A simultaneously transmitting and reflecting surface (STARS) assisted near-field (NF) integrated sensing and communication (ISAC) framework is proposed, where the radio sensors are installed on the STARS to directly conduct the distance-domain sensing by exploiting the spherical wavefront. A new squared position error bound (SPEB) expression is derived to reveal the dependence on beamforming (BF) design and sensor deployment. To balance the trade-off between the SPEB and the sensor deployment cost, a cost function minimization problem is formulated to jointly optimize the sensor deployment, the active and passive BF, subject to communication and power consumption constraints. For the sensor deployment optimization, a joint sensor deployment algorithm is proposed by invoking the successive convex approximation. Under a specific relationship between the sensor numbers and BF design, we derive the optimal sensor interval in a closed-form expression. For the joint BF optimization, a penalty-based method is invoked. Simulation results validated that the derived SPEB expression is close to the exact SPEB, which reveals the Fisher Information Matrix of position estimation in NF can be approximated as a diagonal matrix. Furthermore, the proposed algorithms achieve the best SPEB performance compared to the benchmark schemes accompanying the lowest deployment cost.

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.

A hybrid non-orthogonal multiple access (HNOMA) empowered integrated sensing and communications (ISAC) framework is proposed, which adaptively manages the additional sensing-to-communication (S2C) interference to save the transmit power. Two scenarios with different numbers of communication users (CUs) are investigated. For the first scenario where the number of CUs does not exceed the number of transmit antennas, a mixed integer problem is formulated to optimize the beamforming (BF) design and successive interference cancellation (SIC) options. An ideal case is primarily inspected, which unveils an insight into the required number of dedicated sensing beams. Inspired by this insight, the SIC options are determined while the remaining BF design is solved via semidefinite relaxation (SDR). For the second scenario where the number of CUs exceeds the number of transmit antennas, the CUs are further grouped into NOMA clusters to mitigate the communication-to-communication interference. An alternating optimization-based algorithm is developed, where the BF design, SIC options and power allocation are alternatively optimized. Simulation results reveal that: 1) the proposed algorithm achieves power-saving gain compared to the conventional ISAC; 2) the proposed algorithm can further exploit the benefits of NOMA to save transmission power while maintaining the least beampattern mismatch in the second scenario.

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.

The capstone project is a crucial element of a degree programme and plays a vital role in the growth of learners, as it enables them to enhance their problem-solving skills and improve their employability prospects. In addition to this, the project provides the learners with an opportunity to demonstrate and showcase their critical thinking abilities and creativity. However, due to the year-long independent nature of these projects, learners can disengage due to a lack of motivation or self-regulated skills throughout the project. To address this problem, we formulated a data-driven intervention approach that conducts learner engagement analytics to identify and support disengaged learners, ensuring they maximise the benefits of completing a capstone project. The motivation was also to provide these learners with the necessary resources and support to get them back on track. This approach was implemented in the capstone projects conducted by learners at Queen Mary University of London within the School of Electronic Engineering and Computer Science. Based on the data of the three cohorts in 2020–21, 2021–22 and 2022–23, our analysis shows that the proposed data-driven intervention approach for capstone projects can effectively identify less-engaged learners and targeted interventions are shown to improve the overall performance of these less-engaged learners on capstone projects.

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.

A novel multiple-input multiple-output (MIMO) cognitive radio (CR) system is proposed in this work. Specifically, the underly secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The secondary base station (SBS) beamformers and the STAR-RIS coefficients are jointly designed to maximize the sum rate of secondary users (SUs) considering the power limitation at the SBS, interference limitation at the primary users (PUs), and the coefficients constraint for the STAR-RIS. The block coordinate descent method is invoked to tackle the formulated optimization problem. In each iteration, the beamformers at the SBS are optimized by solving a quadratically constrained quadratic program problem, and the passive STAR beamforming problem is solved with the successive convex approximation-based algorithm. Simulation results show that the proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system.

A novel reconfigurable intelligent surfaces (RIS) assisted near-field (NF) ISAC system is investigated, where the spherical wave propagation environment is utilized to elevate the radio sensing performance. Except for the conventional RIS, the sensor elements are embedded on the RIS surface to conduct the radios sensing functionality. By exploiting the symmetry property of the steering vector, a new expression of position error bound (PEB) is derived to unveil the impact of the sensor deployment. To balance the radio sensing performance and the sensor deployment cost, a cost function minimization problem is formulated to jointly optimize the number of sensor elements and the passive beamforming (BF). To solve this non-convex problem, a joint geometric programming element-wise (JGPE) algorithm is proposed. The successive convex approximation for geometric programming is invoked to optimize the number of sensor elements while the element-wise algorithm is adopted to optimize the passive BF. Numerical results demonstrated that the proposed algorithm reach the least PEB and cost function value among the benchmarks.

A downlink near-field integrated sensing and communication (ISAC) framework is proposed. A novel double-array structure at the BS is proposed, where an assisting receiver (AR) is attached to the main transmitter (MT) to enable the near-field communication (NFC) system with the ability of target positioning. The joint angle and distance Cramér-Rao bound (CRB) is derived and then minimized subject to the communication quality of ser-vice (QoS) requirement and the hybrid-analog-and-digital (HAD) structure constraint. A double-loop iterative algorithm utilizing the penalty dual decomposition (PDD) framework is proposed to tackle the non-convex problem. The numerical results show that: 1) The proposed ISAC system can locate the target in both angle and distance domains; 2) The performance of the HAD ISAC approaches the performance of fully digital (FD) ISAC when the communication QoS requirement is not stringent.

A novel non-orthogonal semantic communication (NSC) framework is proposed for facilitating high-efficiency multi-user semantic communications. The NSC technique enables non-orthogonal semantic streams among users by sharing the same resource block. A semantic superposition coding (SSC) and a semantic interference tolerated (SIT) decoding paradigm are proposed for the NSC transmitters and receivers, respectively. SSC encoders are a type of joint source-channel encoder enabled by deep learning (DL), which aims to superpose the semantic information for different users to a piece of semantic feature sequence. An SSC encoder at the access point (AP) is paired with several SIT decoders at different user equipment. By jointly training the SSC encoder and all SIT decoders, the SIT decoders can identify the desired semantic information for each user, and the semantic interferences introduced by SSC are mitigated. Simulation results reveal that the proposed NSC scheme considerably improves transmission efficiency. Meanwhile, at high compression ratios, the NSC scheme outperforms conventional orthogonal semantic communications in terms of accuracy gains.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing, computing, and communication (ISCC) Internet of Robotic Things (IoRT) framework is proposed. Specifically, the full-duplex (FD) base station (BS) simultaneously receives the offloading signals from decision robots (DRs) and carries out target robot (TR) sensing. A computation rate maximization problem is formulated to optimize the sensing and receive beamformers at the BS and the STAR-RIS coefficients under the BS power constraint, the sensing signal-to-noise ratio constraint, and STAR-RIS coefficients constraints. The alternating optimization (AO) method is adopted to solve the proposed optimization problem. With fixed STAR-RIS coefficients, the subproblem with respect to sensing and receiving beamformer at the BS is tackled with the weighted minimum mean-square error method. Given beamformers at the BS, the subproblem with respect to STAR-RIS coefficients is tacked with the penalty method and successive convex approximation method. The overall algorithm is guaranteed to converge to at least a stationary point of the computation rate maximization problem. Our simulation results validate that the proposed STAR-RIS aided ISCC IoRT system can enhance the sum computation rate compared with the benchmark schemes.

The development of sixth-generation (6G) communication technologies is confronted with the significant challenge of spectrum resource shortage. To alleviate this issue, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multiple-input multiple-output (MIMO) cognitive radio (CR) system. Specifically, the underlying secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the STAR-RIS. The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system. The adoption of STAR-RIS necessitates an intricate beamforming design for the considered system due to its large number of coupled coefficients. The block coordinate descent method is employed to address the formulated optimization problem. In each iteration, the beamformers at the secondary base station (SBS) are optimized by solving a quadratically constrained quadratic program (QCQP) problem. Concurrently, the STAR-RIS passive beamforming problem is resolved using tailored algorithms designed for the two phase-shift models: 1) For theindependent phase-shift model, a successive convex approximation-based algorithm is proposed; 2) For thecoupled phase-shift model, a penalty dual decomposition-based algorithm is conceived, in which the phase shifts and amplitudes of the STAR-RIS elements are optimized using closed-form solutions. Simulation results show that: 1) The proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system; 2) The coupled phase-shift model results in limited performance degradation compared to the independent phase-shift model.

A novel non-orthogonal multiple access (NOMA) assisted full space integrated sensing and communication (ISAC) framework is proposed to elevate the radio sensing performance. Exploiting the simultaneously transmitting and reflecting RIS (STAR-RIS) to extend the half-space into full-space ISAC coverage intensifies the competition for wireless resources. To alleviate this fierce competition as well as ensure ISAC performance, the cluster-based NOMA (CB-NOMA) technique is employed to save the joint communication and sensing (C&S) beams. Furthermore, the dedicated sensing beam accompanied by the joint C&S beams supports the radio sensing functionality. A minimum beampattern gain maximization problem is formulated to jointly optimize the power allocation, active and passive beamformer (BF) design, subject to communication requirements. To solve this non-convex problem, a block coordinate descent (BCD) based integral matrix algorithm is proposed to reach a suboptimal solution. For the joint power allocation and active BF block, the semidefinite relaxation and successive convex approximation are employed to optimize the coupled variables. For the passive BF block, the penalty-based method is invoked. To further reduce the complexity of the passive BF design, a BCD-based element-wise algorithm is proposed, where the joint phase shift and amplitude coefficients of each STAR-RIS element are optimized one by one. Simulation results verified that our proposed algorithms achieve higher beampattern gain towards the intended targets than the benchmark schemes accompanying less mismatch error.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided near-field multiple-input multiple-output (MIMO) communication framework is proposed. A weighted sum rate maximization problem for the joint optimization of the active beamforming at the base station (BS) and the transmission/reflection-coefficients (TRCs) at the STAR-RIS is formulated. The resulting non-convex problem is solved by the developed block coordinate descent (BCD)-based algorithm. Numerical results illustrate that the near-field beamforming for the STAR-RIS aided MIMO communications significantly improve the achieved weighted sum rate.

Compared with the current Shannon's Classical Information Theory (CIT) paradigm, semantic communication (SemCom) has recently attracted more attention, since it aims to transmit the meaning of information rather than bit-by-bit transmission, thus enhancing data transmission efficiency and supporting future human-centric, data-, and resource-intensive intelligent services in 6G systems. Nevertheless, channel noises are common and even serious in 6G-empowered scenarios, limiting the communication performance of SemCom, especially when Signal-to-Noise (SNR) levels during training and deployment stages are different, but training multi-networks to cover the scenario with a broad range of SNRs is computationally inefficient. Hence, we develop a novel De-Noising SemCom (DNSC) framework, where the designed de-noiser module can eliminate noise interference from semantic vectors. Upon the designed DNSC architecture, we further combine adversarial learning, variational autoencoder, and diffusion model to propose the Latent Diffusion DNSC (Latent-Diff DNSC) scheme to realize intelligent online de-noising. During the offline training phase, noises are added to latent semantic vectors in a forward Markov diffusion manner and then are eliminated in a reverse diffusion manner through the posterior distribution approximated by the U-shaped Network (U-Net), where the semantic de-noiser is optimized by maximizing evidence lower bound (ELBO). Such design can model real noisy channel environments with various SNRs and enable to adaptively remove noises from noisy semantic vectors during the online transmission phase. The simulations on open-source image datasets demonstrate the superiority of the proposed Latent-Diff DNSC scheme in PSNR and SSIM over different SNRs than the state-of-the-art schemes, including JPEG, Deep JSCC, and ADJSCC.

A simultaneously transmitting and reflecting RIS (STAR-RIS) empowered integrated sensing and communications (ISAC) framework is proposed, where the STAR-RIS establishes an additional link to compensate for the insufficient LoS link. To alleviate the conflicts between the limited wireless resources and the multifunctionality requirements, a cluster-based NOMA transmission scheme is adopted, where the communication functionality is employed by the joint communication and sensing (C&S) beam in a NOMA approach. A minimum beampattern gain maximization problem is formulated to jointly optimize the power allocation, active and passive beamformer (BF) design. We propose a block coordinate descent (BCD) based iterative algorithm, which splits the optimization variables into two blocks. For the joint power allocation and active BF block, the semidefinite relaxation and successive convex approximation are employed. For the passive BF block, the penalty-based method is invoked to deal with the non-convex constraints. Simulation results verified that our proposed algorithm achieves higher beampattern gain at the intended targets than the other baselines accompanying the least mismatch error.

A novel framework of reconfigurable intelligent surfaces (RISs) enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enhance the service quality for mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are adopted to further increase the spectrum efficiency since RISs are capable to provide NOMA with artificially controlled channels, which can be a beneficial condition for NOMA networks. To optimize the sum rate of all users, a federated learning enhanced deep deterministic policy gradient (FL-DDPG) algorithm is proposed to optimize the deployment and phase shifts of the mobile RIS as well as the power allocation policy. Our simulation results indicate that the mobile RIS scheme can provide about three times data rate gain compare to the fixed RIS. Moreover, the NOMA scheme is capable to achieve a significant data rate gain in contrast with the OMA scheme. Finally, the FL-DDPG algorithm has a superior convergence rate and optimization performance than that of the independent training framework.

A reconfigurable intelligent surface (RIS)-assisted multi-user downlink communication system over fading channels is investigated, where both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes are employed. In particular, the time overhead for configuring the RIS reflective elements at the beginning of each fading channel is considered. The optimization goal is maximizing the effective throughput of the entire transmission period by jointly optimizing the phase shift of the RIS and the power allocation of the AP for each channel block. In an effort to solve the formulated problem and fill the research vacancy of the performance comparison between different machine learning tools in wireless networks, a deep learning (DL) approach and a reinforcement learning (RL) approach are proposed and their representative superiority and inferiority are investigated. The DL approach can locate the optimal phase shifts with the deep neural network fitting as well as the corresponding power allocation for each user. From the perspective of long-term reward, the phase shift control with configuration overhead can be regarded as a Markov decision process and the RL algorithm is proficient in solving such problems with the assistance of the Bellman equation. The numerical results indicate that: 1) From the perspective of the wireless network, NOMA can achieve a throughput gain of about 42% compared with OMA; 2) The well-trained RL and DL agents are able to achieve the same performance in Rician channel, while RL is superior in the Rayleigh channel; 3) The DL approach has lower complexity and faster convergence, while the RL approach has preferable strategy flexibility.

A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated. In contrast to the existing ideal STAR-RIS model assuming an independent transmission and reflection phase-shift control, a practical coupled phase-shift model is considered. Then, a joint active and passive beamforming optimization problem is formulated for minimizing the long-term transmission power consumption, subject to the coupled phase-shift constraint and the minimum data rate constraint. Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy. Inspired by this observation, a pair of hybrid reinforcement learning (RL) algorithms, namely the hybrid deep deterministic policy gradient (hybrid DDPG) algorithm and the joint DDPG & deep-Q network (DDPG-DQN) based algorithm are proposed. The hybrid DDPG algorithm controls the associated high-dimensional continuous and discrete actions by relying on the hybrid action mapping. By contrast, the joint DDPG-DQN algorithm constructs two Markov decision processes (MDPs) relying on an inner and an outer environment, thereby amalgamating the two agents to accomplish a joint hybrid control. Simulation results demonstrate that the STAR-RIS has superiority over other conventional RISs in terms of its energy consumption. Furthermore, both the proposed algorithms outperform the baseline DDPG algorithm, and the joint DDPG-DQN algorithm achieves a superior performance, albeit at an increased computational complexity.

A novel Twitter context aided content caching (TAC) framework is proposed for enhancing the caching efficiency by taking advantage of the legibility and massive volume of Twitter data. For the purpose of promoting the caching efficiency, three machine learning models are proposed to predict latent events and events popularity, utilizing collected Twitter data with geo-tags and geographic information of the adjacent base stations (BSs). Firstly, we propose a latent Dirichlet allocation (LDA) model for latent events forecasting because of the superiority of LDA model in natural language processing (NLP). Then, we conceive long short-term memory (LSTM) with skip-gram embedding approach and LSTM with continuous skip-gram-Geo-aware embedding approach for the events popularity forecasting. Furthermore, we associate the predict latent events and the popularity of the events with the caching strategy. Lastly, we propose a non-orthogonal multiple access (NOMA) based content transmission scheme. Extensive practical experiments demonstrate that: 1) the proposed TAC framework outperforms conventional caching framework and is capable of being employed in practical applications thanks to the associating ability with public interests; 2) the proposed LDA approach conserves superiority for natural language processing (NLP) in Twitter data; 3) the perplexity of the proposed skip-gram based LSTM is lower compared with conventional LDA approach; and 4) evaluation of the model demonstrates that the hit rates of tweets of the model vary from 50% to 65% and the hit rate of the caching contents is up to approximately 75% with smaller caching space compared to conventional algorithms. Simulation results also shows that the proposed NOMA-enabled caching scheme outperforms conventional least frequently used (LFU) scheme by 25%.

A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is formulated for maximizing the throughput. Since ground mobile users are considered as roaming continuously, the UAVs need to be re-deployed timely based on the movement of users. In an effort to solve this pertinent dynamic problem, a K-means based clustering algorithm is first adopted for periodically partitioning users. Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs. In contrast to the conventional deep Q-network (DQN) algorithm, the MDQN algorithm enables the experience of multi-agent to be input into a shared neural network to shorten the training time with the assistance of state abstraction. Numerical results demonstrate that: 1) the proposed MDQN algorithm is capable of converging under minor constraints and has a faster convergence rate than the conventional DQN algorithm in the multi-agent case; 2) The achievable sum rate of the NOMA enhanced UAV network is 23% superior to the case of orthogonal multiple access (OMA); 3) By designing the optimal 3D trajectory of UAVs with the MDON algorithm, the sum rate of the network enjoys 142% and 56% gains than invoking the circular trajectory and the 2D trajectory, respectively.

A novel framework of reconfigurable intelligent surfaces (RISs)-enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enable mobility of the RIS and enhance the service quality for mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are adopted to further increase the spectrum efficiency since RISs are capable of providing NOMA with artificial controlled channel conditions, which can be seen as a beneficial operation condition to obtain NOMA gains. To optimize the sum rate of all users, a deep deterministic policy gradient (DDPG) algorithm is invoked to optimize the deployment and phase shifts of the mobile RIS as well as the power allocation policy. In order to improve the efficiency and effectiveness of agent training for the DDPG agents, a federated learning (FL) concept is adopted to enable multiple agents to simultaneously explore similar environments and exchange experiences. We also proved that with the same random exploring policy, the FL armed deep reinforcement learning (DRL) agents can theoretically obtain a reward gain comparing to the independent agents. Our simulation results indicate that the mobile RIS scheme can significantly outperform the fixed RIS paradigm, which provides about three times data rate gain compared to the fixed RIS paradigm. Moreover, the NOMA scheme is capable of achieving a gain of 42% in contrast with the OMA scheme in terms of the sum rate. Finally, the multi-cell simulation proved that the FL enhanced DDPG algorithm has a superior convergence rate and optimization performance than the independent training framework.

A communication enabled indoor intelligent robots (IRs) service framework is proposed, where non-orthogonal multiple access (NOMA) technique is adopted to enable highly reliable communications. In cooperation with the ultramodern indoor channel model recently proposed by the International Telecommunication Union (ITU), the Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state in order to construct the radio map. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent, which can save training time and hardware costs. Build on the proposed communication model, motions of IRs who need to reach designated mission destinations and their corresponding down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to solve this optimization problem, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. Our simulation results demonstrate in the following: 1) with the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability.

A novel framework is proposed for integrating reconfigurable intelligent surfaces (RIS) in unmanned aerial vehicle (UAV) enabled wireless networks, where an RIS is deployed for enhancing the service quality of the UAV. Non-orthogonal multiple access (NOMA) technique is invoked to further improve the spectrum efficiency of the network, while mobile users (MUs) are considered as roaming continuously. The energy consumption minimizing problem is formulated by jointly designing the movement of the UAV, phase shifts of the RIS, power allocation policy from the UAV to MUs, as well as determining the dynamic decoding order. A decaying deep Q-network (D-DQN) based algorithm is proposed for tackling this pertinent problem. In the proposed D-DQN based algorithm, the central controller is selected as an agent for periodically observing the state of UAV-enabled wireless network and for carrying out actions to adapt to the dynamic environment. In contrast to the conventional DQN algorithm, the decaying learning rate is leveraged in the proposed D-DQN based algorithm for attaining a tradeoff between accelerating training speed and converging to the local optimal. Numerical results demonstrate that: 1) In contrast to the conventional Q-learning algorithm, which cannot converge when being adopted for solving the formulated problem, the proposed D-DQN based algorithm is capable of converging with minor constraints; 2) The energy dissipation of the UAV can be significantly reduced by integrating RISs in UAV-enabled wireless networks; 3) By designing the dynamic decoding order and power allocation policy, the RIS-NOMA case consumes 11.7% less energy than the RIS-OMA case.

A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology. The problem of joint deployment, phase shift design, as well as power allocation in the multiple-input-single-output (MISO) NOMA network is formulated for maximizing the energy efficiency with considering users particular data requirements. To tackle this pertinent problem, machine learning approaches are adopted in two steps. Firstly, a novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. Secondly, a decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS. In the proposed algorithm, the base station, which controls the RIS by a controller, acts as an agent. The agent periodically observes the state of the RIS-enhanced system for attaining the optimal deployment and design policies of the RIS by learning from its mistakes and the feedback of users. Additionally, it is proved that the proposed D3QN based deployment and design algorithm is capable of converging within mild conditions. Simulation results are provided for illustrating that the proposed LSTM-based ESN algorithm is capable of striking a tradeoff between the prediction accuracy and computational complexity. Finally, it is demonstrated that the proposed D3QN based algorithm outperforms the benchmarks, while the NOMA-enhanced RIS system is capable of achieving higher energy efficiency than orthogonal multiple access (OMA) enabled RIS system.

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) downlink transmission framework is proposed. We formulate a long-term stochastic optimization problem that involves a joint optimization of NOMA user partitioning and RIS phase shifting, aiming at maximizing the sum data rate of the mobile users (MUs) in NOMA downlink networks. To solve the challenging joint optimization problem, we invoke a modified object migration automation (MOMA) algorithm to partition the users into equal-size clusters. To optimize the RIS phase shifting matrix, we propose a deep deterministic policy gradient (DDPG) algorithm to collaboratively control multiple reflecting elements (REs) of the RIS. Different from conventional training-then-testing processing, we consider a long-term self-adjusting learning model where the intelligent agent is capable of learning the optimal action for every given state through exploration and exploitation. Extensive numerical results demonstrate that: 1) The proposed RIS-aided NOMA downlink framework achieves enhanced sum data rate compared with the conventional orthogonal multiple access (OMA) framework. 2) The proposed DDPG algorithm is capable of learning a dynamic resource allocation policy in a long-term manner. 3) The performance of the proposed RIS-aided NOMA framework can be improved by increasing the granularity of the RIS phase shifts. The numerical results also show that increasing the number of reflecting elements (REs) is an efficient method to improve the sum data rate of the MUs.

This article proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service. The problem of joint user association, subchannel assignment, power allocation, phase shifts design, and decoding order determination is formulated for maximizing the achievable sum rate. The challenging mixed-integer non-linear problem is decomposed into an optimization subproblem (P1) with continuous variables and a matching subproblem (P2) with integer variables. In an effort to tackle the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmission power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation, and semidefinite relaxation. In terms of the combinational problem (P2), swap matching-based algorithms are developed for achieving a two-sided exchange-stable state among users, BSs and subchannels. Numerical results demonstrate that: i) the sum rate of multi-cell NOMA networks is capable of being increased by 35% with the aid of the IRS; ii) the proposed algorithms for multi-cell IRS-aided NOMA networks can enjoy 22% higher energy efficiency than conventional NOMA counterparts; iii) the trade-off between spectrum efficiency and coverage area can be tuned by judiciously selecting the location of the IRS.

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.

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) downlink transmission framework is proposed. We formulate a long-term stochastic optimization problem that involves the optimization of phase shifting, aiming at maximizing the sum data rate of the mobile users (MUs) in NOMA downlink networks. For intelligently adjusting the phase shifting matrix of the access point (AP), we propose a deep deterministic policy gradient (DDPG) algorithm to collaboratively control multiple reflecting elements (REs) of the RIS. Extensive simulation results demonstrate that: 1) The proposed RIS-aided NOMA downlink framework achieves better sum data rate compared with orthogonal multiple access (OMA) networks. 2) The proposed DDPG algorithm is capable of learning a dynamic resource allocation policy, while conventional optimization approaches can not. 3) Compared with increasing the transmit power of the AP, increasing the number of reflecting elements (REs) is a more efficiency method to improve the sum data rate.

This paper conceive a large intelligent surface (LIS)aided multiple-input multiple-output network for providing wireless services to randomly roaming users. The network performance is analyzed by utilizing stochastic geometry tools. We aim for serving multiple users by jointly designing the passive beamforming weight at LISs and detection weight vectors at users. As a benefit, the interference imposed by the LISs can be suppressed. In an effort to evaluate the performance of the proposed network, we first derive approximated channel statistics for characterizing the effective channel gains. Then, we derive closed-form expressions for the ergodic rate of users. For gleaning further insights, we investigate the high-signal-to-noise-ratio (SNR) of ergodic rate. Our analytical results demonstrate that the specific fading environments encountered between the LISs and users have almost no impact on the ergodic rate attained.

This paper investigates a massive non-orthogonal multiple access (NOMA) enhanced Internet of Things (IoT) network. In order to provide massive connectivity, a novel cluster strategy is proposed, where massive devices can be served simultaneously. New channel statistics are derived. The exact and the asymptotic expressions in terms of coverage probability are derived. In order to obtain further engineering insights, short-packet communication scenarios are investigated. From our analysis, we show that the performance of NOMA enhanced IoT networks is capable of outperforming orthogonal multiple access (OMA) enhanced IoT networks.

A novel framework is proposed for enhancing the driving safety and fuel economy of autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication networks. To solve this pertinent problem, a double deep Q-network (DDQN) algorithm is proposed for making collision-free decisions. Thus, the trajectory and velocity of the AV are determined by receiving real-time traffic information from the base stations (BSs). Compared to the conventional deep Q-network algorithm, the proposed DDQN algorithm is capable of overcoming the large overestimation of action values by decomposing the max-Q-value operation into action selection and action evaluation. Numerical results are provided for demonstrating that the proposed trajectory design algorithms are capable of enhancing the driving safety and fuel economy of AVs. We demonstrate that the proposed DDQN based algorithm outperforms the DQN based algorithm. Additionally, it is also demonstrated that the proposed fuel-economy (FE) based driving policy derived from the DRL algorithm is capable of achieving in excess of 24% of fuel savings over the benchmarks.

In order to meet the real-time performance requirements, intelligent decisions in many IoT applications must take place right here right now at the network edge. The conventional cloud-based learning approach would not be able to keep up with the demands in achieving edge intelligence in these applications. Nevertheless, pushing the artificial intelligence (AI) frontier to achieve edge intelligence is highly nontrivial due to the constrained computing resources and limited training data at the network edge. To tackle these challenges, we develop a distributionally robust optimization (DRO)-based edge learning algorithm, where the uncertainty model is constructed to foster the synergy of cloud knowledge transfer and local training. Specifically, the knowledge transferred from the cloud is in the form of a Dirichlet process prior distribution for the edge model parameters, and the edge device further constructs an uncertainty set centered around the empirical distribution of its local samples to capture the information of local data processing. The edge learning DRO problem, subject to the above two distributional uncertainty constraints, is then recast as an equivalent single-layer optimization problem using a duality approach. We then use an Expectation-Maximization (EM) algorithm-inspired method to derive a convex relaxation, based on which we devise algorithms to learn the edge model parameters. Finally, extensive experiments are implemented to showcase the performance gain over standard learning approaches using local edge data only.

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.

This article investigates a nonorthogonal multiple access (NOMA)-enhanced Internet of Things (IoT) network. In order to provide connectivity, a novel cluster strategy is proposed, where multiple devices can be served simultaneously. Two potential scenarios are investigated: 1) NOMA-enhanced terrestrial IoT networks and 2) NOMA-enhanced aerial IoT networks. We utilize stochastic geometry tools to model the spatial randomness of both terrestrial and aerial devices. New channel statistics are derived for both terrestrial and aerial devices. The exact and the asymptotic expressions in terms of coverage probability are derived. In order to obtain further engineering insights, short-packet communication scenarios are investigated. From our analysis, we show that the performance of NOMA-enhanced IoT networks is capable of outperforming OMA-enhanced IoT networks. Moreover, based on simulation results, there exists an optimal value of the transmit power that maximizes the coverage probability.

Reconfigurable intelligent surfaces (RISs) constitute a promising performance enhancement for next-generation (NG) wireless networks in terms of enhancing both their spectral efficiency (SE) and energy efficiency (EE). We conceive a system for serving paired power-domain non-orthogonal multiple access (NOMA) users by designing the passive beamforming weights at the RISs. In an effort to evaluate the network performance, we first derive the best-case and worst-case of new channel statistics for characterizing the effective channel gains. Then, we derive the best-case and worst-case of our closed-form expressions derived both for the outage probability and for the ergodic rate of the prioritized user. For gleaning further insights, we investigate both the diversity orders of the outage probability and the high-signal-to-noise (SNR) slopes of the ergodic rate. We also derive both the SE and EE of the proposed network. Our analytical results demonstrate that the base station (BS)-user links have almost no impact on the diversity orders attained when the number of RISs is high enough. Numerical results are provided for confirming that: i) the high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA network has superior network performance compared to its orthogonal counterpart.

Reconfigurable intelligent surface (RIS) technique stands as a promising signal enhancement or signal cancellation technique for next generation networks. We design a novel passive beamforming weight at RISs in a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network for simultaneously serving paired users, where a signal cancellation based (SCB) design is employed. In order to implement the proposed SCB design, we first evaluate the minimal required number of RISs in both the diffuse scattering and anomalous reflector scenarios. Then, new channel statistics are derived for characterizing the effective channel gains. In order to evaluate the network's performance, we derive the closed-form expressions both for the outage probability (OP) and for the ergodic rate (ER). The diversity orders as well as the high-signal-to-noise-ratio (SNR) slopes are derived for engineering insights. Moreover, the network's performance of a finite resolution design has been evaluated. Our analytical results demonstrate that: i) the inter-cluster interference can be eliminated with the aid of large number of RIS elements; ii) the line-of-sight of the BS-RIS and RIS-user links are required for the diffuse scattering scenario, whereas the LoS links are not required for the anomalous reflector scenario.

An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing the sum mean opinion score (MOS) of mobile users. Firstly, as user mobility and content popularity have significant impacts on the user experience, a recurrent neural network (RNN) is invoked for user mobility prediction and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of user mobility prediction. Then, based on the predicted mobile users' positions and content popularity, a learning automata based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked for Q-learning to obtain an optimal action selection in a random and stationary environment. It is proven that the LA based action selection scheme is capable of enabling every state to select the optimal action with arbitrary high probability if Q-learning is able to converge to the optimal Q value eventually. In the LAQL algorithm, a central processor acts as the intelligent agent, which allocate contents to BSs according to the reward or penalty from the feedback of the BSs and users, iteratively. To characterize the performance of the proposed LAQL algorithms, sum MOS of users is applied to define the reward function. Extensive simulation results reveal that: 1) the prediction error of RNNs based algorithm lessen with the increase of iterations and nodes; 2) the proposed LAQL achieves significant performance improvement against traditional Q-learning algorithm; and 3) the cooperative caching scheme is capable of outperforming non-cooperative caching and random caching of 3% and 4%, respectively.

A novel non-orthogonal multiple access (NOMA) based cache-aided mobile edge computing (MEC) framework is proposed. For the purpose of efficiently allocating communication and computation resources to users' computation tasks requests, we propose a long-short-term memory (LSTM) network to predict the task popularity. Based on the predicted task popularity, a long-term reward maximization problem is formulated that involves a joint optimization of the task offloading decisions, computation resource allocation, and caching decisions. To tackle this challenging problem, a single-agent Q-learning (SAQ-learning) algorithm is invoked to learn a long-term resource allocation strategy. Furthermore, a Bayesian learning automata (BLA) based multi-agent Q-learning (MAQ-learning) algorithm is proposed for task offloading decisions. More specifically, a BLA based action select scheme is proposed for the agents in MAQ-learning to select the optimal action in every state. We prove that the BLA based action selection scheme is instantaneously self-correcting and the selected action is an optimal solution for each state. Extensive simulation results demonstrate that: 1) The prediction error of the proposed LSTMs based task popularity prediction decreases with increasing learning rate. 2) The proposed framework significantly outperforms the benchmarks like all local computing, all offloading computing and non-cache computing. 3) The proposed BLA based MAQ-learning achieves an improved performance compared to conventional MAQ-learning algorithm.

This paper investigates the non-orthogonal multiple access (NOMA) enhanced Air-to-Everything (A2X) frameworks. A novel 3-Dimension unmanned aerial vehicles (UAVs) framework for providing wireless services to randomly roaming NOMA receivers (Rxs) in the sphere space is proposed by utilizing stochastic geometry tools. In an effort to evaluate the performance of the proposed framework, we first derive closed-form expressions for the outage probability of paired NOMA Rxs. For obtaining more insights, we investigate the diversity order of NOMA enhanced A2X frameworks. Our analytical results demonstrate that the diversity order and the high SNR slope of the proposed framework are m. Numerical results are provided to confirm that for the case of fixed LoS probability, the outage performance of paired NOMA Rxs mainly depends on users with poor channel conditions.

A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of users' mobility information. The problem of joint trajectory design and power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a three-step approach is proposed which is based on machine learning techniques. Firstly, a multi-agent Q-learning based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Secondly, in an effort to determine the mobility information of users based on a real dateset, their position data is collected from Twitter to describe the anonymous user- trajectories in the physical world. In the meantime, an echo state network (ESN) based prediction algorithm is proposed for predicting the future positions of users based on the real dataset. Thirdly, a proposed multi-agent Q-learning based algorithm is invoked for predicting the position of UAVs in each time slot based on the movement of users. The algorithm is proved to be able to converge to an optimal state equation. Numerical results are provided to demonstrate that as the size of the reservoir pool increases, the proposed ESN approach improves the prediction accuracy. Finally, we demonstrate that throughput gains of about 17% are achieved.

This article investigates a wireless caching framework based on tweets and their location data collected from Twitter. The tweet texts are associated with the location information of the corresponding base stations (BSs) for improving the caching efficiency at BSs. Extracted latent topics and predicted content probability are applied to reduce caching redundancy at BSs. A machine learning approach, namely latent Dirichlet allocation (LDA), is invoked to extract location-aware latent topics for better caching performances. In an effort to predict content probability for caching, a novel skip-gram based long short-term memory (LSTM) model is proposed to cluster words with similar semantics for content probability prediction. Moreover, practical data collected from Twitter is tackled to verify the performance of the proposed framework. Extensive practical tests demonstrate that: 1) Our proposed framework is capable of perceiving caching peaks while the conventional counting method fails; 2) The proposed machine learning approaches are capable of generating accurate topics extraction and content probability prediction results; 3) Our proposed framework maintains superiority over conventional caching approaches and possesses considerable application potential due to its ability of associating with indigenous public preferences.

A novel resource allocation scheme for cache-aided mobile-edge computing (MEC) is proposed, to efficiently offer communication, storage and computing service for intensive computation and sensitive latency computational tasks. In this paper, the considered resource allocation problem is formulated as a mixed integer non-linear program (MINLP) that involves a joint optimization of tasks offloading decision, cache allocation, computation allocation, and dynamic power distribution. To tackle this non-trivial problem, Markov decision process (MDP) is invoked for mobile users and the access point (AP) to learn the optimal offloading and resource allocation policy from historical experience and automatically improve allocation efficiency. In particular, to break the curse of high dimensionality in the state space of MDP, a deep reinforcement learning (DRL) algorithm is proposed to solve this optimization problem with low complexity. Moreover, extensive simulations demonstrate that the proposed algorithm is capable of achieving a quasi-optimal performance under various system setups, and significantly outperform the other representative benchmark methods considered. The effectiveness of the proposed algorithm is confirmed from the comparison with the results of the optimal solution.

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.

In this paper, the application of non-orthogonal multiple access (NOMA) aided multiple unmanned aerial vehicles (UAVs) wireless networks is investigated in the downlink scenario. A new multiple UAVs framework, user-centric strategy for providing emergency services in the rural area, is proposed by utilizing a stochastic geometry model. In order to provide practical insights for the proposed NOMA assisted UAV framework, an imperfect successive interference cancelation (ipSIC) scenario is taken into account. For the user-centric strategy, we derive new exact expressions for the coverage probability. The derived analytical results explicitly indicate that the ipSIC coefficient is a dominant component in terms of coverage probability.

In this paper, a unified framework for 3-hop unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network is proposed. Aim at characterizing the performance of proposed framework, by using stochastic geometry, the analytical expressions for the outage probability of uplink/downlink transmissions are derived in closed- form for randomly deployed NOMA users. To obtain more insights of the network performance, the asymptotic analyses for the outage probability in the high signal- to-noise ratio (SNR) regime are carried out. These results reveal that for the uplink transmission, there exists an error floor due to the interference from the far user, while the performance of the far user can outperform the near one for the downlink transmission, which can be explained by the fact that the far user has a higher receiving power.

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 advocates a pair of strategies in non-orthogonal multiple access (NOMA) in unmanned aerial vehicles (UAVs) communications, where multiple UAVs play as new aerial communications platforms for serving terrestrial NOMA users. A new multiple UAVs framework with invoking stochastic geometry technique is proposed, in which a pair of practical strategies are considered: 1) the UAV-centric strategy for offloading actions and 2) the user-centric strategy for providing emergency communications. In order to provide practical insights for the proposed NOMA assisted UAV framework, an imperfect successive interference cancelation (ipSIC) scenario is taken into account. For both UAV-centric strategy and user-centric strategy, we derive new exact expressions for the coverage probability. We also derive new analytical results for orthogonal multiple access (OMA) for providing a benchmark scheme. The derived analytical results in both user-centric strategy and UAV-centric strategy explicitly indicate that the ipSIC coefficient is a dominant component in terms of coverage probability. Numerical results are provided to confirm that: 1) for both user-centric strategy and UAV-centric strategy, NOMA assisted UAV cellular networks is capable of outperforming OMA by setting power allocation factors and targeted rate properly and 2) the coverage probability of NOMA assisted UAV cellular framework is affected to a large extent by ipSIC coefficient, target rates, and power allocations factors of paired NOMA users.

This paper investigates the multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) assisted unmanned aerial vehicles (UAVs) networks. By utilizing a stochastic geometry model, a new 3-D UAV framework for providing wireless service to randomly roaming NOMA users has been proposed. In an effort to evaluate the performance of the proposed framework, we derive analytical expressions for the outage probability and the ergodic rate of MIMO-NOMA enhanced UAV networks. We examine tractable upper bounds for the whole proposed framework, with deriving asymptotic results for scenarios that transmit power of interference sources being proportional or being fixed to the UAV. For obtaining more insights for the proposed framework, we investigate the diversity order and high signal-to-noise slope of MIMO-NOMA assisted UAV networks. Our results confirm that: 1) Outage probability of NOMA enhanced UAV networks is affected to a large extent by the targeted transmission rates and power allocation factors of NOMA users and 2) For the case that the interference power is proportional to the UAV power, there are error floors for the outage probabilities.

Mobile-edge computation offloading (MECO) has been recognized as a promising solution to alleviate the burden of resource-limited Internet of Thing (IoT) devices by offloading computation tasks to the edge of cellular networks (also known as {\em cloudlet}). Specifically, latency-critical applications such as virtual reality (VR) and augmented reality (AR) have inherent collaborative properties since part of the input/output data are shared by different users in proximity. In this paper, we consider a multi-user fog computing system, in which multiple single-antenna mobile users running applications featuring shared data can choose between (partially) offloading their individual tasks to a nearby single-antenna cloudlet for remote execution and performing pure local computation. The mobile users' energy minimization is formulated as a convex problem, subject to the total computing latency constraint, the total energy constraints for individual data downloading, and the computing frequency constraints for local computing, for which classical Lagrangian duality can be applied to find the optimal solution. Based upon the semi-closed form solution, the shared data proves to be transmitted by only one of the mobile users instead of multiple ones. Besides, compared to those baseline algorithms without considering the shared data property or the mobile users' local computing capabilities, the proposed joint computation offloading and communications resource allocation provides significant energy saving.

Caching during off-peak times can bring popular contents closer to users, and hence improves quality of experience (QoE) of users in wireless networks. We formulate an optimization problem of cooperative content caching, with the aim of maximizing the sum mean opinion score (MOS) of all users in the network. To solve the challenging content caching problem, we cluster users by global K-means (GKM), based on content popularity. For improving the effectiveness of caching, we propose a low complexity ε-greedy Q-learning based content caching algorithm which obtains a near-optimal solution. To characterize the performance of the proposed cooperative caching algorithms, sum MOS of users is used to define the reward function in Q- learning. The proposed Q-learning algorithm is capable of assisting the network to efficiently utilize the caching resource of the BSs. Simulation results reveal that: The proposed low complexity ε-greedy Q-learning based content caching algorithm achieves a near-optimal performance and is capable of outperforming GKM based caching.

This paper investigates a two-way relay non- orthogonal multiple access (TWR-NOMA) system, where two groups of NOMA users exchange messages with the aid of one half-duplex (HD) decode-and-forward (DF) relay. Since the signal-plus-interference-to-noise ratios (SINRs) of NOMA signals mainly depend on effective successive interference cancellation (SIC) schemes, imperfect SIC (ipSIC) and perfect SIC (pSIC) are taken into consideration. To characterize the performance of TWR-NOMA systems, we derive closed-form expressions for both exact and asymptotic outage probabilities of NOMA users' signals with ipSIC/pSIC. Based on the results derived, the diversity order and throughput of the system are examined. Numerical simulations demonstrate that: 1) TWR-NOMA is superior to TWR-OMA in terms of outage probability in low SNR regimes; and 2) Due to the impact of interference signal (IS) at the relay, error floors and throughput ceilings exist in outage probabilities and ergodic rates for TWR-NOMA, respectively.

In this paper, a unified framework of non-orthogonal multiple access (NOMA) networks is proposed, which can be applied to code-domain NOMA (CD-NOMA) and power-domain NOMA (PD-NOMA). Since the detection of NOMA users mainly depend on efficient successive interference cancellation (SIC) schemes, both imperfect SIC (ipSIC) and perfect SIC (pSIC) are taken into considered. To characterize the performance of this unified framework, the exact and asymptotic expressions of outage probabilities as well as delay-limited throughput for CD/PD-NOMA with ipSIC/pSIC are derived. Based on the asymptotic analysis, the diversity orders of CD/PD-NOMA are provided. It is confirmed that due to the impact of residual interference (RI), the outage probability of the n-th user with ipSIC for CD/PD-NOMA converges to an error floor in the high signal-to-noise ratio (SNR) region. Numerical simulations demonstrate that the outage behavior of CD-NOMA is superior to that of PD-NOMA.

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.

Caching at the edge has emerged as a promising technology to enhance the quality of service (QoS) of users and mitigate the backhaul load. Since the cache capacity is not arbitrarily large to store all contents, the backhaul can assist to fetch the contents from the core network. The network performance analysis of base station (BS) caching should consider the limited backhaul capacity. In this paper, we analyze the energy efficiency of the cache-enabled cellular networks considering the backhaul with stochastic geometry. First, the content coverage probability (CCP) based on the limited backhaul is analyzed. With the obtained CCP results, the expressions of throughput, power consumption and energy efficiency for a general case and a specific case with the mean load approximation are derived respectively. Simulation results confirm the accuracy of theoretical analysis and verify that BSs caching can dramatically improve energy efficiency on the condition that the content popularity is skewed and the backhaul capacity is relatively small.

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.

This paper considers the resource allocation in cache-enabled energy-cooperative HetNets, where base stations (BSs) with different cache sizes are powered by both conventional grids and renewable energy sources, and energy can be shared between BSs via the smart grid. Simulation results demonstrate that the proposed joint user association and power control algorithm can significantly enhance the sum data rate and the energy efficiency of the whole network.

A two-way relay non-orthogonal multiple access (TWR-NOMA) system is investigated, where two groups of NOMA users exchange messages with the aid of one half-duplex decode-and-forward relay. Since the signal-plus-interference-to-noise ratios of NOMA signals mainly depend on effective successive interference cancellation (SIC) schemes, imperfect SIC (ipSIC), and perfect SIC (pSIC) are taken into account. In order to characterize the performance of TWR-NOMA systems, we first derive closed-form expressions for both exact and asymptotic outage probabilities of NOMA users' signals with ipSIC/pSIC. Based on the derived results, the diversity order and throughput of the system are examined. Then, we study the ergodic rates of users' signals by providing the asymptotic analysis in high signal-to-noise ratio (SNR) regimes. Finally, numerical simulations are provided to verify the analytical results and show that: 1) TWR-NOMA is superior to TWR-OMA in terms of outage probability in low SNR regimes; 2) due to the impact of interference signal at the relay, error floors and throughput ceilings exist in outage probabilities, and ergodic rates for TWR-NOMA, respectively; and 3) in delay-limited transmission mode, TWR-NOMA with ipSIC and pSIC have almost the same energy efficiency. However, in delay-tolerant transmission mode, TWR-NOMA with pSIC is capable of achieving larger energy efficiency compared with TWR-NOMA with ipSIC.

This paper proposes a unified framework of non-orthogonal multiple access (NOMA) networks. Stochastic geometry is employed to model the locations of spatially NOMA users. The proposed unified NOMA framework is capable of being applied to both code-domain NOMA (CD-NOMA) and power-domain NOMA (PD-NOMA). Since the detection of NOMA users mainly depend on efficient successive interference cancelation (SIC) schemes, both imperfect SIC (ipSIC) and perfect SIC (pSIC) are taken into account. To characterize the performance of the proposed unified NOMA framework, the exact and asymptotic expressions of outage probabilities as well as delay-limited throughput for CD/PD-NOMA with ipSIC/pSIC are derived. In order to obtain more insights, the diversity analysis of a pair of NOMA users (i.e., the nth user and mth user) is provided. Our analytical results reveal that: 1) the diversity orders of mth and nth user with pSIC for CD-NOMA are mK and nK, respectively; 2) due to the influence of residual interference, the nth user with ipSIC obtains a zero diversity order; and 3) the diversity order is determined by the user who has the poorer channel conditions out of the pair. Finally, Monte Carlo simulations are presented to verify the analytical results: 1) when the number of subcarriers becomes lager, the NOMA users are capable of achieving more steep slope in terms of outage probability and 2) the outage behavior of CD-NOMA is superior to that of PD-NOMA.

Caching in the cellular networks has been proposed as a promising technology for reducing the content delivery latency and backhaul cost. Since the backhaul capacity is limited in the practical scenario, the network performance analysis of base station (BS) caching should address the effects of the limited backhaul. This paper investigates the energy efficiency of the cache‐enabled cellular networks with the limited backhaul based on the stochastic geometry method. First, the successful content delivery probability (SCDP), which depends on the successful access delivery probability, successful backhaul delivery probability, and cache hit ratio, is analyzed under the limited backhaul. Based on the obtained SCDP results, we derive the analytical expressions of throughput, power consumption, and energy efficiency for various scenes including the general case, the interference‐limited case, and the mean load approximation case. The accuracy of theoretical analysis is verified by the Monte Carlo simulation. The simulation results show that BS caching can dramatically improve energy efficiency when the content popularity is skewed, the content library size is small, and the backhaul capacity is relatively small. Furthermore, it is confirmed that there exists an optimal BS density which maximizes the energy efficiency of the cache‐enabled cellular networks.

Licensed-assisted access (LAA) adds great value to cellular networks by extending access to the unlicensed bands, which leads to increased capacity and spectrum efficiency. To fully take advantage of LAA systems and achieve fair coexistence with Wi-Fi networks, channel access mechanisms, such as the listen before talk (LBT), are required. However, due to the LBT mechanism, LAA systems may suffer from the interference of Wi-Fi hidden nodes. In this paper, a hidden node aware joint licensed and unlicensed resource allocation algorithm for LAA systems is proposed. By utilizing measurable medium access control (MAC) layer statistics, an adaptive unlicensed band weight factor is introduced to reflect the impact of Wi-Fi hidden nodes. A resource allocation optimization problem is formulated to maximize the throughput of LAA with quality of service (QoS) guarantee, then an iteration-based solution is obtained by optimizing subcarriers and power allocation in the licensed band and fraction of time allocation in the unlicensed band with fixed power. Extensive simulation results are given to evaluate the performance and validate the effectiveness of the proposed algorithm.

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.

Driven by the energy consumption concerns, renewable energy harvesting is introduced to reduce the energy demand from the traditional power grid. As a key technology of wireless communications, heterogeneous networks can achieve the spectrum and energy efficiency by deploying various low transmit power base stations, which provide an ideal scenario to utilize the renewable energy harvesting to supply power for the base stations. This paper investigates the user association problem in the heterogeneous networks with hybrid energy sources, where all the base stations are powered by grid and renewable energy. The user association problem is first formulated for utility proportional fairness, aiming to achieve the energy balancing. Then an iterative algorithm operated by the users and base stations is proposed, which can converge to the global optimal solution. Simulation results demonstrate that the proposed algorithm can reduce the on-grid power consumption and achieve a flexible energy balancing.

With the diversification of mobile services, mobile traffic exhibits new characteristics which is different from traditional voice traffic. In this paper, we investigate the optimal base station (BS) density in cellular networks by taking into account the self- similar characteristics of mobile traffic using stochastic geometry approach. Based on the traffic load distribution satisfying self-similar characteristics, traffic coverage probability is defined and its relation with BS density is derived. An optimization problem with the aim of minimizing the BS density is formulated under the constraint of traffic coverage probability. Using binary search algorithm, the optimal BS density and the bounds (both upper and lower bound) for simplified analysis is obtained. The simulation results confirm the accuracy of theoretical analysis and verify the impact of the self-similar characteristics on the optimal BS density.

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.

One of key 5G scenarios is that device-to-device (D2D) and massive multiple-input multiple-output (MIMO) will be co-existed. However, interference in the uplink D2D underlaid massive MIMO cellular networks needs to be coordinated, due to the vast cellular and D2D transmissions. To this end, this paper introduces a spatially dynamic power control solution for mitigating the cellular-to-D2D and D2D-to-cellular interference. In particular, the proposed D2D power control policy is rather flexible including the special cases of no D2D links or using maximum transmit power. Under the considered power control, an analytical approach is developed to evaluate the spectral efficiency (SE) and energy efficiency (EE) in such networks. Thus, the exact expressions of SE and EE for a cellular user or D2D transmitter are derived, which quantify the impacts of key system parameters such as massive MIMO antennas and D2D density. Numerical results corroborate our analysis and show that the proposed power control solution can efficiently mitigate interference between the cellular and D2D tier.

This paper focuses on resource allocation in energy-cooperation enabled two-tier heterogeneous networks (HetNets) with non-orthogonal multiple access (NOMA), where base stations (BSs) are powered by both renewable energy sources and the conventional grid. Each BS can serve multiple users at the same time and frequency band. To deal with the fluctuation of renewable energy harvesting, we consider that renewable energy can be shared between BSs via the smart grid. In such networks, user association and power control need to be re-designed, since existing approaches are based on OMA. Therefore, we formulate a problem to find the optimum user association and power control schemes for maximizing the energy efficiency of the overall network, under quality-of-service constraints. To deal with this problem, we first propose a distributed algorithm to provide the optimal user association solution for the fixed transmit power. Furthermore, a joint user association and power control optimization algorithm is developed to determine the traffic load in energy-cooperation enabled NOMA HetNets, which achieves much higher energy efficiency performance than existing schemes. Our simulation results demonstrate the effectiveness of the proposed algorithm, and show that NOMA can achieve higher energy efficiency performance than OMA in the considered networks.

One of the key 5G scenarios is that device-to-device (D2D) and massive multiple-input multiple-output (MIMO) will be co-existed. However, interference in the uplink D2D underlaid massive MIMO cellular networks needs to be coordinated, due to the vast cellular and D2D transmissions. To this end, this paper introduces a spatially dynamic power control solution for mitigating the cellular-to-D2D and D2D-to-cellular interference. In particular, the proposed D2D power control policy is rather flexible, including the special cases of no D2D links or using maximum transmit power. Under the considered power control, an analytical approach is developed to evaluate the spectral efficiency (SE) and energy efficiency (EE) in such networks. Thus, the exact expressions of SE for a cellular user or D2D transmitter are derived, which quantify the impacts of key system parameters, such as massive MIMO antennas and D2D density. Moreover, the D2D scale properties are obtained, which provide the sufficient conditions for achieving the anticipated SE. Numerical results corroborate our analysis and show that the proposed power control solution can efficiently mitigate interference between the cellular and the D2D tier. The results demonstrate that there exists the optimal D2D density for maximizing the area SE of D2D tier. In addition, the achievable EE of a cellular user can be comparable with that of a D2D user.

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.

This paper considers the massive multiple-input multiple-output (MIMO) enabled heterogeneous cloud radio access network (C-RAN), in which both remote radio heads (RRHs) and massive MIMO macrocell base stations (BS) are deployed to potentially accomplish high throughput and energy efficiency (EE). In this network, the soft fractional frequency reuse (S-FFR) is employed to mitigate the inter-tier interference. We develop a tractable analytical approach to evaluate the throughput and EE of the entire network, which can well predict the impacts of the key system parameters such as number of macrocell BS antennas, RRH density, and S-FFR factor, etc. Our results demonstrate that massive MIMO is still a powerful tool for improving the throughput of the heterogeneous C-RAN while RRHs are capable of achieving higher EE. The impact of S-FFR on the network throughput is dependent on the density of RRHs. Furthermore, more radio resources allocated to the RRHs can greatly improve the EE of the network.

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.

This paper considers power control in energy cooperation enabled heterogeneous networks (HetNets), where each base station (BS) is powered by hybrid energy sources consisting of the conventional power grid and renewable energy sources. Energy can be transferred between BSs with energy loss during the energy transmission process. Transmit power, grid power consumption, and transferred energy are optimized for maximizing the energy efficiency of the whole network. The considered problem is formulated as a non-linear fractional programming problem. To solve it, we propose an energy efficient algorithm, in which the optimal resource allocation policy is obtained by using the lagrangian duality method. Numerical results demonstrate that energy efficiency is substantially improved by using the proposed power control algorithm with energy cooperation, compared with the cases where either power control or energy cooperation are considered.

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%.

This paper considers a hybrid heterogeneous network (HetNet), where macro cells adopt massive multiple-input multiple-output (MIMO), and small cells adopt millimeter wave (mmWave) transmissions. We assume that all base stations (BSs) are solely powered by the renewable energy. The implementation of these emerging techniques has a substantial effect on the user association (UA). Motivated by this, we formulate a user association problem to maximize the network utility while the power cost of each BS does not exceed the harvested energy. To solve it, a low complexity distributed UA algorithm is proposed. The results demonstrate that the proposed algorithm achieves higher throughput than the max reference signal received power (RSRP) and max signal-to-interference-plus-noise ratio (SINR) UAs. It also shows that increasing the number of antennas at the macro cell BS with more power consumption, the throughput continues to increase by using the proposed algorithm, compared to the decrease in throughput by using the existing ones. Increasing the number of mmWave BSs, mmWave BS antennas or mmWave bandwidths can significantly improve the throughput. Compared with massive MIMO macro cells, mmWave small cells play a dominant role in enhancing the throughput of the networks due to the larger bandwidths.

This paper exploits the potential of massive multiple input multiple output (MIMO) in K-tier heterogeneous cellular networks (HCNs), to enhance the data rate for 5G. In such a network, macro base stations (MBSs) are equipped with large number of antennas and support multi-user transmission. We first examine the impact of massive MIMO on user association in K-tier HCNs. Exact and asymptotic expressions for the probability of a user being associated with a macro cell or a small cell are derived. Based on the asymptotic analysis, the impacts of system parameters such as tier's density and BS transmit power on user association are explicitly identified. Furthermore, we derive the coverage probability and rate of the proposed network. Numerical results corroborate our analysis and show that the implementation of massive MIMO in macro cells can significantly enhance the performance of HCNs in terms of coverage and rate. A guideline for practical cellular deployment is reached that MBSs with large antenna arrays can decrease the demands for small cells.

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.

This paper investigates the resource allocation problem for the Gaussian multiple access channel (MAC) with conferencing links, where the two transmitters can talk to each other via wired rate-limited channels. Moreover, the two transmitters are powered by a shared energy harvester which captures energy from the environment. We consider both the non-causal (the energy arrival levels at future time slots are known before transmissions) and the causal (only the energy arrival levels of past and present slots are known) energy-harvesting (EH) models. For the non-causal case, we formulate a resource allocation problem over a finite horizon ofNtime slots to characterize the boundary of the maximum departure region. We then develop the optimal offline power and rate allocation scheme by exploiting the hidden convexity of this problem. Interestingly, it is shown that there exists a maximum transmission rate (named the capping rate) for one of the transmitters. For the causal case, we examine the performance of the greedy scheme, in which the energy is depleted within each slot. In particular, we measure the utility of this scheme against the optimal offline one by competitive analysis, where the competitive ratio of the online greedy scheme, i.e., the maximum ratio between the profits obtained by the offline and online schemes over arbitrary energy arrival profiles, is derived.

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.

Base station (BS) sleeping has been proved to be an effective technique for saving energy consumption in cellular networks. However, BSs in sleeping mode might cause coverage holes, which have a negative impact on the connectivity of the network. In order to overcome this problem, we propose a combined coordinated multi-point (CoMP) transmission and BS sleeping scheme under the heterogeneous networks (HetNets) scenario. The proposed scheme aims at improving energy efficiency as well as increasing coverage probability. In this paper, stochastic geometry analysis is adopted for evaluating the performance of the proposed scheme, instead of the conventional hexagonal grid based approach. Impact of CoMP on energy efficiency in HetNets with a random on/off strategy applied at macro base stations (MBSs) is examined thoroughly. We derived two performance indicators which are coverage probability and energy efficiency in a two-tier HetNets scenario. Numerical results confirm that the combined CoMP and BS sleeping can improve the energy efficiency as well as increase the coverage probability compared with implementing BS sleeping only. The impact of the density ratio of MBSs to Pico BSs (PBSs) on energy efficiency and coverage probability is also quantified.

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.

This paper investigates the optimal resource allocation for the Gaussian multiple access channel (MAC) with conferencing links, where the two transmitters could talk to each other via some wired rate-limited channels. Moreover, the two transmitters are assumed to be powered by a shared energy harvester, and a deterministic energy-harvesting (EH) model is adopted by assuming that the energy arrival times and the corresponding harvested amounts are non-causally known prior to transmissions. We formulate a continuous-time power allocation problem to characterize the maximum departure region over a finite time horizon. By exploiting its convexity, this problem is simplified as a discrete-time problem and the optimal solution is obtained. In particular, it is shown that there exists a certain maximum possible transmission rate (the capping rate) at one of the transmitters. Finally, we compare the performance of the optimal offline algorithm against that of the online one.

This paper investigates the optimal resource allocation for a Gaussian multiple access channel (MAC) with two transmitters powered by a shared energy harvester. A deterministic energy-harvesting (EH) model is adopted, which assumes that the energy arrival amounts and timing are non-causally known before transmissions. Besides, packets for both transmitters are assumed always ready before transmissions. We first formulate the resource allocation problem to characterize the maximum departure region over a finite time horizon as a convex optimization problem. The structural properties of the sum power profile is then studied by exploiting the convexity of the sum power function, which simplifies the optimization problem. Finally, the optimal resource allocation between the two transmitters, in which there exists a cut-off rate at the stronger transmitter, is obtained. We also demonstrate that under the same energy arrival profile, MAC with shared energy harvester achieves the same maximum departure region as its dual broadcast channel (BC).

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.

The optimum power allocation of the one-side interference channel with the non-cognitive relay node was studied. Assuming the orthogonal resources were used on the channels between the sources and relay node, we first derived a transmission scheme based on the dirty paper coding and the interference cancellation. Then with this transmission scheme the rates that was achievable in both the weak and strong interference regimes were given. A joint power allocation scheme among the sources and relay node was proposed, which maximized the sum-rate. The performance of the proposed power allocation scheme was proved. More explicit analysis investigated the effects of the noncognitive feature of the relay node on the power allocation and sum-rate. The relationship between the channel gains and the optimum joint power allocation had also been analyzed.

Load balancing (LB) is an important function of the self-organizing network (SON) for coping with the uneven load distribution to achieve higher spectrum efficiency and lower operational expenditure. This paper proposes a cluster based self-organizing LB scheme, which employs a user-vote mechanism to avoid the ‘virtual partner’ problem experienced by current LB schemes with the load-based partner selection. The user-vote can assist the hot-spot base station (BS) to efficiently select partner BSs for constructing its cluster, and then shift the traffic to the partners within the cluster. Simulation results show that the proposed scheme can effectively solve the ‘virtual partner’ problem. Furthermore, it can reduce the call blocking rate via a small number of partner BSs.

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.

In a distributed antenna system, the optimal antenna deployment design scheme is proposed, in which both the location and the number of the distributed antennas (called access point, AP) are considered for energy efficiency. By minimizing the average distance between users and the APs, the optimal AP distribution is given. The optimal number of APs can be obtained considering both the circuit power and the transmission power for each AP. The simulation results show that the transmission power can be reduced by optimal AP location design. The simulation also considered the trade-off between the circuit power and the transmission power consumption and the total system power can be reduced significantly.

Mitigating Inter-cell Interference (ICI) and ensuring seamless high-quality communication are two challenging issues for OFDMA systems. Cell-level coordinated resource allocation and handover (HO) are the two key technologies for achieving these goals. They have been investigated intensively, however, mainly separately. In this paper, a novel combined Handover Guarantee and Interference Mitigation (HGIM) cell-level resource allocation scheme is proposed. HGIM defines the Handover User Set (HUS) and grants higher allocation priority to handover users. Other active users are prioritized based on a unified cell division model which divides a cell into different ICI sensitive areas. Meanwhile, HGIM defines a Sub-carrier Preferred List (SPL) to optimize allocation. Simulation results show that HGIM achieves greater ICI mitigation and improved handover performance compared with the conventional soft frequency reuse scheme.

This paper proposes a new power control strategy during soft handover in WCDMA systems. According to the link qualities of the down link channels involved in soft handover, the transmit powers of the base stations in the active set are dynamically allocated. This new optimized power control scheme reduces the additional interference caused by the multiple site transmission during soft handover. Compared with the unbalanced and balanced power control scheme adopted by 3GPP, the proposed scheme shows better performance. It increases the downlink capacity and makes the whole system more robust to the fluctuating attenuation of the radio environment.

Abstract Soft handover is analysed and optimized in the downlink direction of a WCDMA system in this paper. Downlink is chosen because the asymmetric nature of most new services supported by the next generation of mobile networks requires more capacity in that direction, and so this is likely to be the limiting factor. The study also sets out to resolve the often controversial opinions about the system level performance with soft handover in the downlink direction. Here, the performance of soft handover is investigated on the link level and on the system level separately. Apart from power control, the cell‐selection scheme and power limits (which are ignored by most of the previous work in the literature) are also included. Results show that from the point of view of link quality, soft handover can prevent the downlink traffic channel power going beyond the power limit. From the system level point of view, the downlink capacity can be increased by choosing a proper soft handover overhead. The optimum value of the overhead depends closely on the cell‐selection threshold and the radio environment. The paper proves that soft handover is an effective way for interference‐reduction and radio resource optimization in the downlink direction for WCDMA systems. Copyright © 2003 John Wiley & Sons, Ltd.