarios are considered. Under the NOMA protocol with imperfect successive interference cancellation (SIC), an IoT access point interacts with two IoT users aided by a STAR-RIS without being detected by two wardens. In this scenario, the two IoT users are located on both sides of the STAR-RIS which adopts coherent phase shifting and operates according to the mode switching protocol. To evaluate the wardens’ detection performance, the Kullback-Leibler (KL) divergence is used. Furthermore, the cascaded channel gains of IoT users and wardens are respectively characterized as Gamma and complex Gaussian random variables. The closed-form expressions of the expectations of KL divergence and the interruption probabilities for downlink and uplink are derived. To further improve the performance, we formulate the effective covert rate maximization as the joint optimization problems of the transmit power and power allocation coefficient for downlink and uplink, subject to the constraints for covertness, reliability and power budget, which are respectively resolved analytically. Extensive simulation results indicate that the proposed scheme improves covertness compared with the benchmarks.

The low earth orbit (LEO) satellite-borne edge cloud (SEC) and machine learning (ML) based semantic communication (SemCom) are both enabling technologies for 6G systems facilitating computation offloading. Nevertheless, integrating SemCom into the SEC networks for user computation offloading introduces semantic coder updating requirements as well as additional semantic extraction costs. Offloading user computation in SEC networks via SemCom also results in new functional challenges considering, e.g., latency, energy, and privacy. In this paper, we present a novel SemCom-assisted SEC (SemCom-SEC) framework for computation offloading of resource-limited users. We then propose an adaptive pruning-split federated learning (PSFed) method for updating the semantic coder in SemCom-SEC. We further show that the proposed method guarantees training convergence speed and accuracy. This method also improves the privacy of the semantic coder while reducing training delay and energy consumption. In the case of trained semantic coders in service, for the users processing computational tasks, the main objective is to minimise the users’ delay and energy consumption, subject to sustaining users’ privacy and fairness amongst them. This problem is then formulated as an incomplete information mixed integer nonlinear programming (MINLP) problem. A new computational task processing scheduling (CTPS) mechanism is also proposed based on the Rubinstein bargaining game. Simulation results demonstrate the proposed PSFed and game theoretical CTPS mechanism outperforms the baseline solutions reducing delay and energy consumption while enhancing users’ privacy.

The deployment of distributed multi-controllers for Software-Defined Networking (SDN) architecture is an emerging solution to improve network scalability and management. However, the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low resilience. Thus, we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane failures. This fault-tolerance strategy has been applied to distributed SDN control architecture, which allows each switch to migrate to next controller to enhance network performance. In this paper, the Reliable and Dynamic Mapping-based Controller Placement problem (RDMCP) in distributed architecture is framed as an optimization problem to improve the system reliability quality, and availability. By considering the bound constraints, a heuristic state-of-the-art Controller Placement Problem (CPP) algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular controllers. The algorithm identifies the optimal controller location, minimum number of controllers, and the expected assignment costs after failure at the lowest effective cost. A metaheuristic Particle Swarm Optimization (PSO) algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness. The effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline algorithms. The findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

Future cellular systems are expected to use millimeter-wave (mm-Wave) frequency bands in addition to the existing microwave bands under 6 GHz. Severe weather conditions, including sand and dust storms (SDSs) and heavy rainfalls, challenge reliable communications over wireless links at those higher frequencies. In such conditions, besides frequency-dependent path-loss, radio signals experience additional attenuation. The SDS attenuation is related to visibility, receiver distance to the storm origin point, soil type, frequency, temperature and humidity. On the other hand, the rainfall attenuation is affected by rainfall rate, polarization, carrier frequency, temperature and raindrop size distribution. Leveraging on experimental measurements carried out in previous works, a novel unified mathematical framework is introduced in this paper to include SDS/rainfall-dependent attenuation in the performance evaluation of terrestrial wireless cellular networks in terms of coverage probability, bit error rate (BER) and achievable rate in the mm-Wave band. Extensive numerical results are presented to show the effects of the different SDS/rainfall parameters on performance, showing that the degradation due to SDS is generally higher than that due to rain and may cause a reduction of even six orders of magnitude in the average achievable bit rate when the frequency increases from 28 to 38 GHz.

Wi-Fi fingerprinting techniques are commonly used in Indoor Positioning Systems (IPS) as Wi-Fi signal is available in most indoor settings. In such systems, the position is estimated based on a matching algorithm between the enquiry points and the recorded fingerprint data. In this paper, our objective is to investigate and provide quantitative insight into the performance of various Nearest Neighbour (NN) algorithms. The NN algorithms such as KNN are also often employed in IPS. We extensively study the performance of several NN algorithms on a publicly available dataset, UJIIndoorLoc. Furthermore, we propose an improved version of the Weighted KNN algorithm. The proposed model outperforms the existing works on the UJIIndoorLoc dataset and achieves better results for the success rate and the mean positioning error.

In the Received Signal Strength (RSS) based Indoor Positioning Systems (IPS), the position of a receiver is estimated by comparing its RSS values with a fingerprint. The fingerprint is a dataset including the measured RSS values at pre-planned Reference Points (RP) for a set of reference transmitters. For a given RPs' spatial distribution, the RSS values are however affected by the intrinsic temporal and spatial uncertainties in the indoor wireless channel, hence constraining the positioning accuracy. To address this issue, we propose an algorithm to predesign the RSS values at each RP using Reconfigurable Intelligent Surface (RIS) technology. In the proposed method, the RIS reflection coefficients are obtained to maximize the difference between the RSS values between the RPs. The simulation results confirm that even with a relatively small number of RIS elements, the proposed method significantly improves the IPS efficiency.

This paper proposes the use of Reconfigurable Intelligent Surfaces (RIS) to customize the Received Signal Strength (RSS) distribution in an indoor environment for an Indoor Positioning System (IPS). RIS is a promising technology that can improve wireless communication by manipulating the propagation of electromagnetic waves. Considering RSS distribution as an essential factor that directly affects the accuracy and reliability of indoor location estimation, in this study, we propose a novel IPS architecture that incorporates RIS as an intermediate layer to control the signal propagation between the transmitter and receiver. Then, intelligently search for RIS elements that can directly and freely change the RSS value in any particular positioning of the environment. Consequently, any arbitrary distribution of the RSS and radio map can be customized in an indoor environment without requiring RIS to change its configuration in time slots in order to support multi-users. By designing a radio map of an indoor environment, RIS can improve the accuracy and reliability of IPS, making it a valuable technology for various applications such as indoor navigation, asset tracking, smart building applications, and indoor augmented reality.

This letter proposes a joint covert and secure transmission scheme in the non-orthogonal multiple access (NOMA) networks against internal eavesdropping and external monitoring. In the network, the strong user wiretaps the signals of the weak user while an external warden monitors the communication behaviors of the strong user. To address the issue, a random artificial noise (AN) based beamforming is adopted, which not only reduces the eavesdropping rate of the strong user but also confuses the warden. We consider the worst case, where the warden can minimize the detection error probability (DEP) by optimizing its detection threshold. Further, we characterize the closed-form expressions of the average minimum DEP of the warden, the connection outage probabilities of the NOMA users and the secrecy outage probability of the weak user under the imperfect successive interference cancellation (SIC). To improve the performance, an effective covert rate maximization problem is formulated, under the constraints of the covertness, reliability and secrecy, which is analytically solved. Numerical results validate the superiority of the proposed scheme.

Efficient connectivity in cellular-connected unmanned aerial vehicles (UAV)s is limited by scarcity of the radio spectrum and strong inter-cell interference (ICI). To address these issues, we propose an aerial-terrestrial network non-orthogonal multiple access (ATN-NOMA) scheme. In this proposed scheme, we pair the aerial user (AU) and terrestrial user (TU) in a NOMA setting to leverage their asymmetric channel gains and rate demands in downlink communications. In ATN-NOMA, the strong ICI issue at the AU receiver is further managed by an elevation-angle based user association, equipping the AU with an adjustable beamwidth directional antenna, and forming a beamforming among the coordinated terrestrial base stations (BS)s. We then obtain the optimal beamwidth and suboptimal power allocation so that the TUs' sum-rate is maximized subject to the AU's Quality-of-Service (QoS) requirement. The corresponding optimization problem is non-convex in which we exploit the structure of the problem and apply successive convex approximation (SCA) to obtain a suboptimal solution. We then derive the statistical properties, which consequently enable us to estimate the aggregated ICI. In cases where no interfering BSs have the same elevation angle as the coordinated BSs, we further approximate the AU's outage probability. We then compare the TUs' sum-rate and the outage probability of the ATN-NOMA with multiple existing schemes. Extensive simulation results show that our proposed ATN-NOMA scheme outperforms existing schemes by 52-91% in terms of the sum-rate, and its analytical outage probability can be as low as the order of 10-17. Furthermore, we show that the pairing of AU and TU in multi-cell networks remains beneficial, subject to effective mitigation of ICI.

Miners in a blockchain system are typically rewarded in two ways - through a fixed block reward, and by the transaction fees that are voluntarily offered by its users. As the available space inside a block is limited, users must compete against each other by submitting higher fees to obtain this limited resource. In this paper, we model blockchain transaction inclusion as a time-sensitive dynamic game, where users base their fees depending on both what their competitors in the network are offering, and by their own urgency of having their transactions approved. We then investigate the effect that mempool congestion (the aggregate size of transactions waiting to be confirmed) and different block sizes have on the fees users would be willing to pay. Our analysis concludes that miners have no rational reason to artificially limit the block size, which is in direct contrast with previous research findings. Instead, we find that increasing the block size in relation to a growing mempool both lowers the individual fees that users have to pay, and increases the total in fees collected, which raises not only the utility of the miners, but also of the regular users of the blockchain system.

The intelligent reflecting surface (IRS) technology is emerged as an enabling technology for beyond 5G systems and IoT networks in which the signal propagation is reconfigured to enhance wireless system performance. IRS consists of many passive elements and each reflecting the incident signal with a certain phase shift to collectively achieve the required beamforming. The IRS is to be a low profile and lightweight setting with a conformal geometry hence its position can be easily engineered to achieve certain performance enhancements. In the current literature, however, the flexibility in the IRS position is often overlooked since it is considered as a given fixture. We argue that optimizing the IRS position provides a new degree of freedom in the network design and enables extra performance gain. In this paper, we analytically characterize the optimal IRS s position to maximize the achievable system rate. We then obtain the optimal IRS positions for different IRS settings with fixed-height and variable-height and consider both cost-efficient equal phase shift IRS, and non-equal phase shift IRS that enables sophisticated beamforming. We further incorporate antenna directivity in our analysis and investigate its effect on the optimal IRS position in each case. Simulation results show that the provided optimal position yields higher performance than settings with random IRS locations. Our results provide significant practical insights on the network coverage design using the IRS.

Cyber-Physical Systems (CPS) are evolving and gradually building an ecosystem of smart homes, smart cities and automated systems. Indoor Positioning Systems (IPSs) play an essential part in providing location-based services to many demanded applications such as robots, UAVs, shopping malls, health care and more. Indoor positioning based on Wi-Fi is widely used to limit the complexity and cost of the Indoor Positioning System (IPS). This study aims to find an efficient design that makes IPS based on Wi-Fi fingerprinting more simple and scalable to enhance indoor positioning performance. Investigating the IPS system design in indoor settings tries to improve the positioning accuracy of Wi-Fi RSSI-based systems and reduce database-fingerprinting complexity by using cloud-computing architecture for efficient resource management and system scalability.

Reconfigurable Intelligent Surface (RIS) is a promising technology that has received much attention for indoor localization in recent years. This technology can control the propagation environment by passively changing reflected signals. Many RIS-based systems need complex operations for the RIS configuration. Also, fingerprint-based indoor localization methods need several access points to create different fingerprints for nearby areas. In this paper, we present a fingerprint-based indoor localization system using RIS. We conFigure RIS, change its configuration repeatedly, and create a vector for each fingerprint. Simulation results demonstrate that the proposed approach can accurately localize users with a single access point. Moreover, it is shown that some parameters such as the number of reference points, the number of RIS elements, and the number of RIS configurations significantly affect localization accuracy. In contrast, the results are independent of RIS location.