The emergence of generative artificial intelligence (GenAI) has brought both challenges and opportunities for education. In this paper, we propose a GenAI-empowered, group-based authentic assessment for a Network Engineering course. This group assignment promotes challenge-based learning (CBL) and leverages GenAI to enhance students' creativity, critical thinking, collaboration, and technical problem-solving skills, while also improving students' GenAI literacy through fostering their ability to effectively engage with GenAI tools. The authenticity of this assignment is reflected in two folds: 1) students engage in a real-world engineering challenge, roleplaying as network engineers, and 2) they develop essential skills for co-creating solutions using GenAI tools, a key competency for future engineers. The group assignment is structured into five stages, each aligned with Bloom's Taxonomy to progressively develop cognitive skills from understanding foundational knowledge to synthesis, evaluation, and creation. To mitigate challenges such as overreliance on GenAI tools and varying levels of digital literacy, we provide guidance on the responsible and ethical use of GenAI, design reflective assessment tasks with constructive feedback, and establish clear marking criteria that emphasise both the learning process and the final outputs of the assignment. Initial evaluation and feedback from trials have highlighted the effectiveness of using GenAI tools in addressing complex engineering challenges and the value of collaborating in a real-world engineering context. This innovative approach demonstrates the potential of GenAIempowered authentic assessments to enhance learning experiences in technical fields like Network Engineering.

This paper investigates the impact of self-revision quizzes on student engagement and reflective learning in a Transnational Education (TNE) programme module. Designed around Kolb's Experiential Learning Cycle, the quizzes em-phasise four stages: concrete experience, reflective observation, abstract conceptualisation, and active experimentation, encour-aging students to identify knowledge gaps and apply feedback iteratively. Reflective learning supports metacognition and self-assessment, helping students enhance engagement and deepen their understanding of complex topics. Introduced in 2020/21, the self-revision quizzes provided immediate feedback with brief validation for correct answers and detailed explanations for incorrect ones, guiding students back to relevant teaching materials. Questions were based on recurring queries in QMPlus (Queen Mary's Virtual Learning Environment) and in-class discussions, targeting challenging areas of the module. Designed as formative assessments, the quizzes allowed multiple attempts to promote continuous revision. Over four years (2020/21 to 2023/24), quiz timing and reminders were adjusted to maximise participation. Results show that engagement varied between 25% and 57% per year, with the highest engagement linked to well-timed quizzes before assessments and multiple reminders. Feedback from the 2023/24 cohort revealed 55% of respondents found the quizzes very helpful for clarifying concepts, while 39% found them somewhat helpful but acknowledged the need for additional practice. Moreover, students who engaged with the quizzes consistently performed better in both final exams and the coursework. This study highlights the potential of self-revision quizzes to enhance engagement and prepare students for assessments such as exams, particularly in TNE contexts. It contributes to formative assessment research by showcasing how reflective learning tools can drive continuous learning. Plans are underway to integrate Generative AI for tailored feedback and quiz automation, reducing academic workload and expanding applicability to other modules.

Generative Artificial Intelligence (GenAI) is rapidly transforming higher education by automating complex processes, augmenting human capabilities, and fostering essential competencies for a global workforce. This study investigates GenAI's impact on educational practices within the Transnational Education (TNE) sector, focusing on its role in enhancing content creation, supporting personalised learning, and fostering critical thinking skills. Through qualitative focus group discussions with university educators and industry professionals, this research explores the dual challenges and opportunities GenAI presents, including ethical considerations, evolving student behaviours, and the need for innovative assessment methods. Educators emphasise GenAI's potential to improve student engagement and learning outcomes, while industry professionals highlight the critical importance of interdisciplinary skills and AI literacy. Drawing on both current literature and practical insights, the study calls for a balanced integration of GenAI, where it complements traditional teaching methods and prepares students for an AI-driven global workforce. Findings underscore the need for curriculum innovations and training programmes that equip TNE graduates with technical proficiency and the collaborative skills essential for effective human-AI interaction, ultimately shaping a workforce ready for the demands of AI-integrated industries.

This paper presents an AI-assisted approach that leverages Multimodal Large Language Models (MLLMs) to automate the generation of Multiple-Choice Questions (MCQs) for modules in engineering education. The system introduces a LOs extraction to MCQs generation pipeline, which extracts Learning Outcomes (LOs) from provided lecture notes and generates relevant MCQs with solutions and explanations based on the extracted LOs. By harnessing MLLMs' capabilities in vision and text comprehension, coupled with carefully crafted prompts from human educators, the tool efficiently produces context-relevant MCQs that can streamline teaching material development. The effectiveness of this AI-powered MCQ generation pipeline is investigated through experiments across a number of engineering modules with evaluations on the quality of the generated MCQs by human educators. The analysis of the evaluation results shows the AI tool's ability to generate MCQs that are well-aligned with LOs and exhibit strong contextual relevance, demonstrating the potential of AI-assisted approaches to enhance the efficiency of creating high-quality MCQs in engineering education. However, the variability in quality ratings across different aspects underscores the continued need for human expertise and oversight in the assessment design process. The findings provide useful insights into the capabilities and limitations of state-of-the-art multimodal language models in supporting assessment development in engineering education.

Over-the-air federated learning (OTA-FL) presents a promising distributed machine learning paradigm that improves the efficiency of local update aggregation by leveraging the superposition property of wireless multiple access channels (MACs). However, it faces significant security and privacy concerns that demand careful consideration. To address these threats associated with OTA-FL, we develop a secure and private over-the-air federated learning (SP-OTA-FL) framework, which can realize the secure and private aggregation for both OTA-FL with unbiased aggregation (UB-OTA-FL) and OTA-FL with biased aggregation (B-OTA-FL). In this framework, a subset of devices participate in training, while another subset functions as jammers, emitting jamming signals to enhance the security and privacy of the OTA-FL process. In particular, we measure the privacy leakage of users’ data using differential privacy (DP) and introduce an innovative application of mean squared error security (MSE-security) to evaluate the security of the OTA-FL system. We conduct convergence analyses for both convex and non-convex loss functions. Building on these analytical results, we separately formulate optimization problems for UB-OTA-FL and B-OTA-FL to enhance the learning performance of SP-OTA-FL by strategically optimizing the scheduling of training participants and jammers. The effectiveness of the proposed schemes is verified through simulations.

Personalised learning seeks to provide a tailored and highly effective learning experience, to maximize the unique potential of individual learners. However, despite the potential benefits, the implementation of personalised learning has not significantly materialized within the current structure of higher education institutions. In this paper, we propose a Guided Personalised Learning (GPL) model, specifically designed to facilitate effective interactions between educators and learners. The GPL model empowers learners to develop their tailored learning plans, while enabling educators to adapt their teaching and embrace student-centred pedagogy to address the diverse learning needs of students in the same classroom. We developed prototypes for the practical implementation of the GPL model in two undergraduate engineering courses and conducted initial evaluations of their effectiveness.

This innovative practice full paper provides an in-depth analysis of the Design and Build (D&B) module, which utilises cross-programme grouping method, within UK undergraduate engineering programmes, showcasing a unique approach to authentic assessment. It elucidates the module's significant impact on enhancing student employability and interdisciplinary collaboration, offering a novel model that integrates real-world challenges and teamwork into the academic curriculum. The distinctiveness of the D&B module lies in its branched structure, which not only reinforces technical and soft skills but also promotes innovation and practical application of knowledge. The primary aim of such modules is to enhance student employability through the development of technical expertise, problem-solving abilities, and teamwork skills. Additionally, it seeks to foster innovation and the practical application of theoretical knowledge, preparing graduates to meet the dynamic demands of the engineering industry. The study's findings reveal that the D&B module significantly contributes to student employability by enhancing technical competencies, soft skills, and the ability to engage in innovative problem-solving. Graduates from the programme demonstrate a high degree of readiness for the professional environment, showcasing the effectiveness of the module in bridging the gap between academic learning and industry requirements.

This research full paper proposes a novel data-driven approach for programme review that leverages module assessment data in an undergraduate engineering degree programme and graph theory concepts. The approach involves constructing a curriculum correlation graph, where nodes represent modules and edge weights are determined by correlation coefficients between assessment results of all modules in the engineering programme. Based on the curriculum correlation graph, graph-theoretic techniques and metrics, such as the minimum spanning tree, clustering coefficients and centrality measures, are employed to perform quantitative analyses, which evaluate the coherence of the programme's curriculum delivery. Furthermore, the approach facilitates a quantitative evaluation of the alignment between the programme's intended curriculum structure, as encapsulated in the designed curriculum graph, and its actual delivery, represented by the curriculum correlation graph. By comparing centrality measures between these two graphs, the approach highlights areas where the programme's curriculum delivery may deviate from its original design expectations, allowing targeted interventions to address potential misalignments. The proposed approach is applied to a UK-China transnational education undergraduate engineering degree programme. The analysis results demonstrate the effectiveness of the proposed data-driven approach in providing comprehensive and quantitative insights into the programme's curriculum design and delivery. By leveraging the power of graph theory and data analysis techniques, this approach offers a valuable tool for programme review, enabling programme teams in higher education institutions to identify both strengths and potential discrepancies in the alignment between a programme's curriculum delivery and its original design expectations, so that informed decision and targeted efforts can be made towards continuous improvement and enhancement of the academic degree programme.

Federated learning (FL) has been widely investigated in academic and industrial fields to resolve the issue of data isolation in the distributed Internet of Things (IoT) while maintaining privacy. However, challenges persist in ensuring adequate privacy and security during the aggregation process. In this article, we investigate device scheduling strategies that ensure the security and privacy of wireless FL. Specifically, we measure the privacy leakage of user data using differential privacy (DP) and assess the security level of the system through the mean-square error security (MSE-security). We commence by deriving the analytical results that reveal the impact of the device scheduling on privacy and security protection, as well as on the learning process. Drawing from these analytical findings, we propose three scheduling policies that can achieve secure aggregation of wireless FL under different cases of channel noise. In particular, we formulate an integer nonlinear fractional programming problem to improve the learning performance while guaranteeing privacy and security of wireless FL. We provide an insightful solution in the closed form to the optimization problem when the model has a high dimension. For the general case, we propose a secure and private aggregation (SPA) algorithm based on the branch-and-bound (BnB) method, which can obtain the optimal solution with low complexity. The effectiveness of the proposed schemes for device selection is validated through simulations.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete-aperture XL-MIMO using an electromagnetic (EM) channel model based on the near-field spherical wavefront. We analytically reveal the impact of the discrete aperture and polarization mismatch on the received power. We also complement the classical Fraunhofer distance based on the considered EM channel model. Our analytical results indicate that a limited part of the XL-array receives the majority of the signal power in the near field, which leads to a notion of visibility region (VR) of a user. Thus, we propose a VR detection algorithm and leverage the acquired VR information to devise a low-complexity symbol detection scheme. Furthermore, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the correctness of the analytical results and the effectiveness of the proposed algorithms. It reveals that our algorithms approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

This paper develops an optimal design for device scheduling, alignment coefficient, and aggregation rounds within a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system considering a constrained sum power budget. In DP-OTA-FedAvg, gradients are aligned using an alignment coefficient and then aggregated over the air, utilizing channel noise to ensure participant privacy. This study highlights two critical tradeoffs in aligned over-the-air federated learning (OTA-FL) systems with limited power and privacy budgets. Firstly, it reveals the tradeoff between the number of scheduled devices and the alignment coefficient. Secondly, it investigates the balance between aggregation distortion and local training error while adhering to the sum power constraint. Specifically, we measure privacy using differential privacy (DP) and perform convergence analyses for both convex and non-convex loss functions. These analyses provide insights into how device scheduling, the alignment coefficient, and the number of global aggregations affect both privacy preservation and the learning process. Building on these analytical results, we formulate an optimization problem aimed at minimizing the optimality gap of DP-OTA-FedAvg under power and privacy constraints. By specifying the number of aggregation rounds, we derive a closed-form expression describing the relationship between the alignment coefficient and the number of scheduled devices. We then tackle the problem through iterative optimization of scheduling and aggregation rounds. The effectiveness of the proposed policies is verified through simulations, and the performance advantage is particularly pronounced in scenarios where devices have poor channel conditions and limited sum-power budgets.

In this work, we propose low-complexity designs for XL-MIMO in the near-field with spatial non-stationarities. We first introduce a notion of visibility region (VR) and propose a VR detection algorithm. Then, we exploit the acquired VR information to design a low-complexity detection scheme for XL-MIMO systems. To further reduce the complexity, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Then, partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the effectiveness of the proposed algorithms which approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

In this paper, we propose a device scheduling scheme for differentially private over-the-air federated learning (DP-OTA-FL) systems, referred to as S-DPOTAFL, where the privacy of the participants is guaranteed by channel noise. In S-DPOTAFL, the gradients are aligned by the alignment coefficient and aggregated via over-the-air computation (AirComp). The scheme schedules the devices with better channel conditions in the training to avoid the problem that the alignment coefficient is limited by the device with the worst channel condition in the system. We conduct the privacy and convergence analysis to theo-retically demonstrate the impact of device scheduling on privacy protection and learning performance. To improve the learning accuracy, we formulate an optimization problem with the goal to minimize the training loss subjecting to privacy and transmit power constraints. Furthermore, we present the condition that the S-DPOTAFL performs better than the DP-OTA-FL without considering device scheduling (NoS-DPOTAFL). The effectiveness of the S-DPOTAFL is validated through simulations.

An adaptive next generation multiple access (NGMA) downlink scheme is provided, where non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) users are served with the same orthogonal time and frequency resource to address the energy constraints and massive connectivity issues of Internet-of-Things networks. Based on this scheme, the long-term power-constrained sum rate maximization problem is investigated, where beamforming, power allocation, and user clustering are jointly optimized, subject to a long-term total power constraint. To solve the formulated problem, a spatial correlation-based user clustering approach is proposed and a resource allocation algorithm is designed based on the trust region policy optimization (TRPO) algorithm, which demonstrates stable convergence under large learning rates. Numerical results verify that the sum rate of the proposed NGMA scheme outperforms the conventional NOMA and SDMA schemes. Moreover, the spatial correlation-based clustering algorithm achieves an increasing sum rate gain compared to the channel correlation-based baseline algorithm as the spatial correlation in the channel model increases.

To alleviate the negative impact of noise on wireless federated learning (FL), we propose a channel-weighted aggregation scheme of FL (CWA-FL), in which the parameter server (PS) makes aggregation of the gradients according to the channel conditions of devices. In the proposed scheme, the gradients are transmitted to the PS in an uncoded way through an orthogonal multiple access (OMA) channel, which can avoid the synchronization issue among devices faced by over-the-air FL. The convergence analysis of CWA-FL is conducted and the theoretical results show that the scheme can converge with the rate of$\mathcal {O} (\frac{1}{T})$. Simulation results show that the proposed scheme performs better than the equal-weighted aggregation scheme of FL (EWA-FL) and is more robust to noise.

A novel reconfigurable intelligent surfaces (RISs)-based transmission framework is proposed for downlink non-orthogonal multiple access (NOMA) networks. We propose a quality-of-service (QoS)-based clustering scheme to improve the resource efficiency and formulate a sum rate maximization problem by jointly optimizing the phase shift of the RIS and the power allocation at the base station (BS). A model-agnostic meta-learning (MAML)-based learning algorithm is proposed to solve the joint optimization problem with a fast convergence rate and low model complexity. Extensive simulation results demonstrate that the proposed QoS-based NOMA network achieves significantly higher transmission throughput compared to the conventional orthogonal multiple access (OMA) network. It can also be observed that substantial throughput gain can be achieved by integrating RISs in NOMA and OMA networks. Moreover, simulation results of the proposed QoS-based clustering method demonstrate observable throughput gain against the conventional channel condition-based schemes.

A fundamental challenge for millimeter wave (mmWave) communications lies in its sensitivity to the presence of blockages, which impact the connectivity of the communication links and ultimately the reliability of the entire network. In this paper, we are exploited to deal with the link outage issue caused by a reconfigurable intelligent surface (RIS)-aided mmWave communication system for enhancing the network reliability and connectivity in the presence of random blockages. To enhance the robustness of the beamforming in the presence of random blockages, we formulate a stochastic optimization problem with the aim of minimizing the outage probability. To tackle the proposed optimization problem, we introduce a low-complexity algorithm based on the stochastic majorization-minimization method, which learns sensible blockage patterns without searching for all combinations of potentially blocked links. Numerical results confirm the performance benefits of the proposed algorithm in terms of outage probability and effective data rate.

In this paper, we investigate the design of robust and secure transmission in intelligent reflecting surface (IRS) aided wireless communication systems. In particular, a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers, where the artificial noise (AN) is transmitted to enhance the security performance. Besides, we assume that the cascaded AP-IRS-user channels are imperfect due to the channel estimation error. To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model. To handle the resulting non-convex optimization problem, we first approximate the outage rate probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on alternating optimization, the penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power compared to other benchmark schemes.

Abstract Nowadays, people trade electricity through centralized companies or organizations which is vulnerable to cyber attacks and incapable of coping with increasing demands from stakeholders. In this paper, we propose a new Peer-to-Peer Electricity Blockchain Trading (P2PEBT) system based on the current charging and discharging schemes for electric vehicles (EV) in the smart grid to enable users to participate in the trading process. In order to cope with the current situation of the high volume of EV integration, the proof-of-Benefit (PoB) consensus primitives are proposed for P2PEBT to achieve demand response by providing incentives to balance local electricity demand in the novel blockchain system. PoB is implemented by executing the smart contracts on the Ethereum platform, and the process of achieving the maximal benefits is completed by submitting the transaction in the decentralized network. Security analysis shows that the P2PEBT system is able to manage a potential protection against up to a number of attacks. We demonstrate that the proposed system using the PoB consensus mechanism can achieve lower power fluctuation without requiring a third-party intermediary.

By activating blocked users and altering successive interference cancellation (SIC) sequences, reconfigurable intelligent surfaces (RISs) become promising for enhancing non-orthogonal multiple access (NOMA) systems. This work investigates the downlink performance of RIS-aided NOMA networks via stochastic geometry. We first introduce the unique path loss model for RIS reflecting channels. Then, we evaluate the angle distributions based on a Poisson cluster process (PCP) framework, which theoretically demonstrates that the angles of incidence and reflection are uniformly distributed. Lastly, we derive closed-form expressions for coverage probabilities of the paired NOMA users. Our results show that 1) RIS-aided NOMA networks perform better than the traditional NOMA networks; and 2) the SIC order in NOMA systems can be altered since RISs are able to change the channel gains of NOMA users.

Internet of Things (IoT) is one of the most cited terms within the communication research communities. Next generation wireless networks technologies are expected to have massive‐connections of tens of billions of devices. Such a huge number of devices raised a number of concerns in regards to how much accessible resources are available and what are the best technologies for managing those resources, all in order to avoid shutdowns/collapses in every means. In terms of wireless networks, and in regards to energy being the backbone of IoT devices, Low Power Wide Area Networks (LPWAN) technologies are considered to be a potential solution for IoT applications. In particular, this study reviews Long‐Range (LoRa) technology and advances in the literature of LoRaWAN protocol to date. Furthermore, it discusses the challenges in LoRaWAN and diverts the attention towards applying Ultra‐Dense Network concept on LPWAN.

A novel framework for dynamic 3-D deployment of unmanned aerial vehicle (UAV) in the aerial base station system (ABSS) that based on the machine learning algorithms is proposed. In the framework, the UAV is deployed as an aerial base station to serve a group of ground users and is placed based on the prediction of the users' mobility. The joint problem of prediction of users' track and 3-D deployment of the UAV is formulated for maximizing the sum transmit rate. A two-step approach is proposed for predicting the movement of users and for determining the dynamic 3-D placement of the UAV. Firstly, an echo state network (ESN) based prediction algorithm is utilized for predicting the future positions of users based on the real-world datasets collected from Twitter. Secondly, an iterative K-Means based algorithm is proposed for obtaining the optimal placement of UAV at each time slot based on the output of ESN model. Numerical results are illustrated for showing the superiority of the proposed algorithm over the prevalent algorithm on prediction tasks. The accuracy and efficiency of the proposed framework are also investigated. Additionally, compared with static placement of the UAV, the advantage of dynamic 3-D deployment is demonstrated.

The massive adoption of Electric Vehicles (EVs) requires the grid system to coordinate with a large number of energy transactions, where the current grid network poses vulnerability against the excessive power loads and attacks. The difficulty of an efficient charging/discharging control mechanism lies on the randomness of future events and scalability of the transaction platform. In this paper, a Proof-of-Benefit consensus mechanism with Online benefit generating (ONPoB) algorithm is proposed on the blockchain platform to handle the EV charging/discharging loads to flatten the overall power load fluctuation. It is demonstrated that all EVs can be charged and achieves a best-known competitive ratio of 2.39. The ONPoB consensus mechanism is approved to better accommodate the EV scenario compared with other mechanisms. And the ONPoB algorithm is able to substantially reduce the Power Fluctuation Level (PFL) in comparison with popular scheduling algorithms.

The emergence of blockchain technology brings opportunities for the transactional energy to minimize the time gap and cost in the trading process. This paper proposes a public power exchange service network for Electric Vehicles (EV) to charge and discharge from the power grid. An enhanced novel consensus mechanism Proof-of-Benefit (ePoB) is proposed to improve the protocol security and performance of the electricity exchange system. Furthermore, the benefit number generation algorithm for choosing the leader in the network guarantees the overall power grid network performance by minimizing the load variance. Through theoretical and experimental analysis, the public power exchange system with ePoB consensus protocol achieves higher scalability than Proof- of-Work (PoW) and Paxo-based or BFT-based consensus protocols. Also, it demonstrates that the consensus protocol is capable of withstanding the Sybil attack while achieving lower power load fluctuation level compared with the benchmark.

Internet of Things (IoT) is one of the most cited terms within the wireless communication research communities. Next generation wireless networks technologies are expected to have massive-connections of tens of billions of devices. In terms of wireless networks, and in regards to collisions and transmission delay drawbacks being critical challenges when deploying IoT devices, Low Power Wide Area Networks (LPWAN) technologies are considered to be a potential solution for IoT applications. In particular, this paper investigates the use of Long-Range (LoRa) technology for serving dense applications. Furthermore, it identifies a dense application and investigates the possibility of using LoRaWAN for such applications. This work proposes a priority scheduling technique based on unsupervised learning clustering algorithm (K-Means). The proposed technique shows a reduction of the collision rate, the transmission delay and enhancement of the throughput in comparison to conventional LoRaWAN networks and other optimisation techniques.

In today’s cyber world, assessing security threats before implementing smart grids is essential to identify and mitigate the risks. Loss Event Frequency (LEF) is a concept provided by the well-known Factor Analysis of Information Risk (FAIR) framework to assess and categorize the cyber threats into five classes, based on their severity. As the number of threats is increasing, it is possible that many threats might fall under the same LEF category, but FAIR cannot provide any further mechanism to rank them. In this paper, we propose a method to incorporate the FAIR’s LEF into Bayesian Network (BN) to derive the numerical assessments to rank the threat severity. The BN probabilistic relations are inferred from the FAIR look-up tables to reflect and conserve the FAIR appraisal. Our approach extends FAIR functionality by providing a more detailed ranking, allowing fuzzy inputs, enabling the illustration of input-output relations, and identifying the most influential element of a threat to improve the effectiveness of countermeasure investment. Such improvements are demonstrated by applying the method to assess cyber threats in a smart grid robustness research project (IRENE).

This paper studies the application of non-orthogonal multiple access (NOMA) to cache-enabled cloud radio access network (CRAN) with mixed multicast and unicast transmission. Users requesting the same content are grouped together and served with a cluster of remote radio heads (RRHs) using distributed beamforming. In addition, the user with better channel condition in each group is allowed to request an extra unicast content via the NOMA protocol. Each RRH has a local cache which enables it to acquire the requested contents either from the local cache or from the central processor via the fronthaul link. Taking the maximum fronthaul capacity into consideration, we investigate the subchannel (SC) allocation problem to both RRHs and multicast groups to improve the weighted network sum rate. The optimal solution requires exhaustive search, which become prohibitively complicated as the number of RRHs and groups increases. To tackle this problem effectively, we formulate this problem as a three-sided matching problem among SCs, RRHs and multicast groups, and propose a novel low-complexity matching algorithm. We prove mathematically that the proposed algorithm converges to a stable matching within limited number of iterations. Numerical results unveil that the proposed algorithm closely approaches the optimal solution and outperforms the conventional orthogonal multiple access (OMA)-based CRAN.

Fog computing, also referred to mobile edge computing (MEC), has been recognized as an effective technology to cope with the computation-intensive applications of mobile users. In energy harvesting (EH) enabled fog-computing- based radio access networks (F-RANs), green power can be utilized by fog-computing enabled access points (F-APs) to support the computation offloaded from the mobile users. The utilization of EH will minimize the average grid power consumption. However, intermittency and uneven distribution of the harvested energy may bring in dynamics of the offloading design and affect the delay processing. In this paper, we propose a delay-aware energy efficient computation offloading scheme for F-RANs with hybrid energy supplies. The optimization problem is formulated to minimize the consumption of the non-renewable grid energy under delay and networks constraints. Simulation results show that the proposed offloading scheme can reduce grid power consumption. Besides, the number of computation tasks can be completed within the delay provision.

In this paper, novel resource allocation design is investigated for NOMA-enhanced heterogeneous networks (Het-Nets), where small cell base stations (SBSs) are enabled to communicate with multiple small cell users (SCUs) via the NOMA protocol. The resource allocation problem with the aim of maximizing the sum rate of SCUs is formulated as a many-to-one matching game. Due to the existence of co-channel interference, this game is shown to belong to a class of matching games with peer effects. To solve this game, we propose a novel distributed algorithm where the SBSs and resource blocks (RBs) can interact to decide their desired allocation. The proposed algorithm is proved to converge to a two-sided exchange-stable matching with much lower complexity compared to the centralized method. Simulation results unveil that: 1) The proposed algorithm closely approaches the global optimal solution by around 92.5% within a limited number of iterations; and 2) The developed NOMA-enhanced HetNets scheme achieves a higher sum rate of SCUs compared to the traditional OMA-based HetNets scheme.

The adoption of Electric Vehicle (EV) can shave the peak load and flatten the load profile in an urban area. However, the uncontrolled patterns of simultaneous and randomised EV charging may increase the peak load and thus destabilise the power grid. To address this problem effectively, this paper proposes an optimal charging scheme with the objective of lowering the power fluctuation level. The charging scheme takes into account the uncertainty of EV's driver behaviour and EV charging demand model with minimal impacts on daily routine of EV users. Firstly, a power fluctuation level problem is formulated and a novel EV charging scheme based on Genetic Algorithm is further proposed to solve the problem. The simulation results have shown the robustness of the proposed scheme in lowering the power fluctuation level and the overall peak demand significantly in the grid system.

Energy harvesting is becoming an attractive option of energy supply for wireless networks as it can effectively reduce capital expenditure (CAPEX) and operational expenditure (OPEX). In this paper, an energy efficient radio resource optimization algorithm is proposed for a two-tier heterogeneous cloud radio access network (H-CRAN) where macro cells are empowered by conventional grid power and remote radio heads (RRH) are empowered by renewable energy sources. The resource allocation optimization is firstly formulated as a mixed integer programming problem, which is NP-hard. Therefore, an equivalent green power utilization maximization problem is formulated, and solved by Lagrange dual decomposition method. Numerical results show that the proposed algorithm can increase the utilization of the green power harvested from the renewable energy sources. This, in turn, leads to reduced grid power consumption compared to the baseline algorithms.

In this paper, a novel non-orthogonal multiple access (NOMA) enhanced device-to-device (D2D) communication scheme is considered. Our objective is to maximize the system sum rate by optimizing subchannel and power allocation. We propose a novel solution that jointly assigns subchannels to D2D groups and allocates power to receivers in each D2D group. For the subchannel assignment, a novel algorithm based on the many-to-one two-sided matching theory is proposed for obtaining a suboptimal solution. Since the power allocation problem is nonconvex, sequential convex programming is adopted to transform the original power allocation problem to a convex one. The power allocation vector is obtained by iteratively tightening the lower bound of the original power allocation problem until convergence. Numerical results illustrate that: 1) the proposed joint subchannel and power allocation algorithm are an effective approach for obtaining near-optimal performance with acceptable complexity and 2) the NOMA enhanced D2D communication scheme is capable of achieving promising gains in terms of network sum rate and the number of accessed users, compared to a traditional OMA-based D2D communication scheme.

In this paper, a novel resource allocation design is investigated for non-orthogonal multiple access (NOMA) enhanced heterogeneous networks (HetNets), where small cell base stations (SBSs) are capable of communicating with multiple small cell users (SCUs) via the NOMA protocol. With the aim of maximizing the sum rate of SCUs while taking the fairness issue into consideration, a joint problem of spectrum allocation and power control is formulated. In particular, the spectrum allocation problem is modeled as a many-to-one matching game with peer effects. We propose a novel algorithm where the SBSs and resource blocks interact to decide their desired allocation. The proposed algorithm is proved to converge to a two-sided exchange-stable matching. Furthermore, we introduce the concept of `exploration' into the matching game for further improving the SCUs' sum rate. The power control of each SBS is formulated as a non-convex problem, where the sequential convex programming is adopted to iteratively update the power allocation result by solving the approximate convex problem. The obtained solution is proved to satisfy the Karush-Kuhn-Tucker conditions. We unveil that: 1) the proposed algorithm closely approaches the optimal solution within a limited number of iterations; 2) the `exploration' action is capable of further enhancing the performance of the matching algorithm; and 3) the developed NOMA-enhanced HetNets achieve a higher SCUs' sum rate compared with the conventional OMA-based HetNets.

Restricted Access Window (RAW) has been introduced to IEEE 802.11ah MAC layer to decrease collision probability. However, the inappropriate application of RAW duration for diverse groups of devices would increase uplink energy consumption, delay and lower down the data rate. In this paper, we study a RAW optimization problem with a novel retransmission scheme that utilizes the next empty slot for retransmission in the uplink. The problem is formulated based on overall energy efficiency and delay of each RAW by applying probability theory and Markov Chain. To jointly optimize energy efficiency and delay, an energy-delay aware window control algorithm is proposed to adapt RAW size by estimating the number of time slots and internal slot duration in one RAW for different groups. The optimal solution is derived by applying Gradient Descent approach. Simulation results show that our proposed algorithm improves up to 113.3% energy efficiency and reduces 53.4% delay compared to the existing RAW.

In this paper, a novel non-orthogonal multiple access (NOMA)-based device-to-device (D2D) communications framework is proposed. A major novelty of the proposed framework is that it introduces the new concept of ``D2D group" which utilizes NOMA transmission, enabling one D2D transmitter to communicate with multiple D2D receivers simultaneously. Based on the considered framework, a resource allocation optimization problem is formulated, where multiple D2D groups are allowed to reuse the same subchannel. The objective of this work is to maximize the system sum rate by satisfying the signal-to-interference- plus-noise (SINR) constraints of both D2D and traditional cellular users. Note that the formulated problem is non-deterministic polynomial-time (NP) hard in nature, thus a novel resource allocation algorithm based on the many- to-one two-sided matching theory is proposed for obtaining a suboptimal solution. It is proved that the proposed algorithm converges to a stable state within limited number of iterations. Numerical results illustrate that: i) the proposed algorithm is an effective approach for obtaining near- optimal performance with acceptable complexity; and ii) the proposed NOMA-based D2D framework is capable of achieving promising gains over traditional orthogonal multiple access (OMA)-based D2D framework.

In this paper, we facilitate device-to-device (D2D) communications to provide relay assistance to cell-edge user equipments (UEs) with the objective of improving system throughput. We first formulate a joint problem of relay node selection which helps cell-edge UEs find the proper relay nodes, as well as spectrum allocation for D2D links to maximize the system throughput with interference constraints to both D2D and traditional cellular UEs. Furthermore, we propose a distributed algorithm adopting a two-level game model which consists of inner and outer levels to solve the formulated problem. In the inner level, we use the Stackelberg game to select relay nodes for cell-edge UEs, where the relay nodes act as the leaders and the cell-edge UEs act as the followers. In the outer level, the coalition formation game is used to allocate proper spectrum for the D2D links between cell-edge UEs and their relay nodes. The games do not proceed separately, but are dependent on each other, which improves the efficiency of the proposed game model. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in terms of system throughput.

Cooperative multiple-input-single-output (CMISO) scheme has been proposed to prolong the lifetime of cluster heads (CHs) in cluster-based Internet of Things (IoT) systems. However, the CMISO scheme introduces additional energy overhead to cooperative nodes (Coops) and further reduce the lifetime of these devices. In this paper, we first formulate the problem of cooperative coalition selection for CMISO scheme to prolong the average battery operating time among the whole network, and then propose to apply the quantum-inspired particle swarm optimization (QPSO) to select the optimum cooperative coalition. Simulation results proved that the QPSO algorithm outperforms particle swarm optimization (PSO) and quantum genetic algorithm (QGA).

Energy efficiency is a crucial challenge in cluster-based capillary networks for Internet of Things (IoT) systems, where the cluster heads (CHs) selection has great impact on the network performance. It is an optimization problem to find the optimum number of CHs as well as which devices are selected as CHs. In this paper, we formulate the clustering problem into the CHs selection procedure with the aim of maximizing the average network lifetime in every round. In particular, we propose a novel CHs selection scheme based on QPSO and investigate how effective it is to prolong network lifetime and reserve the overall battery capacity. The simulation results prove that the proposed QPSO outperforms other evolutionary algorithms and can improve the network lifetime by almost 10%.

Massive devices and various applications imposes new challenges for Machine-to-Machine (M2M) communications to enable Internet of Things (IoT). In this paper, we investigate a QoS-aware joint access control and duty cycle control problem for M2M communications to optimise the overall network performance, including energy efficiency, end-to-end delay, reliability, throughput and fairness. We first model a practical hybrid M2M communication network and measure the overall network performance through a cost function. Then, an optimisation problem is formulated to minimise the long-term aggregated network cost. Further more, we overcome the non-convexity of the cost function and mathematically derive the optimal access control. Finally, we propose a distributed access control followed by a reinforcement learning (RL) based duty cycle control which adapts to various network dynamics without priori network information. Simulation results show that, the proposed joint access control and duty cycle control minimise the network long-term aggregated cost, while achieving fairness among cluster heads with QoS differentiation.

Restricted Access Window (RAW) has been introduced for IEEE 802.11ah MAC layer to decrease collision probability. However, both the number of devices involved and duration of a RAW affect the transmission energy and overhead information. In this paper, we study the energy efficiency of the uplink communications of IEEE 802.11ah networks and propose an access window algorithm using probability theory to find an optimal number of devices contending in adaptive RAW size. We formulate the problem of RAW optimization based on the overall energy consumption of different transmission states and the data rate in one RAW. The optimal solution is derived by applying a Hill Climbing approach. Simulation results show that our proposed algorithm outperforms existing RAW on uplink energy efficiency.

In the heterogeneous networks (HetNets) powered by hybrid energy sources, it is imperative to reduce the total on-grid energy consumption as well as minimize the peak-to-average on-grid energy consumption ratio, since the large peak-to-average on-grid energy consumption ratio will translate into the high operational expenditure (OPEX) for mobile network operators. In this paper, we propose a joint user association and green energy allocation algorithm which aims to lexicographically minimize the on-grid energy consumption in HetNets, where all the base stations (BSs) are assumed to be powered by both the power grid and renewable energy sources. The optimization problem involves both the user association optimization in space dimension, and the green energy allocation in time dimension. The independence nature of this two-dimensional optimization allows us to decompose the problem into two sub-problems. We first formulate the user association optimization in space dimension as a convex optimization problem to minimize total energy consumption via balancing the traffic across different BSs in a certain time slot. We then optimize the green energy allocation across different time slots for an individual BS to lexicographically minimize the on-grid energy consumption. Simulation results indicate the proposed algorithm achieves significant on-grid energy saving, and substantially reduces peak-to-average on-grid energy consumption ratio.

In green communications, it is imperative to reduce the total on-grid energy consumption as well as minimize the peak on-grid energy consumption, since the large peak on-grid energy consumption will translate into the high operational expenditure (OPEX) for mobile network operators. In this paper, we consider the two-dimensional optimization to lexicographically minimize the on-grid energy consumption in heterogeneous networks (HetNets). All the base stations (BSs) therein are envisioned to be powered by both power grid and renewable energy sources, and the harvested energy can be stored in rechargeable batteries. The lexicographic minimization of on-grid energy consumption involves the optimization in both the space and time dimensions, due to the temporal and spatial dynamics of mobile traffic and green energy generation. The reasonable assumption of time scale separation allows us to decompose the problem into two sub-optimization problems without loss of optimality of the original optimization problem. We first formulate the user association optimization in space dimension via convex optimization to minimize total energy consumption through distributing the traffic across different BSs appropriately in a certain time slot. We then optimize the green energy allocation across different time slots for an individual BS to lexicographically minimize the on-grid energy consumption. To solve the optimization problem, we propose a low complexity optimal offline algorithm with infinite battery capacity by assuming non-causal green energy and traffic information. The proposed optimal offline algorithm serves as performance upper bound for evaluating practical online algorithms. We further develop some heuristic online algorithms with finite battery capacity which require only causal green energy and traffic information. The performance of the proposed optimal offline and online algorithms is evaluated by simulations.

Mobility load balancing MLB redistributes the traffic load across the networks to improve the spectrum utilisation. This paper proposes a self-organising cluster-based cooperative load balancing scheme to overcome the problems faced by MLB. The proposed scheme is composed of a cell clustering stage and a cooperative traffic shifting stage. In the cell clustering stage, a user-vote model is proposed to address the virtual partner problem. In the cooperative traffic shifting stage, both inter-cluster and intra-cluster cooperations are developed. A relative load response model is designed as the inter-cluster cooperation mechanism to mitigate the aggravating load problem. Within each cluster, a traffic offloading optimisation algorithm is designed to reduce the hot-spot cell's load and also to minimise its partners' average call blocking probability. Simulation results show that the user-vote-assisted clustering algorithm can select two suitable partners to effectively reduce call blocking probability and decrease the number of handover offset adjustments. The relative load response model can address public partner being heavily loaded through cooperation between clusters. The effectiveness of the traffic offloading optimisation algorithm is both mathematically proven and validated by simulation. Results show that the performance of the proposed cluster-based cooperative load balancing scheme outperforms the conventional MLB. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, a fair user association scheme is proposed for heterogeneous networks (HetNets), where the user association optimization is formulated as a Nash bargaining problem. The optimization objective is to maximize the sum of rate related utility, under users' minimal rate constrains, while considering user fairness and load balance between cells in different tiers. Nash bargaining solution and coalition are adopted to solve this optimization problem. Firstly, a two-player bargaining scheme is developed for two base stations (BSs) to bargain user association. Then this two-player scheme is extended to a multi-player bargaining scheme with the aid of Hungarian algorithm that optimally groups BSs into pairs. Simulation results show that the proposed scheme can effectively offload users from macrocells, improve user fairness, and also achieve comparable sum rate to the scheme that maximizes the sum rate without considering user fairness.

The heterogeneous cellular networks (HCNs) will be the typical layout of the next generation mobile networks. Understanding the aggregate interference from multi-tier heterogeneous base stations (BSs) of HCNs is the key for research on network deployment and interference management. In this paper, we propose a statistical model for quantifying the aggregate interference in HCNs and evaluating its impact on system performance. We first model the distribution of multitier heterogeneous BSs as spatial Poisson point process and derive the characteristic function (CF) of the downlink aggregate interference for a specific target user. We review the CF of single-tier network interference and proof that the aggregate interference of HCNs follows the stable distribution, based on which, we derive statistical characterization of aggregate interference amplitude and power, respectively. Then, we propose an aggregate interference statistical model based on truncated-stable distribution. Finally, the users outage probability of the HCNs is analysed via the proposed model. The proposed model is validated with simulation. This work provides essential understanding of interference of HCNs and gives insights which can facilitate system performance analysis and interference management.

IEEE 802.15.4 beacon-enabled mode adopts duty cycle to achieve energy efficiency and provides an optional acknowledgement (ACK) mechanism to ensure the transmission reliability. However, frequently sending ACK introduces additional ACK transmission energy consumption and increases end-to-end delay. In this paper, we focus on a duty cycle optimisation problem with joint consideration on energy efficiency, end-to-end delay and reliability for IEEE 802.15.4 networks. We first formulate a cumulative ACK enabled duty cycle optimisation problem as an inventory control problem. Then, the optimal solution to the problem is derived by applying dynamic programming (DP). Furthermore, a low complexity delay-energy aware duty cycle control (DE-DutyCon) is proposed to reduce the computational complexity of implementing the control on computation limited sensor devices. The joint-cost upper bound of DE-DutyCon is also provided. DE-DutyCon achieves an exponential reduction of computational complexity compare with DP optimal control. Simulation results show that the proposed DE-DutyCon achieves close performance in terms of energy efficiency, end-to-end delay and packet drop ratio compare with DP optimal control under various network traffic.

In wireless networks, it is of great significance to balance power consumption and network quality of service (QoS). In this paper, we propose an optimal user association algorithm for delay and power consumption tradeoffs in HetNets with hybrid energy sources. In the considered HetNets, all the base stations (BSs) are assumed powered by a combination of power grid and renewable energy sources, in order to achieve both reliable and green communications. The proposed user association algorithm aims to enhance network QoS by minimizing the average traffic delay, as well as reduce on-grid power consumption by maximizing the utilization of green power harvested from renewable energy sources. To this end, a convex optimization problem is formulated to minimize the weighted sum of cost of average traffic delay and cost of on-grid power consumption. We have proved that the proposed user association algorithm converges to the global optimum which enables a flexible tradeoff between average traffic delay and on-grid power consumption. Simulation results indicate that the proposed user association algorithm substantially reduces on-grid power consumption with limited sacrifice on average traffic delay, compared with the existing user association algorithm which aims to minimize the average traffic delay.

In heterogeneous networks (HetNets), due to transmit power disparity between macro and pico base stations (BSs), the conventional strongest downlink (DL) reference signal received power (RSRP) based user association results in high uplink (UL) interference. Such interference degrades the UL performance especially in terms of energy efficiency. In this paper, we propose Joint Uplink and Downlink User Association (JUDUA) that takes both the UL and DL energy efficiencies into consideration when deciding the serving BS for user equipments (UEs). JUDUA formulates user association optimization problem as a Nash bargaining problem aiming to maximize the sum of log-scale UL and DL energy efficiencies among all UEs. Simulation results demonstrate that JUDUA improves UL and DL energy efficiencies of UEs, which in turn boosts UL and DL system capacity, reduces UL transmit power compared with the conventional user association schemes.

Channel reservation is a valid technique to reduce dropping probability when spectrum handover happens in cognitive radio. Most of the existing channel reservation schemes focus on proper number of the channels reserved and the number is fixed for a certain system. These static channel reservation schemes bring high blocking probability. In this paper, reusing reserved channels is considered in dynamic channel reservation (DCR) scheme. Secondary users are divided into two classes according to different time-delay-sensitivity in order to use reserved channels dynamically. Renewal theory is used to obtain the forecast results of licensed channels to determine the number of channels reserved. The DCR scheme is analyzed with a Markov chain. Numerical results show that the DCR scheme performs lower blocking probability and higher throughput with dropping probability basically keeping unchanged compared with the schemes based on static channel reservation.

In order to deal with uneven load distribution, mobility load balancing adjusts the handover region to shift edge users from a hot-spot cell to the less-loaded neighbouring cells. However, shifted users receive the reduced signal power from neighbouring cells, which may result in link quality degradation. This paper employs a user relaying model and proposes a user relay assisted traffic shifting (URTS) scheme to address this problem. In URTS scheme, a shifted user selects a suitable non-active user as relay user to forward signal, thus enhancing the link quality of the shifted user. Since the user relaying model consumes relay user's energy, a utility function is designed in relay selection to reach a trade-off between the shifted user's link quality improvement and the relay user's energy consumption. Simulation results show that the URTS scheme can improve SINR and capacity of shifted users. Also, URTS scheme keeps the cost of relay user's energy consumption at an acceptable level.

This paper proposes a multichannel medium access control (MAC) protocol for energy efficient IEEE802.15.4 home area networks that consists of novel allocation and superframe scheduling algorithm that improves the channel selection strategy and the superframe scheduling. It aims to improve the overall reliability and reduce the average delay. The proposed protocol is implemented in IEEE802.15.4 home area network andsimulation results show the proposed protocol outperforms the existing IEEE802.15.4 MAC protocols in the aspects of schedulability, reliability, delay and the overall throughput.

In heterogeneous network (HetNet), the combined cell range extension (CRE) and enhanced inter-cell interference coordination (elCIC) proposed by 3GPP is considered as the most effective user association scheme. However, this scheme requires strict frame synchronization between macrocells and small cells. In this paper, we propose a Nash bargaining solution (NBS) based user association scheme which does not require such synchronization between cells in different tiers. The proposed scheme formulates user association optimization problem as a Nash bargaining problem. The objective is to maximize the sum data rate related utility of all users in the overall system while guaranteeing user's minimal data rate and considering the user fairness. The simulation results show the proposed scheme can achieve higher sum rate of all users and better user fairness compared with the combined CRE and elCIC based user association scheme. Moreover, the proposed scheme has low computational complexity by applying Hungarian algorithm.

In this paper an intelligent scheduling architecture is presented for LTE-Advanced downlink transmission, to enhance the Quality of Service (QoS) provision to different traffic types while maintaining system level performance in such as system throughput. Hebbian learning process and K-mean clustering algorithm are integrated in the Time Domain (TD) of scheduling architecture, to intelligently allocate available radio resources to Real Time (RT) and Non Real Time (NRT) traffic, and to prioritise RT users based on their Packet Drop Rate (PDR) feedback. The integration of these algorithms allows just enough resource allocation to RT traffic and diverts extra resources to NRT traffic, to fulfill its minimum throughput requirements. System level simulation is set up for system level performance evaluation. Simulation results show that the proposed architecture reduces average delay, delay violation probability and average Packet Drop Rate (PDR) of RT traffic while guaranteeing the support of minimum throughput to NRT traffic and maintains system throughput at good level.

This paper gives an overview of the Long Term Evolution (LTE) of the Universal Mobile Telecommunication System (UMTS), which is being developed by the 3rd Generation Partnership Project (3GPP). LTE constitutes the latest step towards the 4th generation (4G) of radio technologies designed to increase the capacity and speed of mobile communications. Particular attention is given to the requirements and targets of LTE, its use of multiple antenna techniques, and to the Single Carrier Frequency Division Multiple Access (SC-FDMA) modulation scheme used in the LTE uplink. Furthermore new future research areas are proposed here.

In this paper an Adaptive Time Domain Scheduling Algorithm (ATDSA) is proposed for Long Term Evolution-Advanced (LTE-A) downlink (DL) transmission. This algorithm uses the Hebbian learning process to allocate radio resource adaptively to different types of traffic. The aim is to improve QoS provision for different traffic types while maintaining a reasonable tradeoff between system throughput and user fairness. The proposed ATDSA is implemented and validated in a dynamic packet scheduling framework via a system level simulation. Results show that ATDSA reduces the average delay, delay viability and packet drop rate (PDR) of real time traffic; guarantees the minimum throughput of non real time traffic while balancing the tradeoff between system throughput and user fairness.