ABSTRACT As a cornerstone of the internet of things, massive grant‐free random multiple access (MGFRMA) has attracted wide attention in recent years. However, due to lack of coordination, it is difficult to remove access collisions among randomly activated users, which makes the existing solutions unsatisfactory. To solve the issue, this paper introduces cognitive radio (CR) and preamble delay (PD) to reduce access collisions, together with non‐orthogonal multiple access (NOMA) to improve spectrum efficiency, and then develops a CR‐NOMA‐PD‐based MGFRMA scheme. First, a three‐step MGFRMA protocol is advanced. It helps users to obtain the channel occupancy state before uplink transmission with spectrum sensing so as to consciously avoid access conflicts. Then, an optimisation strategy for jointly selecting access channel and power level is designed according to the sensing results. The competitive transmission scheme with PD is formulated, based on which uplink signals are well modelled. Finally, a multi‐user detection algorithm involving channel filtering, power level detection, preamble detection, and data recovery is proposed. The performance of the CR‐NOMA‐PD‐based MGFRMA scheme is also analysed and simulated. Simulation results indicate that the proposed scheme improves the access ratio of users and system overload capacity significantly compared to the existing schemes. It also has better robustness and scalability.

This paper considers an integrated sensing and communication (ISAC) system, where two dual-functional base stations (BSs) serve their users and detect multiple targets. To improve detection accuracy while meeting communication quality of service, this paper proposes a two-phase cooperative target detection algorithm that relies on Capon-based adaptive beamforming and maximum likelihood estimation (MLE)-based hypothesis testing. Specifically, based on Capon’s detection results, the two BSs first scan targets with an omnidirectional beam and then track targets with a directional beam. Subsequently, multiple hypotheses regarding the locations of targets are established based on the detection results of the Capon method, and the MLE is employed for hypothesis testing to filter out ghost targets. Finally, simulation results show that the proposed algorithm achieves more precise angles-of-arrival estimation of multiple targets than conventional single-BS sensing, and enables high-precision localization by eliminating ghost targets.

In wireless-powered communication networks (WPCNs) augmented by reconfigurable intelligent surface (RIS), achieving high throughput while managing signaling overhead remains a critical challenge. Conventional approaches rely on instantaneous channel state information (I-CSI) for dynamic RIS beamforming, which leads to prohibitive channel estimation and feedback overhead in large-scale deployments. To address this issue, this paper proposes a novel two-timescale protocol that integrates statistical CSI for long-term RIS beamforming optimization and short-term I-CSI for optimizing resource allocation. In particular, the proposed method designs multiple RIS beamforming patterns using statistical information, while dynamically adjusting time and power allocation within each coherence interval based on effective I-CSI. An alternating optimization (AO) based algorithm is then developed to iteratively refine RIS phase shifts for both downlink energy transfer and uplink information transfer using gradient projection, and derive optimal resource allocation in closed-form expressions via Karush-Kuhn-Tucker (KKT) conditions. Simulation results validate the framework’s efficacy, demonstrating that using only 33% of the total RIS beamforming patterns can achieve 94% of the sum-rate performance of full I-CSI approaches, which provides useful guidelines for reducing the feedback overhead in the considered RIS aided WPCNs.

Reconfigurable intelligent surface (RIS)-enhanced secure wireless communication systems have attracted growing attention. However, RIS can also be misused to assist eavesdropping, introducing new security challenges. This paper investigates the secrecy rate degradation caused by illegal RIS (IRIS) in a typical RIS-assisted integrated sensing and communication (ISAC) system, where a dual-functional base station performs multiuser communication and radar sensing simultaneously with the help of an RIS, while an eavesdropper (Eve) leverages an IRIS for malicious interception. Specifically, two types of sensing targets are considered, including point target and extended target. The detection probability is used as the sensing performance metric for point target, while the the Cramér–Rao bound (CRB) of the complete target response matrix is used for extended target. Based on these metrics, two different secrecy rate maximization problems are formulated under either a detection probability or a CRB threshold constraint. To solve the first non-convex problem, we adopt a combination of closed-form fractional programming, minorization–maximization, successive convex approximation, and the alternating direction method of multipliers. For the second problem, semidefinite relaxation and alternating optimization are employed to obtain suboptimal solutions. Simulation results demonstrate the great potential of ISAC systems and highlight the critical role of joint beamforming and reflection design, as well as strategic RIS deployment, in mitigating potential security threats posed by the IRIS.

Rate-splitting multiple access (RSMA) is a promising non-orthogonal transmission scheme capable of achieving higher data rates with massive connectivity compared to conventional orthogonal multiple access. However, the effectiveness of RSMA can be significantly hindered by imperfect successive interference cancellation (SIC), leading to severe interference and rate degradation. Existing research has either focused on hybrid RSMA systems, where users are grouped and assigned orthogonal resources, or incorporated improper Gaussian signaling (IGS) to enhance interference management. However, no prior work has explored the combination of these approaches to flexibly manage interference under imperfect SIC. To address this gap, we propose a novel downlink hybrid RSMA system with IGS under imperfect SIC, where users are grouped into pairs to form RSMA groups. Specifically, we introduce the use of IGS for the common message within each group to effectively mitigate interference caused by imperfect SIC. The problem of optimizing the user grouping, subcarrier allocation, rate allocation, power allocation and IGS circularity coefficient, aimed at maximizing the sum rate under the minimum rate requirement, is investigated. We first develop a block coordinate descent method combined with successive convex approximation to optimize all variables except user grouping and subcarrier allocation. Subsequently, a swapping-based algorithm is proposed to refine user grouping and subcarrier allocation iteratively. Extensive simulation results validate the effectiveness of the proposed hybrid RSMA with IGS scheme, demonstrating its superior performance compared to various benchmark schemes in the literature.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems and simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are considered as promising technologies for enhancing the performance of wireless communication systems. In this paper, we investigate the performance of a STAR-RIS-aided CF-mMIMO system under channel aging, which has been ignored in previous studies. Firstly, we propose a linear minimum mean squared error (LMMSE) aggregated channel estimator and formulate statistical channel state information (CSI) properties for the subsequent system performance analyses. Then, closed-form expressions for the uplink and downlink spectral efficiencies (SEs) of the STAR-RIS-aided CF-mMIMO system under channel aging are explored, where for the uplink, the two-layer large-scale fading decoding (LSFD) and the simple centralized decoding (SCD) are utilized, respectively, and for the downlink, the maximal ratio (MR) precoding and fractional power control (FPC) are adopted. Moreover, the optimal LSFD coefficients that maximize the uplink SE is presented. Afterwards, for further enhancement of SEs, a novel optimization scheme is presented, which optimizes the passive beamforming (PB) of the STAR-RIS to minimize the normalized mean square error (NMSE) of the aggregated channel estimation. The simulation results reveal that the STAR-RIS-aided CF-mMIMO system achieves superior uplink and downlink performance compared to both the RIS-aided CF-mMIMO system and the conventional CF-mMIMO system without RIS over aging channel. Furthermore, the results show that the PB optimization can significantly reduce the NMSE of channel estimation, thereby improving the estimation accuracy and SEs under channel aging.

The convergence of communication, sensing, and computing capabilities is a key trend in future 6th generation mobile (6 G) networks. Integrating unmanned aerial vehicles (UAVs) with federated learning can further enhance network performance in these areas while reducing resource overhead and protecting data privacy. However, due to limited spectrum resources and data heterogeneity, lack of client scheduling not only increases bandwidth pressure but also degrades training performance. Moreover, incentive allocation in federated learning directly influences whether UAVs accept client selection and participate in collaborative learning tasks. In order to solve the above problems, this paper designs an incentive assignment scheme for UAV clients in federated intelligent inspection based on communication-sensing-computing integration. This scheme comprehensively considers two dimensional metrics, client data quality and contribution value, for UAV incentive allocation and selection, hence abbreviated as the Multi Dimensional Scheme (MDS). MDS accounts for the communication, sensing, and computational energy consumption of UAVs, establishing a federated learning candidate pool through contract theory. Subsequently, UAVs that contribute more to model training are selected from the candidate pool via Bayesian optimization. Experiments conducted on multiple datasets show that, compared to existing methods, MDS effectively improves the accuracy of model training while reducing incentive costs.

Suffering from serious rate degradation, how to improve transmission rate with low latency is a challenging issue in ultra-reliable and low-latency communications (URLLC), especially when there is no enough blocklength for data transmission. To handle this issue, we propose to integrate the reconfigurable intelligent surface (RIS) and superimposed pilot (SP) into massive multiple-input multiple-output (mMIMO) systems, where the SP ensures latency by simultaneously sending pilot and data while the RIS improves high transmission rate by reflecting the SP signals. Practically, we derive the finite blocklength ergodic achievable rate lower bound in closed form under imperfect channel estimation and pilot interference removal. Then, we maximize the weighted sum rate of all the users by jointly designing the power control of SP at each user and the phase shifts at the RIS. Due to the highly coupled variables, we first decompose the original problem into the phase shift design subproblem and the power control design subproblem, which are resolved by a genetic algorithm (GA) and an iterative algorithm based on geometric programming (GP). Then, a block coordinate descent algorithm is proposed. Correspondingly, the complexity and convergence of the proposed algorithms are analyzed. Finally, our numerical results demonstrate that the joint design scheme can bring effective rate improvement in stringent latency constraints.

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, assisted by an existing receive access point. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme, where the beamforming and placement of the UAV are dynamically adjusted in different stages. Specifically, in the first stage, without prior knowledge about the target’s location, the UAV fixes at the initial location and performs wide beam sensing to probe the target. In the following stages, given the coarse estimation result of the target’s location (obtained in the previous stage), the UAV adjusts its location and performs narrow beam sensing to locate the target. Besides, the quality of service requirements of the users are guaranteed in all stages. Based on the proposed sensing scheme, we formulate and solve two optimization problems to improve the sensing accuracy. Finally, numerical results demonstrate the effectiveness of the proposed algorithm.

Content caching at base stations (BS) can reduce backhaul traffic delays to deliver the requested files to users, but its effectiveness is limited by BS storage capacity. We propose a novel approach that integrates artificial intelligence-generated content (AIGC) into the BS operations. Instead of caching entire files, our AIGC-enhanced BS can cache smaller prompts, allowing files to be reconstructed on demand. We explore the challenge of jointly optimizing hybrid caching, AIGC computation, and communication resource allocation with the goal of minimizing average system latency. Given the non-convex nature and the complexity of mixed integer non-linear programming involved, we propose a divide-and-conquer algorithm that breaks down the problem into two timescale levels. Theoretical analysis and simulations confirms that our AIGC-enhanced hybrid content caching outperforms the conventional content caching.

Federated learning for electric vehicle suspension (FLEVS) is a key technology to address the limitations of battery capacity and the rising use of sensing cameras in electric vehicles. However, current FLEVS systems face challenges such as energy constraints, privacy risks, and copyright issues in car enterprise cloud (CEC) trained models. This paper proposes a frozen watermark-based federated learning incentive mechanism to tackle these issues. It incorporates a global frozen watermark to leverage continuous learning while minimizing training interference. Additionally, a Stackelberg game-based incentive mechanism between the CEC and mobile vehicles (MVs) optimizes information security strategies. The derived optimal reward function and iteration parameters are validated through numerical results, demonstrating the scheme's effectiveness.

Cell-free multiple-input-multiple-output generally uses a star topology for superior communication but faces high costs due to long cables. An economical alternative, the stripe topology, is suitable for specific deployments but cannot meet user demands in densely populated areas due to limited fronthaul capacity. To address these limitations, we propose a hybrid network structure combining stripe and star topologies, ensuring system performance while reducing deployment costs. In such a network, joint precoding and fronthaul compression is considered to maximize system sum-rate and an alternating optimization (AO) algorithm is proposed. However, the AO algorithm involves an iterative process and complex matrix calculations, making it unsuitable for practical applications. To deal with this issue, we propose a low-complexity iterative gradient descent (IGD) algorithm with simple matrix operations. To further reduce online computational complexity, we propose a novel deep unfolding neural network (DUNN) scheme, which is interpretable and scalable, based on the IGD algorithm. Simulation results show that the hybrid topology significantly improves system capacity compared to the stripe-only topology. Additionally, the DUNN achieves a tradeoff between the achievable sum-rate performance and the corresponding computational complexity.

Thanks to the customization of channel propagation, reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (MIMO) is recognized as a competitive candidate technique for the future communication system. However, only the limited performance gain can be afforded by passive RIS due to the double-fading effect in RIS-aided links. In this article, we consider the CF massive MIMO system with the assistance of the active RIS, which is capable of reflecting and amplifying the incident signal, to enable the Internet of Things network. We analyze the tradeoff between the number of active RIS reflecting elements (REs) and the amplification coefficient. Considering the power constraint at the active RIS, we formulate a sum-rate maximization problem to jointly optimize the user transmission power, the receive beamforming, and the RIS reflecting precoding. Since the original problem is nonconvex, we decouple it into three subproblems and then design an alternating optimization algorithm to solve them iteratively. Using the Lagrangian dual reformulation and generalized Rayleigh quotient theory, we derive the closed-form solutions for both the user transmission power and the uplink receive beamforming. We also develop a low-complexity method to acquire the RIS reflecting precoding based on the primal-dual subgradient theory. Compared to the passive RIS, the active RIS can significantly improve the system performance with fewer REs. Moreover, the proposed optimization scheme effectively mitigates the drawbacks of the active RIS under the high-transmission power regime and enhance its benefits. Finally, the proposed alternating optimization algorithm is validated by numerical results.

In view of super-large scale access and dynamic connectivity requirements in 6G, the number of users and data is increasing exponentially, which makes it difficult to achieve sustainable development of communications. Meanwhile, with the development of cross-modal processing, semantic information is highly considered accurate and intelligent, which is expected to bring new ideas for 6G. Therefore, in this study, we propose a novel framework of cross-modal semantic communications to improve the efficiency of the multimodal processing, especially the tactile modal. Meanwhile, we have redesigned the significant aspects of artificial intelligence (AI), such as encoding, transmission, and processing. As federated learning (FL) inherently supports multiple privacy-preserving and security measures, we also introduce FL to assist AI training in cross-modal semantic communications, including the expansion of multimodal data, cross-modal semantic extraction, comprehensive decision-making, and privacy protection. First, in encoding aspects, we propose a hybrid coding for haptic signal coding by deep learning (DL) according to the semantic association between material identification and tactile code optimization. Second, in transmission aspects, we propose a modal-aware resource allocation for the fairness optimization between transmission requirements and network resources with deep reinforcement learning (DRL). Third, in signal processing, low-rank signal reconstruction and immersive quality of experience (QoE) evaluation by DL and machine learning (ML) are provided to improve and quantify users’ experience. In addition, the computational experiments of those key technologies are shown individually and the specification of their probability is discussed separately. Finally, future research topics related to the above issues are suggested.

In the 5G era, the demand for high-quality communication services has rendered spectrum resources increasingly scarce, particularly for remote users (UEs). Unmanned Aerial Vehicles (UAVs) provide a viable solution to this challenge by facilitating dynamic spectrum sharing. This paper proposes a hybrid two-stage Stackelberg game model that enhances UAV-assisted communications by integrating both static and dynamic spectrum-sharing strategies. In this model, UAVs serve as relays for base stations (BSs) to serve remote UEs. They can optimize spectrum allocation and negotiate pricing directly with UEs instead of BS, thereby reducing the burden on BS. The model operates in two stages: In the first stage, UAVs act as followers with BS as leaders; In the second stage, UAVs become leaders in interactions with remote UEs. Our analysis focuses on the effects of spectrum sharing and pricing strategies on system performance, emphasizing the enhanced utility for BS, UAVs, and UEs. We demonstrate that the proposed model significantly improves spectrum efficiency, particularly under high-demand conditions, and maintains stable and efficient operations within UAV-assisted communication systems, as supported by simulation results.

To address the low efficiency of traditional spectrum-sharing systems, UAVs can serve as airborne relays to enhance user communication services. However, the issues of spectrum scarcity and underutilized spectrum holes have not been fully resolved. Therefore, designing an effective spectrum resource reallocation mechanism is essential to improve spectrum utilization efficiency further. Moreover, existing research rarely explores co-channel interference arising from spectrum trading between UAVs and often overlooks queuing mechanisms in spectrum trading scenarios. This paper proposes a dynamic spectrum-sharing scheme (DSS) leveraging UAV-assisted communication to address these challenges. To enhance spectrum utilization, a two-level Stackelberg game-based incentive mechanism is developed for on-demand UAV spectrum trading. Additionally, a co-channel interference preference-based spectrum matching scheme (CIPS) is designed, which comprehensively considers co-channel interference resulting from spectrum sharing and prioritizes the importance of UAV users’ services. Finally, optimal pricing and trading volume strategies are efficiently determined using a gradient-based iterative search algorithm. Simulation results demonstrate that the proposed model achieves higher system revenue, which is 8.97% to 186.82% higher than other models, and ensures flexible spectrum allocation and effective co-channel interference mitigation.

The development of Industrial Internet of Things (IIoT) applications presents a critical challenge in terms of latency limitation, particularly considering the limited availability of resources that prevent a single fog device from fully executing large-scale computing tasks. In such scenarios, enabling distributed computing across multiple fog servers or collaborating with cloud servers holds promising potential. To improve the efficiency of task offloading while accounting for the crucial role of movable fog devices (e.g., robots and unmanned cars), we formulate a joint optimization problem as a partially observable Markov decision process (POMDP), incorporating offloading decisions, computing resource allocation, and trajectory optimization under constraints related to available resources and collision avoidance. Due to the nondeterministic polynomial-time hardness (NP-hardness) in the problems of task offloading and resource allocation, we reformulate a matroid-constrained submodular maximization problem and propose an iterative low-complexity algorithm to find solutions. Subsequently, extracting better solutions from submodular optimization, we propose a multiagent reinforcement learning (MARL)-based algorithm to solve the trajectory optimization problem for the movable fog devices acting as agents, making decisions based on their local observations. Finally, simulation results have validated that the proposed scheme has a superior performance compared to the baselines.

We investigate the spectral and energy efficiencies of the uplink in an integrated satellite-terrestrial cell-free massive multiple-input multiple-output (IST-CF-mMIMO) system assisted by rate-splitting multiple access (RSMA). In the IST-CF-mMIMO system, the terrestrial users employ RSMA to transmit a message as a superposition of two parts with different power to the terrestrial access points and low-Earth-orbit satellite. Taking realistic conditions such as the spatially correlated Ricean fading channels, imperfect channel knowledge, and successive interference cancellation into account, we derive rigorous closed-form expressions for uplink achievable spectral and energy efficiencies and evaluate these performance metrics across a range of system configurations. Additionally, to enhance the system energy efficiency, we formulate the design of users’ power control coefficients as an energy efficiency optimization problem and design an efficient algorithm based on Lagrangian dual transformation and quadratic transformation techniques to solve it. Comprehensive simulations validate our theoretical propositions and evaluate the efficacy of the proposed energy efficiency maximization algorithm.

In Industrial Internet of Things (IIoT), timely detection of equipment failures and predictive maintenance are crucial. Leveraging Federated Learning (FL) allows for distributed model training on inspection devices, enabling predictive maintenance without compromising data privacy. However, Traditional FL faces communication and scalability challenges in large scale industrial scenarios. While hierarchical federated learning (HFL) improves flexibility, it struggles in signal-unstable scenarios. This paper proposes a UAV-assisted HFL framework for distributed remote inspection in IIoT, where UAVs enhance communication via high-altitude links and act as edge servers to collect and aggregate model parameters, reducing the central server’s communication burden and improving training efficiency. In this framework, energy-constrained edge clients face challenges of energy efficiency and data silos, while UAV deployment and energy limitations must also be addressed. To optimize fair and energy-saving training, we formulate an optimization problem to minimize energy consumption based on communication and training costs. This is decomposed into two sub-problems: (1) client selection, tackled as a multi-objective optimization using a MAB-based algorithm with a customized reward function balancing energy use and fairness; (2) UAV scheduling, addressed with a heuristic algorithm to optimize edge server deployment. Combining these schemes enables efficient scheduling for large-scale IIoT inspections. Finally, simulation experiments demonstrate the proposed strategy’s significant advantages in reducing system energy consumption, enhancing model accuracy, and improving fairness.

In cell-free massive multiple-input multiple-output (CF-mMIMO) systems, seamless communication coverage is achieved through the dense deployment of numerous access points (APs), significantly enhancing spectral efficiency (SE) for users and overall system capacity. However, the implementation of this approach demands substantial deployment costs and unavoidably necessitates the use of non-ideal hardware. This paper investigates the achievable rate of users in the uplink CF-mMIMO systems that employ nonlinear power amplifiers (PAs) and low-resolution analog-to-digital converters (ADCs) at user equipment (UE) and APs, respectively. In particular, we derive a closed-form expression for the achievable uplink user rate and conduct a comprehensive analysis of various factors, including the number of APs, UE density, number of AP antennas, and ADC resolution. To mitigate the interference among UEs and maximize the sum rate, we propose a graph neural network (GNN) assisted actor-critic algorithm (DMAGNN-AC) for power allocation. The established framework overcomes the representation bottleneck of DRL in high-dimensional unstructured state spaces and provides physically interpretable feature embeddings. In comparison to the full power output, the proposed power allocation scheme is capable of doubling the rate. Furthermore, to address the detrimental impact of low-resolution ADCs on the rate, we develop an enhanced algorithm, multi-agent deep Q-integrated network (MADQIN), which optimizes the allocation strategy of ADC resolutions. Finally, the effectiveness of the proposed schemes is validated by the presented simulation results.

This paper concentrates on the synergetic effect of the hybrid multicast/unicast/device-to-device (D2D) transmission for downlink cell-free massive multiple-input multiple-output (MIMO) Internet-of-Things (IoT) systems. By leveraging on the acquired imperfect channel state information (CSI) both for multicast, unicast, D2D links, particularly, we first derive the closed-form solutions for the multicast, unicast, and D2D transmission links, respectively. After that, based on the practical power consumption model, the achievable sum energy efficiency (EE) analysis is conducted as well by exploiting the achievable sum rate. At last, extensive simulation results are provided to validate the performance of the proposed framework. The obtained results are given to provide promising preliminary insights on the potential of deploying multicast/unicast/D2D in the cell-free massive MIMO topology. It is revealed by the above analysis that some guiding rules of the practical deployment of future. It is noteworthy that when we select the 5, 6, and 8 bits, we can achieve the maximum sum EE, the tradeoff from the total power consumption to the sum rate, and the maximum sum rate, respectively.

As one of the most critical tasks currently, it is essential to excavate the status update performance. In this paper, we combine access control and pilot design to minimize average age of information (AoI) in cell-free (CF) massive multiple-input multiple-output (MIMO) system with grant-free (GF) random access, where the positions of both the access points (APs) and machine-type communication devices (MTCDs) are geographically distributed as independent Poisson point processes (PPPs). Based on the establish two-disk computation model, we frist formulate the access successful probability by leveraging the stochastic geometry for GF transmission. Then, the closed-form expression of the average AoI is approximately derived. The derived results enable us precisely quantify the impact of network parameters on the system-level performance. To minimize the average AoI, a joint access control and pilot design scheme is proposed. Finally, simulation results are provided to furnish invaluable insight into system performance and certificate the validity of the proposed algorithm.

In the time-division-duplexing (TDD)-based cell-free (CF) massive multiple-input multiple-output (MIMO) system, the channel reciprocity needs to be recovered via a reciprocity calibration operation due to the random circuit impact on the transceiver radio frequency. In this paper, we study the effect of the calibration error on the TDD CF massive MIMO system under non-ideal calibrations. Assuming the spatially correlated Ricean fading channel, we derive the closed-form expression of the downlink achievable rate, which takes both the channel estimation error and the calibration error into account. Some novel insights of the calibration error in the CF massive MIMO system are gathered from the analytical results. It is shown that the downlink achievable rate is more sensitive to the calibration error at the user side. In order to provide a uniformly good service for each user, we employ the geometric programming (GP) to solve the max-min power optimization problem to maximize the minimum user rate. Additionally, we utilize the scaled alternating direction method of multipliers to develop a calibration error-aware beamforming scheme to mitigate the impact of calibration errors, improving the downlink sum-rate. Numerical results demonstrate that the proposed GP-based algorithm significantly improves the 95%-likely per-user downlink achievable rate with a fast convergence behavior. Moreover, the proposed calibration error-aware beamforming scheme enhances the downlink sum-rate and outperforms other benchmark schemes.

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits uninhabited aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost.

In this paper, we study the transmit and receive beamforming vectors in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) performs the downlink communication with user equipments (UEs) and active sensing tasks simultaneously. While reflected signals are utilized for passive sensing at the receive access points (RAPs). We adopt different fusion strategies based on the backhaul capacity between the RAPs and BS. Specifically, in the scenarios with unlimited backhaul capacity, the sensing signals received by the BS and RAPs are forwarded to the central controller (CC) for signal fusion. In contrast, in the scenarios with limited backhaul capacity, the BS and each RAP make independent decisions and transmit their binary inference results to the CC for result fusion. Furthermore, we explore two cases of the signal-to-interference-plus-noise ratio (SINR) with and without the sensing interference cancellation (Case-1 SINR and Case-2 SINR). By optimizing the beamforming vectors according to different fusion strategies, we aim to maximize sensing performance while ensuring the minimum SINR requirement for the UEs subject to the power budget at the BS. Finally, numerical results demonstrate that the proposed beamforming optimization schemes can reach the upper bound of performance under both fusion strategies. It is also shown that adding the sensing signals generally improve the sensing performance in Case-1 SINR.

Clustered vehicular federated learning (CVFL) facilitates data sharing and collaborative decision-making among vehicles, thus refining traffic behavior and demonstrating the immense potential for transforming intelligent transportation systems into a reality. However, non-independent and identically distributed data and diverse model requirements among vehicular clients hinder the feasibility of a one-size-fits-all model. Besides, “selfish” vehicular clients may be unwilling to participate in learning tasks because of the huge resource consumption of the training process. To address these challenges, in this paper, we first group local models using the adaptiveK-means-based model grouping method and then aggregate the models within each group to generate CVFL models for subsequent multi-model training. Secondly, we propose a dynamic matching-based clustering method based on the local data quality and similarity to achieve efficient vehicular client clustering. Subsequently, a meticulously crafted hierarchical incentive mechanism, grounded in a three-stage Stackelberg game, is introduced to incentivize both cluster heads and members in a layered fashion, with the initiation stemming from the CVFL server. To determine the optimal strategies for the three-stage game, an iterative algorithm is proposed, and near-optimal analytical solutions are obtained with reduced complexity. The simulation results demonstrate that our CVFL system, augmented with the hierarchical incentive mechanism, can effectively motivate multiple clusters to train multiple models in parallel, thus improving overall efficiency.

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, with the aid of an existing receive access point (RAP). By fusing the measurement results of the UAV and RAP, the location of the target is estimated. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme. Specifically, in the first stage, in the absence of prior knowledge about the target’s location, the UAV fixes at the initial location and adjusts the beamformer to perform wide beam sensing to probe the target. In the following stages, with the previous coarse estimation result of the target’s location, the UAV performs narrow beam sensing by jointly adjusting the placement and also transmit beamformer. Besides, the quality of service requirements of the users are guaranteed in all stages. Accordingly, optimization problems are formulated for the first and following stages, respectively. By involving the semidefinite relaxation technique and then solving a quadratic semidefinite programming problem, the solution in the first stage is obtained. In the following stages, we jointly apply the alternating optimization, successive convex approximation, trust region, and also Dinkelbach’s methods to address the intricate coupling between the UAV placement and beamformer. Finally, numerical results demonstrate the effectiveness of the proposed algorithms.

Drones are evolving into highly capable and adaptable devices, prompting the development of advanced control frameworks. This paper introduces a novel online control framework tailored for a multi-task drone, explicitly addressing the simultaneous execution of in-situ sensing and goods delivery. To tackle this complex scenario, a finite-horizon Markov decision process (FH-MDP) is formulated to ensure not only the prompt delivery of goods but also the minimization of energy consumption and the maximization of the drone's reward for in-situ sensing. A significant contribution lies in establishing the monotonicity and subadditivity of the FH-MDP. This mathematical foundation provides evidence for the existence of an optimal, monotone, deterministic Markovian policy. The crux of the optimal policy revolves around flight distance- and time-related thresholds, determining the precise points at which the drone should switch its optimal action. This unique feature empowers the multi-task drone to make real-time decisions, such as adjusting flight speed or engaging in in-situ sensing, by comparing its current state with these predefined thresholds. This process can be accomplished with a linear complexity, ensuring efficiency in decision-making. The optimality of our approach is rigorously demonstrated through numerical validation, where it is compared against a computationally expensive, dynamic programming-based alternative. Under the considered simulation settings, our approach reduces drone energy consumption by a substantial 19.8% compared to existing benchmarks. This not only highlights the practical effectiveness of the proposed framework but also underscores its potential for significant advancements in the field of drone operations and energy efficiency.

Content caching is a promising solution to overcome the backhaul traffic delay issue by caching content at the base station (BS). However, the performance of content caching is restricted by the limited BS cache storage. In this paper, we are the first to employ artificial intelligence generated content (AIGC) to enhance the content caching performance by empowering the BS’s intelligence capability. Besides caching a popular file, our AIGC-enhanced BS can alternatively cache its prompt of smaller size to reconstruct the file whenever needed by mobile terminals. We reveal the fundamental tradeoff between caching storage saving and computation delay for AIGC to decide whether to cache a file or its prompt. In this regard, this paper investigates the new problem of joint hybrid caching, computation and communication resource allocation optimization to minimize the average system latency to serve mobile terminals. As the problem is a non-convex and involves mixed integer non-linear programming, we develop a divide-and-conquer algorithm to decompose the problem into two timescale levels. We theoretically prove that our AIGC-enhanced hybrid content caching outperforms the conventional content caching once the computation capacity is non-trivial. Extensive simulation results also validate the superiority of the proposed hybrid content caching.

Wireless federated learning (WFL) enables devices to collaboratively train a global model via local model training, uploading and aggregating. However, WFL faces the data scarcity/heterogeneity problem (i.e., data are limited and unevenly distributed among devices) that degrades the learning performance. In this regard, artificial intelligence generated content (AIGC) can synthesize various types of data to compensate for the insufficient local data. Nevertheless, downloading synthetic data or uploading local models iteratively takes a lot of time, especially for a large amount of devices. To address this issue, we propose to leverage non-orthogonal multiple access (NOMA) to achieve efficient synthetic data and local model transmission. This paper is the first to combine AIGC and NOMA with WFL to maximally enhance the learning performance. For the proposed NOMA+AIGC-enhanced WFL, the problem of jointly optimizing the synthetic data distribution, two-way communication and computation resource allocation to minimize the global learning error is investigated. The problem belongs to mixed integer nonlinear programming, whose optimal solution is intractable to find. We first employ the block coordinate descent method to decouple the complicated-coupled variables, and then resort to our analytical method to derive an efficient low-complexity local optimal solution with partial closed-form results. Extensive simulations validate the superiority of the proposed scheme compared to the existing and benchmark schemes such as the frequency/time division multiple access based AIGC-enhanced schemes.

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). Considering the limited capacity of backhaul links, the signals received at the RAPs cannot be sent to the central controller (CC) directly. Instead, a novel metric of result aggregation for IAPS is proposed. Specifically, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at minimizing the probability of error at the CC under communication quality of service constraints, an algorithm of power optimization is proposed. Finally, numerical results validate the positive effect of dedicated sensing symbols and the potential of the proposed IAPS scheme.

Traditional cell-free massive multiple-input multiple-output (CF-mMIMO) systems face challenges of resource scarcity, cognitive limitations, and coverage blind spots, which primarily stem from the extensive deployment of long cables connecting each access point to the central processing unit in the system. To maximize the minimum achievable user rate and enhance the performance of a downlink CF-mMIMO system, we propose an innovative scheme that jointly integrates user association, unmanned aerial vehicle (UAV) placement, and transmission power allocation, with UAV-satellite assisted. The scheme also considers stringent constraints, including maximum power capacities, cross-layer interference limitations, and essential coverage demands. Confronting the complexity of the initial non-convex optimization challenge, we dissect it into more tractable sub-problems that encompass user association, UAV placement, and power allocation. Our approach employs an iterative algorithm, systematically resolving these sub-problems in sequence. Simulation results conclusively demonstrate the efficacy of the proposed scheme in optimizing system resource allocation and achieving comprehensive coverage.

In future 6G vehicular networks, precise positioning is essential for improving communication quality and efficiency. This paper proposes a novel TDOAIFDOA-based cooperative localization method among multiple vehicles. By selecting anchor vehicles within the range of the base station and utilizing their received echo information, this method enables efficient and low-latency positioning. First, based on a defined variable selection criterion, a subset of vehicles with optimal locations is chosen. Using the echo signals generated by inter-vehicle communication, the method jointly predicts the motion parameters of target vehicles. A time-delay Doppler approach based on matched filtering is employed to estimate the reflected echo information for dynamic vehicle cooperation, ultimately assisting the base station in achieving directional communication with the target vehicles. Results show that under specified noise conditions, the proposed V2V cooperative localization achieves performance close to the CRLB lower bound, with deviations between 0 and 0.8. This method offers a new approach to directional communication in vehicular networks, particularly suited for high-dynamic, high-concurrency large-scale vehicle communication in complex traffic environments.

Federated Learning (FL) is a new way of training models used in Internet of Things (IoT) systems. It is a method that maintains the privacy of client devices while improving model accuracy and reliability. However, there is a problem in FL applications due to the lack of incentives. Clients have different motivations and produce different quality datasets, which leads to a divergence in the quality of local models uploaded to the central server. To address this issue, we propose a new incentive model based on the Stackelberg game. The mechanism we suggest distributes rewards based on the quality of the models uploaded to the server by each client, rather than the amount of data trained. We transform the model into two optimization problems, and we propose a linear complexity algorithm to solve them. This algorithm can achieve the optimal solution and greatly reduce computational complexity, as shown in our experimental results.

Incentive mechanism is an effective approach to encourage user participation in the Federated Learning (FL) process and improve training efficiency. However, current research often focuses on scenarios with complete information or weak information asymmetry between the server and users, and few studies consider incentive mechanism design in strong asymmetric information scenarios. Meanwhile, most works assume that users' resource contributions to model performance are independent of each other, which is not consistent with practical situations. To tackle these challenges, we design an incentive contract tailored for scenarios with strong information asymmetry. Our contract leverages the probability distribution of user types to ensure its appropriateness. Furthermore, taking into account the correlation of the users' resource contributions, we propose an iteration algorithm to determine the set of optimal contract items that satisfy the constraints of individual rationality (IR) and incentive compatibility (IC). Our simulation results show that our contract can effectively motivate multiple users to take part in the training process, enabling the server to achieve utility close to those in weak asymmetric information scenarios while maintaining robustness.

Abstract Opportunistic spectrum access (OSA), a promising technology to resolve radio spectrum scarcity, is still faced with some challenges, of which one is potentially frequent channel handoff (CH) even handoff blocking (HB) for secondary user (SU) to avoid primary user (PU). Reaccess is a direct way to resolve HB. However, multiple handoff blocking‐then‐reaccess (H‐BTR) are time consuming. Whether it is worthwhile to make multiple H‐BTR, is an interesting but hardly mentioned issue. To this end, the multiple H‐BTR‐based OSA are focused on here. Three key indices, the average transmission probability, handoff delay, and average reaccess times of SU in the H‐BTR‐based OSA, are deduced as close form first. Then, the effects of H‐BTR frequency and service traffic rate of SU on OSA are discussed. Finally, the H‐BTR‐based OSA is compared to two other schemes, the handoff without BTR (HWBTR)‐based OSA and the stop‐and‐waiting (SW)‐based OSA. Theoretical and simulated results show that the H‐BTR‐based OSA performs best among three schemes. Making H‐BTR in moderate frequency according to service traffic rate can increase transmission opportunity while additional delay of SU is tolerable, thus the performance of OSA can be well improved.

Abstract In recent years, the Integrated Satellite Aerial Terrestrial (I‐SAT) network has garnered significant attention as an innovative and integrated communication system. However, it still encounters interference in the face of the complex external environment. In this context, reconfigurable intelligent surface (RIS) provides a key way of solving this problem and effectively improves the performance and stability of the I‐SAT network. This article considers the combination of unmanned aerial vehicle (UAV) and RIS and proposes a novel architecture for sub‐connected active RIS (ARIS) under the energy consumption constraints of UAV and ARIS. The authors first provide a UAV‐ARIS based position prediction strategy for the vehicle. Then, a joint RIS phase shift, amplification and UAV trail optimization algorithm is proposed to pursue a high achievable rate. The interference between each link and the total energy consumption are all taken into consideration. In addition, a deep deterministic policy gradient (DDPG) algorithm is utilized for the optimization problem, and achieves convergence in continuous action space. Finally, the simulation results affirm the precision of the proposed method in significantly enhancing performance compared to other schemes.

In this paper, we consider the cell-free massive multiple-input multiple-output (MIMO) system for supporting ultra-reliable and low-latency communication (URLLC) transmission, where a large number of access points (APs) serve a small number of users in the short packet regime. Assuming channel aging and channel spatial correlation, we derive the closed-form expression of the downlink achievable rate with the normalized conjugate beamforming (NCB). Under the goal of maximizing the minimum user rate, we formulate a max-min power optimization problem with a power constraint at each AP. However, it is challenging to solve this problem because the objective function is a complicated function of power coefficients. To tackle this difficulty, we use a path-following method to approximate the objective function to a logarithmic function and transform the polynomial constraint into a monomial. Thus, we can iteratively solve the original problem by reformulating it as a series of geometric programming problems. Numerical results verify the tightness of the closed-form expression for the downlink achievable rate in the short packet regime. Both channel aging and channel spatial correlation significantly degrade the system performance of CF massive MIMO URLLC systems. Moreover, Using NCB and the proposed max-min power allocation can effectively alleviate this impairment and improve the system performance.

In the context of Internet of Things, this letter considers a cell-free (CF) massive multiple-input–multiple-output (MIMO) system for ultrareliability and low-latency communication (URLLC) assisted by multiple reconfigurable intelligent surfaces (RISs). We derive the closed-form expression of the downlink achievable rate under multiple correlated RISs and pilot contamination. To mitigate the impact of pilot contamination and improve the fairness among users, we minimize the maximum normalized mean-squared error (NMSE) of the channel estimation by jointly optimizing the pilot power coefficient and the RIS phase shifts. Due to the nonconvexity of the original problem, we design an alternating optimization algorithm to solve the substitutable two subproblems using fractional programming and sequential convex approximation. Numerical results validate the proposed algorithm in terms of decreasing the maximum user NMSE and converging. Moreover, the 95%-likely per-user downlink achievable rate is also improved.

The Industrial Internet of Things (IIoT) is one of the typical application scenarios in the beyond fifth generation (B5G) wireless communication systems. Due to numerous metal obstacles and machines, the industrial channel, especially at the millimeter-wave (mmWave) bands, exhibits complex characteristics that have not been considered in existing literature. This article proposes an innovative 3-D nonstationary geometry-based stochastic model (GBSM) for IIoT scenarios at mmWave bands. In the proposed model, device reflections (DRs) caused by massive metal machines are modeled based on geometrical optics. Furthermore, the generalized extreme value (GEV) distribution and generalized Pareto (GP) distribution are used to parameterize the number of clusters and rays within a cluster, respectively. Further, the Doppler shift is modeled and analyzed using the Gaussian distribution. Some channel statistical characteristics are captured by the proposed model, such as the power delay profile, root-mean-square delay spread, root-mean-square angle spread, intercluster delay, and space–time–frequency correlation function. Then, these channel statistical characteristics are well fitted to the ray-tracing simulations and the channel measurements. The excellent fitting results demonstrate the high accuracy of the proposed model, which is crucial for future IIoT communication system design. What is more, this article shows the antenna height and propagation scenarios can significantly affect the DR ratio, which should adapt to various IIoT communication scenarios.

Unmanned aerial vehicle (UAV) swarms have found widespread applications in executing high-complexity and remote-risk missions. However, the limited onboard resources and energy of UAV swarms may hinder their support for computation-intensive yet delay-sensitive applications, especially when faced with exponentially growing big data. This article focuses on investigating a UAV swarm-assisted aerial–ground collaborative computing system, where one UAV swarm is divided into different groups and collaborates with a remote ground base station (BS) to provide computation services for ground smart mobile devices (SMDs). A comprehensive optimization framework is presented to maximize the system’s computation efficiency by jointly designing group formation, UAV trajectories, and resource allocation. The formulated problem involves a fractional structure with nonlinear coupling of different variables, rendering it highly nonconvex. To address this challenge, we propose a Dinkelbach-based looped iterative optimization (DLIO) algorithm. Specifically, Dinkelbach’s method is initially adopted to reformulate the original problem into a parametric structure, which is then decomposed into subproblems for group formation, resource allocation, and UAVs’ trajectory scheduling. Subsequently, these subproblems are addressed by a looped iterative optimization (LIO) algorithm. The outer loop determines group forming and resource allocation, while the inner loop utilizes the method of successive convex approximation (SCA) to solve trajectory scheduling for UAVs. Simulation results validate the effectiveness of our proposed DLIO algorithm, ensuring rapid convergence and significant improvements in the system’s computation efficiency compared to other benchmarks.

With rapid adoption in vertical industries and further assistance of edge computing, Internet-of-Things (IoT) applications are experiencing phenomenal growth. However, the concurrence of heterogeneous IoT devices, limited system resources, and varying network conditions poses an ultimate challenge to resource scheduling for meeting the increasingly diverse requirements of IoT applications. Most existing resource scheduling techniques are achieved using common performance indicators for all devices as the optimization objective, which may lose effectiveness when dealing with the diverse requirements across heterogeneous IoT devices. Towards this end, we focus on enhancing IoT device-specific Quality of Experience (QoE) through jointly optimizing communication and computation resources. First, a three-layer QoE assessment model is constructed to characterize the general correlation between resource provisioning and device-specific QoE. Then, to maximize the overall QoE amongst IoT devices, a two-stage resource scheduling scheme is proposed to realize the simultaneous optimization of IoT devices and the edge system. Specifically, during stage I, a distributed resource scheduling algorithm with low complexity is designed for each IoT device to optimize the local computing rate by considering its resource-constrained nature. During stage II, a Proximal Policy Optimization (PPO)-based online learning approach is proposed on the edge system to schedule communication bandwidth and optimize computational rate. Finally, extensive experiments demonstrate that our proposal outperforms the existing works from the perspective of QoE performance.

This letter establishes a novel transmission framework that amalgamates downlink (DL) training with rate-splitting multiple access, thereby being expected to enhance the spectral efficiency (SE) of a cell-free massive multiple-input multiple-output enabled Internet of Things (IoT) system. Considering a correlated Ricean fading environment coupled with imperfect channel knowledge, we derive a closed-form expression for the achievable SE and evaluate the DL SE under a variety of system configurations. Our comprehensive simulations corroborate the theoretical findings and yield critical insights pertinent to the system’s architectural design.

Fault prediction is an important application in the Industrial Internet of Things (IIoT) to ensure the safety of industrial systems and factories. Currently, deep learning-based fault prediction models are more popular, and multi-factory co-operation is required to improve the accuracy and generality of fault prediction models. Federated learning can coordinate multiple clients to train models together while protecting client privacy, and thus is widely used for training fault prediction models. How to incentivise more factories to participate in model training is crucial, however, most of the existing incentive mechanisms focus on the problem of fair measurement of client contributions and ignore the problem of incentive allocation in scenarios with limited incentive budgets. In this paper, we design a self-safeguarding incentive mechanism for large scale-federated industrial maintenance based on double layer reinforcement learning, known as Dual Layer Incentive (DLI). The method enables the central server to achieve higher model training accuracy within a limited incentive budget through rational allocation of incentives, which ultimately reduces the overall cost of model training. In addition, we categorise participating clients into “diligent clients”“, inefficient clients” and “malicious clients” based on their contributions and design tailor-made incentives for each client type, which saves training costs and enhances the safety of the model training process. Finally, the proposed approach is evaluated through experiments using various datasets. The results show that the method significantly improves the accuracy and safety of industrial fault prediction model compared to other existing methods.

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). We consider both the cases where the capacity of the backhaul links between the RAPs and BS is unlimited or limited and adopt different fusion strategies. Specifically, when the backhaul capacity is unlimited, the BS and RAPs transfer sensing signals they have received to the central controller (CC) for signal fusion. The CC processes the signals and leverages the generalized likelihood ratio test detector to determine the present of a target. However, when the backhaul capacity is limited, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at maximize the target detection probability under communication quality of service constraints, two power optimization algorithms are proposed. Finally, numerical simulations demonstrate that the sensing performance in case of unlimited backhaul capacity is much better than that in case of limited backhaul capacity. Moreover, it implied that the proposed IAPS scheme outperforms only-passive and only-active sensing schemes, especially in unlimited capacity case.

Studies on novel sub-6GHz indoor surface wave communication channel models are presented. With the aid of a surface wave launcher that based on planar magnetic dipole antennas, a wireless coupled, indoor surface wave channel measurement apparatus is constructed to validate the channel characteristics, i.e., path loss, channel impulse response, and channel capacity. As validated in the 2.5-4.5GHz band, the path loss can be reduced by 30dB compared to the free-space propagation case, owing to the sufficient excitation and propagation of the surface wave mode. In addition, scattering paths can be clearly identified with mitigated multi-path effect. Unlike traditional coaxial surface wave launchers that cascading with the transmission line in a wired fashion, the surface wave launcher is coupled to a long, single conductor line with length longer than 30 wavelengths that emulating the long, bulky conductors in Industrial Internet-of-Things (IIoT) in a wireless manner. Therefore, the wireless IIoT nodes can be flexibly networked and deployed as desired in a low-loss fashion with less multi-path effect. The conceptual propagation scheme and channel modelling approach is expected to benefit the design and deployment of IIoTs.

Cell-free massive multiple-input-multiple-out (CF-mMIMO) systems provide limited coverage because of expensive wired fronthaul between access points (APs) and central processing unit (CPU). To address this challenge, we propose a novel framework where an unmanned aerial vehicle (UAV), acting as an aerial AP, works coherently with the ground APs to expand the coverage of conventional CF-mMIMO system. To fully utilize the spectrum resource, the wireless fronthaul between the CPU and UAV shares the total bandwidth with the radio access networks. Considering limited power supply of the UAV and for the goal of green communications, we formulate a weighted sum power minimization problem to jointly optimize downlink beamforming and fronthaul compression, as well as UAV placement. The formulated problem is a mixed timescale problem, thus we propose a two-timescale optimization framework in which the UAV placement is optimized in each long timescale based on statistical channel state information (CSI), then the downlink beamforming and fronthaul compression are optimized in each short timescale based on instantaneous CSI. Specifically, uplink-downlink duality and semidefinite relaxation (SDR) based alternating optimization techniques are introduced to find solutions to the short timescale issue, while successive convex approximation and SDR methods are invoked to find solutions to the long timescale issue. Finally, simulation results corroborate the performance of the proposed algorithm.

Rate-splitting multiple access (RSMA) has emerged as a potent paradigm shift in wireless communications, demonstrating resilience to channel state information (CSI) inaccuracies and significant rate enhancements. This work investigates RSMA’s application within the context of ultra-reliable and low-latency communication (URLLC) for the forthcoming Internet-of-Everything networks. Specifically, we integrate RSMA with a cell-free massive multiple-input multiple-output (MIMO) architecture to support URLLC demands. Considering the imperfect CSI, attributable to pilot contamination and thermal noise, we derive rigorous lower-bound expressions for the downlink achievable rates. These expressions are applicable to short-packet communication scenarios and RSMA strategy over spatially correlated Rician fading channels. Utilizing these analytical expressions, we perform an exhaustive rate performance evaluation, varying system parameters such as the numbers of pilots, access points (APs), devices, and antennas per AP, alongside different multiple access techniques. Furthermore, we address the power control coefficient design for both common and private streams, framing it as an optimization problem aimed at maximizing the weighted sum-rate and enhancing URLLC service quality. To tackle this non-convex challenge, we introduce a geometric programming-based path-following algorithm, which iteratively converges to the solution. The theoretical underpinnings and the efficacy of the proposed power optimization algorithm are corroborated through extensive simulation results.

Reconfigurable intelligent surface (RIS) is envisioned as a potential technology to improve spectrum efficiency with low energy consumption. Moreover, the spectrum reuse technologies are also extensively applied in various networks, such as vehicle-to-vehicle (V2V) in transportation system, machine-to-machine (M2M) in industrial Internet of Things (IIoT) system and so on. In this paper, we investigate the performance of the general RIS-assisted device-to-device (D2D) communication. We consider the limited feedback system where the channel state information (CSI) is imperfectly known. The analytical expression of the ergodic achievable rate (EAR) for beamforming (BF) strategy and interference cancellation (IC) strategy are derived. For exploring engineering design, we investigate the tight bounds of EAR with much simpler form. Then, the EAR gain brought by RIS compared to the traditional D2D system is analyzed. Further, four specific scenarios are discussed in detail, including the weak interference case, the high SNR case, the large number of BS antennas case and the large number of reflective elements case. We derive the EAR approximations of these four cases and explore a series of insights. Numerical Results shows the perfect agreement between the analytical results and simulations.

Clustered Vehicular Federated Learning (CVFL) can be used to improve traffic safety, increase traffic efficiency, and reduce vehicle carbon emissions. Therefore, it is extremely promising in intelligent transportation systems. However, in practice, it is difficult to accurately cluster vehicular clients with mobility according to data distribution. In addition, vehicular clients may be reluctant to contribute their computation and communication resources to perform learning tasks if the CVFL server does not give them proper incentives. In this paper, we would like to address the above issues. Specifically, considering the mobility of vehicular clients, we first propose a clustering method to cluster vehicular clients into several clusters based on the cosine similarity between the model gradient of local vehicular clients and the K-means method. Then, we design a set of optimal contracts specifically for the clusters, aiming to motivate them to select the optimal number of intra-cluster iterations for model training and give the closed-form solution to the contracts under the constraints of individual rationality, incentive compatibility, and task accuracy. The proposed contract theory based incentive mechanism not only effectively motivates every cluster, but also overcomes the information asymmetry problem to maximize the utility of the CVFL server. Finally, simulation results validate the effectiveness of the proposed clustering method and the designed contract.

This paper investigates the achievable rate and spectral efficiency (SE) of an uplink cell-free massive multiple-input multiple-output Internet-of-Things (mMIMO-IoT) network over Ricean fading channels, where both access points and user equipments (UEs) are equipped with multiple antennas. We derive tight closed-form expressions for the lower-bound achievable rate and SE under maximum ratio combining and imperfect channel state information (CSI). Moreover, we propose a target-signal-to-interference-plus-noise-ratio-tracking opportunistic power control (TOPC) algorithm with gradual soft UE removal to mitigate the effects of unsupported UEs. The proposed TOPC algorithm is fully distributed, as each UE updates its transmit power based on local CSI. Numerical results show that adding more antennas at the UEs can enhance the achievable rate, but may degrade the achievable SE due to the increased pilot overhead. Moreover, the Ricean fading channels offer much higher achievable rate and SE than the Rayleigh fading channels, and our TOPC algorithm exhibits satisfactory performance in various aspects.

Cooperation can help unmanned aerial vehicles (UAVs) improve their plans to visit charging stations and avoid congestion, but can be hindered by privacy concerns. We propose a new, privacy preserving, joint routing, and charging scheduling framework which allows multiple cellular-connected UAVs to jointly optimize their routes and charging schedules in a decentralized fashion. The framework allows each UAV to minimize its energy usage and connectivity outage, maximize its recharged energy, ensure its timely arrival, and preserve its privacy concerning its trajectory and destination. The key idea is that we obfuscate probabilistically the destination of each UAV, and design a new noncooperative Bayesian game among the UAVs to find their best routes and charging schedules toward the obfuscated destinations. Another important aspect is that we prove the game is a potential Bayesian game with a pure-strategy Bayesian Nash equilibrium and the best response yielded with the Bellman–Ford algorithm. This new framework preserves the UAVs’ privacy in the sense that an UAV only shares the probability of its visit to a charging station at different times, and its best response is based on an obfuscated destination. Simulations demonstrate that the framework ensures timely arrivals with near-optimal routes and substantially lower complexity than a centralized routing scheme based on brute force.

Rate splitting multiple access (RSMA) provides a flexible transmission framework that can be applied in mobile edge computing (MEC) systems. However, the research work on RSMA-assisted MEC systems is still at the infancy and many design issues remain unsolved, such as the MEC server and channel allocation problem in general multi-server and multi-channel scenarios as well as the user fairness issues. In this regard, we study an RSMA-assisted MEC system with multiple MEC servers, channels and devices, and consider the fairness among devices. A max-min fairness computation offloading problem to maximize the minimum computation offloading rate is investigated. Since the problem is difficult to solve optimally, we develop an efficient algorithm to obtain a suboptimal solution. Particularly, the time allocation and the computing frequency allocation are derived as closed-form functions of the transmit power allocation and the successive interference cancellation (SIC) decoding order, while the transmit power allocation and the SIC decoding order are jointly optimized via the alternating optimization method, the bisection search method and the successive convex approximation method. For the channel and MEC server allocation problem, we transform it into a hypergraph matching problem and solve it by matching theory. Simulation results demonstrate that the proposed RSMA-assisted MEC system outperforms current MEC systems under various system setups.

The key idea of this paper is to leverage channel angle information to propose a low-complexity mode switching design in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multi-antenna non-orthogonal multiple access (NOMA) systems, which is suitable to both downlink and uplink transmissions by applying the concept of angle reciprocity. Particularly, a location-based assignment algorithm is firstly provided to perform NOMA pairing, then the equal gain transmission at the base station and the cophase matching criterion at STAR-RIS are adopted to improve the performance of the reflected user and the effective channel gain of the paired users, respectively. According to the proposed design, considering the difference in the Rician factors of the channel from the base station to STAR-RIS caused by the change of line-of-sight and non-line-of-sight components, two different cases for channel statistics of the paired users are rigorously characterized. Subsequently, we utilize the above results to analyze the closed-form expressions for the outage probability and ergodic rate of all users under downlink and uplink transmissions. Further, the diversity orders and the high slopes corresponding to all expressions are also determined to pursue more insights. Finally, numerical results are presented to demonstrate our analysis and reveal that: 1) the various system factors have significant impact on the performances of the proposed design; and 2) the low-complexity design can reduce the overheads of channel estimation and signal processing at the expense of extremely-low or even no performance loss in contrast to the traditional maximum ratio transmission based design.

Against the background of 6G communication and big data, more and more attention have been paid to the communication reliability and resource efficiency. Reconfigurable intelligent surface (RIS) is envisioned as a potential technology to improve the communication environment by controlling the electromagnetic wave propagation. A majority of related researches use the central limit theorem (CLT) to implement the analytical evaluation, which results in inaccuracy for the case with small number of reflective elements. In this letter, we investigate the outage behavior of RIS-enabled downlink cellular networks where exist several device-to-device (D2D) pairs under the general fading channels, i.e., Nakagami-m fading channels. We propose a comprehensive solution to evaluate the outage probability for all the cases with different numbers of reflective elements. Our solution utilizes the multivariate Fox's H-function and proposes the outage expressions in closed form. Finally, The accuracy of the closed-form outage probability is verified in simulation section for different cases of system configurations.

In this paper, we propose a low-complexity mode switching design in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multi-antenna non-orthogonal multiple access (NOMA) system via exploiting channel angle information only. Particularly, a location-based assignment algorithm is firstly provided to perform NOMA pairing, then the equal gain transmission at base station and cophase matching criterion at STAR-RIS are adopted to improve the performance of reflected user and the effective channel gain of paired users, respectively. On the basis of to the above design, we study the exact channel statistics in two different cases to further derive the outage probability of reflected and transmitted users. Numerical results are presented to demonstrate our analyses and reveal that: 1) the various system factors have markedly impacts on our proposed design; and 2) the low-complexity design can reduce the overheads of channel estimation and signal processing at the expense of extremely low performance loss.

In this paper, we investigate a distributed reconfigurable intelligent surface aided massive access in multipleinput single-output non-orthogonal multiple access system with imperfect channel state information (CSI) and successive interference cancellation (SIC). In particular, a novel active and passive beamforming scheme are designed to fully eliminate the intercluster interference and improve the effective channel gain of the prioritized users, respectively. To study the performance of the proposed scheme, the exact channel statistics are derived to further analyze the outage probability of each user within a cluster. Finally, simulation results are presented to prove our theoretical analyses and reveal that: 1) enhancing the estimated accuracy of CSI and the ability of SIC process can significantly enhance the outage performance; 2) the proposed zero-forcing based scheme can obtain a higher system throughput compared to previous designs.

Abstract The internet of things accelerates the wireless connections of massive devices to provide all kinds of new services, and thus intensifies the spectrum scarcity and access delay. Cognitive radio (CR) technology brings a solution for the issue. As a typical CR scheme, opportunistic spectrum access (OSA) has been addressed widely in the past decade. However, the tradeoff between two key indexes, the throughput of cognitive radio network (CRN) and the delay of secondary user (SU), is rarely mentioned so far, which ignites the authors’ work in this paper. Taking into account a channel handoff (CH) based multi‐channel OSA scenario, the authors first analyze the opportunistic transmission performance of SU, and model the throughput of CRN as well as the handoff delay of SU. Then, the authors build up a delay‐constraint throughput optimization problem, and thus formulate the throughput‐delay tradeoff for OSA. Finally, the optimal traffic rates of SU for a good throughput‐delay tradeoff are derived according to maximizing the throughput of CRN. Theoretical and simulated results show that to enhance the throughput and to reduce the delay do not conflict always. By well adjusting the traffic rates of SU according to the traffic rates of primary user, the throughput of CRN can be improved while the handoff delay can be kept under a given level.

Grant-free random access (GFRA) becomes attractive in Internet of Things (IoT) due to its low signaling overhead and short access latency. In this article, we investigate GFRA in a multicel massive multiple-input-multiple-output (MIMO) system. As the IoT device usually has sporadic traffic, we set a packet buffer for each device to describe its temporal traffic, and also use the stochastic geometry to describe the randomness of devices’ spatial locations. With the backoff mechanism, only devices with a nonempty buffer and a successful backoff are activated and allowed to request access. Unlike previous works that regard all devices selecting the same pilot (i.e., the colliding devices) as undetectable, we give a more accurate model that the base station (BS) can detect colliding devices when they locate far away from each other, and we set a unique collision area for each device to quantify the boundary that the collision can be ignored. A tight approximation for the number of packets successfully transmitted at the unit area and time slot, named packet throughput, is derived. Based on it, we obtain the optimal backoff parameter that maximizes the packet throughput under devices’ delay constraints. It is shown that when pilots are insufficient or device packet traffic is heavy, a long backoff time is needed. However, as the pilot grows or the packet traffic turns light, devices should gradually reduce the backoff time. In particular, if pilots are surplus, cheap detectors can be equipped on the BS without an obvious packet throughput reduction.

Proactive eavesdropping is an effective approach to improve the performance of wireless information surveillance, where suspicious users are legitimately eavesdropped by authorized monitors. For the situation when no monitor is available, we propose to authorize unsuspicious users to surveil suspicious users provided that the quality-of-service (QoS) of unsuspicious users is guaranteed. This paper considers an interference network consisting of a suspicious-transmitter (STx)/suspicious-receiver (SRx) pair and an unsuspicious-transmitter (UTx)/unsuspicious-receiver (URx) pair. We assume that URx acts as a monitor to eavesdrop the suspicious communication, and UTx adopts improper Gaussian signaling (IGS) for flexibly managing the interference to SRx. The QoS of the unsuspicious communication is guaranteed by enforcing its outage performance to be higher than a desirable level. The problem of optimizing the transmit power and the IGS circularity coefficient of UTx to maximize the average eavesdropping rate is optimally solved by the Lagrange duality method. Theoretical results show that IGS is effective when the eavesdropping is successful and the unsuspicious communication is in non-outage status. Simulation results confirm that the proposed design works well even when no additional outage of the unsuspicious communication is allowed, and show that IGS can greatly outperform proper Gaussian signaling in most cases.

This paper investigates the uplink performance of a general cell-free massive multiple-input multiple-output (CF-mMIMO) system, in which all access points (APs) and user equipments (UEs) suffer from hardware impairments (HWIs). Besides, there are several reconfigurable intelligent surfaces (RISs) that aim to improve the coverage quality, spectral efficiency (SE), and energy efficiency (EE). Relying on the knowledge of only imperfect channel state information, a tight closed-form expression for the lower-bound achievable SE is derived. Based on this expression, we quantitatively investigate the impacts of different system parameters on uplink SE and EE, and conduct a tradeoff analysis between using more APs versus using more RISs with respect to the above performance metrics. In addition, we also design a max-min SE algorithm that takes into account both large-scale fading decoding weights and power control coefficients to guarantee UE fairness. Specifically, the proposed algorithm admits a closed-form solution and is therefore memory-efficient and time-saving. Both the theoretical analysis and the effectiveness of the proposed max-min SE algorithm are verified via extensive simulations.

Driven by the development of time-sensitive applications, short packet transmission (SPT) design has become the key point in the ultra-reliable and low-latency communications (URLLC) area. The primary challenge in it is that the delay caused by pilot overhead cannot be neglected. To deal with this issue, this paper presents a frame structure adopting partial-superimposed-pilot (PSP) scheme for SPT. The key of PSP scheme is that the number of data symbols is equal to the available blocklength, and the number of pilot symbols transmitted with data in the training stage needs to be optimized. Under the finite blocklength regime, we first derive a closed-form lower bound achievable rate of an uplink massive MIMO system with imperfect pilot removal for maximal-ratio-combining (MRC) receiver. Then, we formulate a weighted sum rate maximization problem by jointly optimizing the pilot length, pilot power, and data power. We derive a closed-form solution of optimal pilot length. Using the log-function and successive convex approximation (SCA) method, we develop an iterative optimization framework to find a locally optimal power solution. For comparison, the conventional frame structures based on complete-superimposed-pilot (CSP) and regular pilot (RP) schemes are also shown. Simulation results indicate that the proposed PSP scheme is superior to the existing CSP and RP schemes.

Proactive eavesdropping is an effective approach to legitimately surveil the suspicious communications. Current studies all considered that physical layer security (PLS) techniques such as the wiretap coding are not applied by the suspicious users (SUs) to protect their communications. Contrary to that, we consider that the wiretap coding in PLS is adopted by the SUs to defend against the proactive eavesdropping from the legitimate monitor over fading channels. Under the fixed power allocation (FPA) and the water-filling power allocation (WFPA) at the SUs, the problem of jamming power allocation at the monitor to maximize the relative eavesdropping rate under the average transmit power constraint is investigated. The optimization problem is solved by two nested optimizations, where the bisection search method is used in the outer optimization, and the Lagrange duality method and the successive convex approximation method are used in the inner optimization. Simulation results confirm the effectiveness of the proposed algorithms compared to various baseline algorithms. It is shown that the proposed algorithm under the WFPA at the SUs outperforms the one under the FPA at the SUs, and the monitor achieves lower relative eavesdropping rate when the SUs adopt a stricter secure transmission scheme.

This paper puts forth a new application of an unmanned aerial vehicle (UAV) to joint goods delivery and in-situ sensing, and proposes a new algorithm that jointly optimizes the route and sensing task selection to minimize the UAV’s energy consumption, maximize its sensing reward, and ensure timely goods delivery. This problem is new and non-trivial due to its nature of mixed integer programming. The key idea behind the new algorithm is that we interpret the possible waypoints of the UAV as location-dependent tasks to incorporate routing and sensing in one task selection process. Another critical aspect is that we construct a new task-time graph to describe the process, where each vertex corresponds to a task associated with its location, time and reward, and each edge indicates the propulsion energy required for the UAV to travel between two tasks. By redistributing the weight of a vertex to its incoming edges, the new UAV routing and sensing task selection problem can be converted to a weighted routing problem in the new task-time graph and solved optimally using the Bellman-Ford algorithm. Validated by a real-world case study, our approach can outperform its alternatives by over 18% in task reward.

Unmanned aerial vehicles (UAVs) have been extensively applied to goods delivery and in-situ sensing. It becomes increasingly probable that multiple UAVs are delivering goods and carrying out sensing tasks at the same time. The destinations of the UAVs are usually required to jointly design their trajectories and sensing selections, leading to privacy concerns for the UAVs. This paper presents a new game-theoretic routing framework for joint goods delivery and sensing of multiple cellular-connected UAVs, where the UAVs minimize their energy consumption and connectivity outage, maximize their sensing reward, and ensure timely goods delivery and trajectory privacy by optimizing their trajectories and sensing task selections in a decentralized manner. The key idea is that we unify routing and sensing in a single task selection process, which is further transformed into routing on a task-time graph. Another important aspect is that we design a non-cooperative potential game for the routing on the task-time graph. A distributed strategy is developed, where each UAV only reports its sensing task selections and withholds its destination information and its best response produced by the Bellman-Ford algorithm. By this means, the destination and trajectory privacy of the UAVs are protected. Simulations show that the new game-theoretic approach can ensure timely delivery and achieve close-to-optimal solutions with significantly lower complexity compared to a centralized brute-force approach.

Mobile edge computing (MEC) is a promising paradigm to handle the mismatch between computation-intensive applications and resource-limited devices. Nevertheless, as most Internet of Things (IoT) terminals are battery-limited, the computation gain of MEC may be compromised due to insufficient battery energy for task offloading. Wireless power transfer (WPT) and full duplex (FD) communications are economical charging and transmission methods for battery-limited IoT terminals. However, when integrating wireless power transfer and FD into MEC, the incentive problem should be jointly addressed with task offloading, because the WPT facilities and their powered IoT nodes belong to different service operators. In this paper, we investigate the efficiency of WPT from the perspective of battery saving, and propose an efficient wireless powered task offloading and incentive mechanism in FD MEC-enabled cellular IoT networks. The battery saving efficiency, which addresses both the total cost of WPT and saved energy of battery, is proposed as the performance metric. By adopting this metric as the utility function of the network operator (NO), the task offloading and incentive problem are jointly formulated as a Stackelberg game. We then propose an efficient alternating direction iteration-based algorithm to solve its equilibrium efficiently. Simulation results demonstrate the benefits of our algorithm in battery saving by comparisons with utility oriented benchmarks. Moreover, it reveals the tradeoff between the utility of NO and battery saving, which verifies the positive effects of FD communications and WPT in improving the efficiency of battery saving.

Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.

To legitimately eavesdrop communications of suspicious users such as criminals, proactive eavesdropping has been proposed as an effective approach. Current works considered that suspicious communication links (SCLs) carry homogeneous services, which is unrealistic due to randomness of user behaviors. Thus, this paper investigates proactive eavesdropping over multiple SCLs with heterogeneous services. The relative eavesdropping non-outage probability and the relative eavesdropping rate are adopted as eavesdropping utilities for delay-sensitive (DS) and delay-tolerant (DT) suspicious services, respectively. The jamming power allocation problem to maximize the weighted sum eavesdropping utility under the average transmit power constraint at the monitor is investigated under the non-adaptive fixed power allocation and the adaptive power allocation schemes adopted by the SCLs. The problem belongs to sum-of-ratios optimization and is transformed to an equivalent parameterized subtractive-form with some extra auxiliary variables. Two nested optimizations are proposed to solve the equivalent problem based on the Lagrange duality method and the damped Newton method. Simulation results demonstrate the superiority of the proposed algorithms compared to various benchmark algorithms in existing literature. It is shown that the impacts of the power allocation schemes of the SCLs on the eavesdropping performance may be different for the DS and DT suspicious services under different system setups.

With the rapid development of 5G, massive machine-type communication is expected to experience significant growth, leading to severe random access collisions. To address this issue, we first adopt deep neural networks to detect random access collisions by learning the features of the received signals. Based on the collision-detection results, we propose a double-contention random access (DCRA) scheme, with which the base station can schedule one more contention process for devices experiencing collisions. To fully harness the collision-resolution capability of the proposed DCRA scheme, we further analyze its performance and illustrate how to tune the backoff parameters to optimize the network throughput. It is revealed that the maximum throughput of the DCRA scheme depends on the number of random access preambles and the collision recognition accuracy. The corresponding optimal backoff parameters are then obtained, which greatly facilitates implementations in practice. Simulation results show that with a high collision recognition accuracy, the proposed scheme can achieve significant throughput improvement.

Grant-free random access (GFRA) becomes attractive in Internet-of-Things (IoT) due to its low signaling overhead. In this paper, we investigate the GFRA in a multicell massive multiple-input multiple-output (MIMO) system after considering both the spatial and temporal traffic of devices. By introducing the backoff mechanism, only devices with non-empty buffer and a successful backoff can request access. Unlike previous works on GFRA that regard all devices selecting the same pilot as undetectable, we set a unique collision area for each device to quantify the boundary that BS can detect the collision. With tools of stochastic geometry and queueing theory, we derive a tight approximation for the number of packets successfully transmitted at unit area and time slot, named as packet throughput (Tp). Based on it, we find that the range of the collision area has a remarkable effect on Tp. The optimal backoff parameter that maximizes Tpis also obtained, and we find that a long backoff time is needed when the pilot is insufficient or the packet traffic is heavy. Compared with the fully-loaded access, our optimal backoff mechanism can significantly improve Tp, especially for the system with crowded devices.

To meet the demanding need for global connectivity, non-terrestrial networks can provide essential support to complement and extend the terrestrial infrastructure. However, the presence of non-terrestrial networks comes up with new demands. For example, a critical problem is satisfying the latency constraints of delay-sensitive applications while reducing the signaling consumption to save valuable channel resources in the random access stage. To address this issue, we propose a distributed access class barring (ACB) factor determination algorithm in this paper to satisfy the specific latency constraints of delay-sensitive applications and reduce the signaling exchange between user equipments (UEs) and the base station simultaneously. With this algorithm, UEs can estimate the required ACB factor only by their previous experiences in an estimation period rather than relying on the base station. Simulations show that the proposed distributive ACB factor determination algorithm can satisfy the requirements of delay-sensitive applications well when the delay constraint is not too strict. Besides, the influence of the variation of UEs and the estimation period is also discussed. It is found that the estimation period plays an important role in the accurate estimation of the ACB factor and needs to adapt to the changes in the number of UEs.

In this paper, we study a distributed reconfigurable intelligent surface-aided downlink multiple-input multiple-output non-orthogonal multiple access systems with random distributed users, where the signal alignment can be achieved within the paired users by controlling discrete phase shifts. In particular, a unified precoder and decoder are provided to cancel inter-cluster interference. To evaluate the performance of proposed framework, the near and far users’ channel statistics with Nakagami-m fading are derived. Further, the approximate expressions of average outage probability are obtained by utilizing the proposed channel statistics, and the corresponding diversity orders can also be obtained to acquire more insights. Finally, simulation results reveal that not only discrete phase shifts design by selecting a 3bit resolution can realize the near optimal performance but also the diversity gain can be significantly improved by increasing the number of reflection elements.

Energy harvesting (EH)-enabled cellular Internet of Things (IoT) is a promising solution to handle the charging and accessing of massive IoT nodes. However, limited by the high-frequency band of future 5G, the radius of the small base station (SBS) is reduced, hence greatly increasing the cost of the network operators (NOs). In this article, we consider the joint cell association, cell sleeping (CS), and incentive decision problem for EH-aided cellular IoT with full-duplex (FD) self-backhauls. We formulate a Stackelberg game to investigate the coordination between the utilities of NO and energy transmitters (ETs), where both the features of FD self-backhauls and CS are introduced to reduce the expense of NO. We then propose an alternative direction algorithm to solve the equilibrium of the game efficiently, where the relationship of the formulated constraints and variables are utilized to transform the original problem into two subproblems. We propose a two-level Lagrangian relaxation to solve the first subproblem, while the other is proved to be convex and solved by an efficient iteration. Simulation results demonstrate the benefits of our algorithm in utility improvement and expense reduction. Moveover, it shows that our algorithm can obtain high efficiency by adjusting the tradeoff between the number of active SBS and transmitting power of ETs according to the network deployment.

Mobile-edge computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation intensive and delay-critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering nonstationary computation demands, user mobility, and wireless fading channels. This article aims to study the tradeoff between energy efficiency (EE) and service delay for multiuser multiserver MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on the Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a$[O(1/V), O(V)]$tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines.

Novel empirical path-loss and root mean square delay spread (RDS) models for smart office scenarios are proposed. The effects of person density on the path loss and RDS are investigated based on the extensive measurements at 2.3–2.5 GHz. First, both of the measured path loss and RDS data are modeled as the dual log-distance functions. It is caused by the regular structure and furniture in the office environment. Second, in the proposed path-loss model, the path-loss exponents and the additional attenuation factor are modeled as quadratic functions of the person density. Meanwhile, the RDS is found to be uncorrelated with the person density. These phenomena reveal that the persons in the environments can be regarded as absorbers rather than scatters. Then, the accuracy of the proposed models is validated by the measured data and compared with two traditional models. Finally, the effect of the persons’ movements on the path loss and RDS is investigated, and the proposed models are extended to millimeter wave bands by a ray tracing technology. The proposed models and results can provide necessary information for link budget and algorithm design for the Internet of Things smart office scenarios.

In this paper, the legitimate surveillance of a suspicious mobile edge computing (MEC) network consisting of a suspicious edge server (SES) and multiple suspicious users (SUs), in the presence of a full-duplex monitor is studied. Each SU has a computation task to complete within a time deadline and can completely or partially offload the task to SES, while the monitor can either jam or assist the suspicious communications during task uploading and result downloading. With the heterogeneous offloading model adopted by the SUs, the problem of optimizing the monitor mode and transmit power to maximize the average ratio of successfully eavesdropped tasks, subject to the monitor transmit power constraint and the task completion time deadline constraint is investigated. The problem is solved via exploring the particular problem structure and adopting the sum-of-ratios optimization. Simulation results show that the proposed algorithm significantly outperforms the benchmark algorithms, especially for the SUs with partial offloading. It is also shown that the proposed algorithm is of low-complexity and achieves almost the same performance as the high-complexity optimal algorithm. Besides, compared to the SUs with binary offloading, the eavesdropping performance for the SUs with partial offloading is shown to be much better.

Short packet communications play key roles in the Internet-of-Things. Conventional Shannon’s coding theorem is not applicable for short packet communications, and the achievable rate in the finite blocklength regime is related to the blocklength and the decoding error probability. Contrary to conventional physical layer security, wireless information surveillance assumes that the communication users are suspicious users and the eavesdroppers are legitimate monitors, and proactive eavesdropping via jamming has been proposed to improve the eavesdropping performance. Existing works on proactive eavesdropping ignored the scenario when the suspicious users adopt the short packet communications. Thus, this paper tries to develop effective proactive eavesdropping schemes suitable for the short packet suspicious communications. Under the truncated channel inversion power allocation and the modified constant power allocation policies at the suspicious source, the problems of jamming power allocation for maximizing the monitoring success probability subject to the average transmit power constraint at the monitor are investigated. Based on the Dinkelbach-type algorithm, the Lagrange duality method and a novel tractable approximate expression for the decoding error probability, we derive suboptimal solutions to the problems. Simulation results demonstrate the effectiveness of the proposed solutions compared to various benchmark algorithms in existing literature.

The combination of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) has been recognized as a critical method to improve the sixth generation networks performance. In this paper, a distributed RIS-aided downlink multiple-input multiple-output (MIMO) NOMA systems with discrete phase shifts are studied, where the channel directions from base station to paired users can be manipulated with the assistance of RISs by employing the concept of signal alignment. In order to ensure base station can flexibly serve some users with NOMA and others users with spatial division multiple access, a unified precoder and decoder are provide to cancel inter-cluster interference. Subsequently, the channel statistics over Nakagami-$m$fading channels are derived for near and far users. In particular, considering the cascade channel gain may exists two different cases, Beaulieu series is further adopted to characterize their corresponding cumulative distribution function. In what follows, the outage probability and ergodic rate for three situations within one cluster are derived by utilizing the obtained channel statistics, respectively. Based on the derived results, we also analyze the diversity order and high signal-to-noise ratio slope to provide essential insights into the considered systems. Finally, simulation results are presented to reveal that: 1) selecting the setting of 3-bits resolution can realize a near-aligned performance for our proposed systems; 2) the cascade channel statistic caused by RIS can be evaluated with any number of RIS element and any channel gain via Beaulieu series.

This paper investigates the secure transmission in downlink cell-free massive multiple-input multiple-output (MIMO) systems in the presence of an active multi-antenna eavesdropper (Eve) over Rician fading channels, assuming that each access point (AP) possesses multiple antennas which are connected with low-resolution digital-to-analog converters (DACs). Closed-form expressions of the achievable secrecy rate relied on the additive quantization noise model are derived. Based on these analytical results, we quantify the impacts of key system parameters, such as the antenna array number, DAC resolution, Rician$\mathcal K$-factor, and balance factor between data and artificial noise power on secrecy enhancement. Several interesting insights are attained by assuming that Eve can or cannot perfectly remove inter-mobile-terminal interference. Moreover, we also propose a power control algorithm that maximizes the achievable secrecy rate, which can be represented as a series of second-order-cone programs for which efficient solvers exist. All the theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments.

Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.

It is an irreversible trend to build a green and sustainable vehicular network facing with the dramatic increase in urban traffic. Reducing energy consumption has been an important aspect for green transportation. Reconfigurable intelligent surface (RIS) is considered as a promising technology to enhance the communication quality with higher energy efficiency. In this paper, we focus on the RIS-assisted vehicular networks. We obtain the closed-form analytical expressions for outage probability, ergodic achievable rate and average energy efficiency. A series of insights are further explored. Based on these, we discuss the performance under high SNR case, as well as, weak interference case. And then, the approximations in simpler form expressions are provided for each case, respectively. Outage diversity order and high SNR rate slope are also investigated. In addition, we propose a power allocation algorithm to maximize the ergodic achievable sum rate guaranteeing the outage probability and average energy efficiency. Numerical results show that our analytical results agree well with the Monte Carlo simulations in various network configurations. Besides, our proposed power allocation scheme significantly enhances the ergodic achievable sum rate compared with the equal power strategy.

This paper investigates a proactive eavesdropping scenario where multiple suspicious communication (SC) links are eavesdropped by a full-duplex monitor who can send jamming or constructive signals on each SC link. In order to avoid being discovered by the suspicious users, the monitor restricts the SC quality-of-service (QoS) degradation caused by itself. Under this SC QoS constraint and the average transmit power constraint, the problems of optimizing the mode and transmit power of the monitor to maximize the average successful eavesdropping probability and the average eavesdropping rate are investigated for the delay-sensitive suspicious service and delay-tolerant suspicious service, respectively. By proving that the time-sharing condition is satisfied, the optimal solutions are derived based on the Lagrange duality method. Simulation results validate the effectiveness of the proposed schemes. It is shown that the proposed schemes can provide satisfactory eavesdropping performance improvement even with zero SC QoS degradation requirement.

Federated learning (FL) enables devices to update a global model while keeping the training data local, so that data privacy is protected. However, the local data of devices is usually non-independent and identically distributed (non-i.i.d.), which leads to performance degradation. This paper aims to address this issue by a client-selection approach. In particular, in consideration of balancing the label distribution of the selected clients, a new client selection method called grouping based scheduling (GS) scheme is proposed, with which clients are divided into several groups based on a new metric called group earth mover’s distance (GEMD). Experiment results show that the GS can improve the performance of FL algorithms, compared to the random scheduling scheme. An encryption method is further proposed to enhance privacy protection, which facilitates the application of the proposed GS scheme.

In this paper, we consider a hierarchical federated learning system and formulate a joint problem of edge aggregation interval control and time allocation to minimize the weighted sum of training loss and training latency. To quantify the learning performance, an upper bound of the average global gradient deviation, in terms of the edge aggregation interval, the time allocated for training, and the number of successfully participating devices, is derived. Then an alternative problem is formulated, which can be decoupled into two sub-problems and solved with two steps. In the first step, given the time allocation strategy, a relaxation and rounding method is proposed to optimize the edge aggregation interval. In the second step, with the results of the obtained edge aggregation interval and based on the convex optimization theory, an optimal time allocation can be evaluated. Simulation results show that the proposed scheme, compared to the benchmarks, can achieve higher learning performance with lower training latency.

Federated learning (FL) has become a promising solution to train a shared model without exchanging local training samples. However, in the traditional cloud-based FL framework, clients suffer from limited energy budget and generate excessive communication overhead on the backbone network. These drawbacks motivate us to propose an energy-aware hierarchical federated learning framework in which the edge servers assist the cloud server to migrate the local models from the clients. Then a joint local computing power control and client association problem is formulated in order to minimize the training loss and the training latency simultaneously under the long-term energy constraints. To solve the problem, we recast it based on the general Lyapunov optimization framework with the instantaneous energy budget. We then propose a heuristic algorithm, which takes the importance of local updates into account, to achieve a suboptimal solution in polynomial time. Numerical results demonstrate that the proposed algorithm can reduce the training latency compared to the scheme with greedy client association and myopic energy control, and improve the learning performance compared to the scheme in which the associated clients transmit their local models with the maximal power.

Abstract Ultra‐dense networking is considered an effective solution to achieve high capacity in 5G networks. However, the densely distributed base stations (BSs) in ultra‐dense networks (UDNs) make the inter‐cell interference much more serious than that in traditional cellular networks. Therefore, it is important to mitigate inter‐cell interference in the UDNs to improve network performance. To tackle this problem, we propose a novel virtual resource mapping algorithm that includes a resource reservation (RR) algorithm and a real‐time resource embedding (RE) algorithm. Specifically, according to the number of services predicted by a dynamic service model, the RR algorithm is proposed to determine the sets of multiplexing BSs in the next time cycle and reserve channel resource required by each BS. Then, to further reduce inter‐cell interference, the real‐time RE algorithm is proposed to allocate the channel resource in real time. Finally, simulation results show that the proposed algorithm has better performance in terms of signal‐to‐interference‐plus‐noise ratio and acceptance ratio, compared to the existing algorithms, such as the frequency reuse channel allocation algorithm and inter‐cell interference coordination algorithm.

Mobile-edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate the EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras, such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this article, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and EH. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the central process unit frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on the Lyapunov optimization and semidefinite programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of the user mobility, EH model, and channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.

Recently, wireless powered cooperative cognitive radio networks (CRNs), which combine the technologies of radio frequency (RF) energy harvesting and CR, have drawn great attention. In such networks, energy cooperation between the cognitive users (CUs) and the wireless powered primary users (PUs) can be performed, where the CUs can charge the PUs wirelessly in exchange for the spectrum access. Specifically, energy cooperation and information transmission is executed in two phases, where the CUs transmit their data signals and the PUs harvest energy from these signals in the first phase, and the PUs transmit their data using the harvested energy in the second phase. In particular, we consider two multiple access schemes for the CUs, namely non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA). For both NOMA and TDMA, the PU sum-rate maximization problems under the minimum CU sum-rate constraint are first simplified by exploring particular problem structure, then are transformed to convex problems, and finally are solved optimally. The PU sum-rates of the two schemes are compared theoretically as well as numerically. It is revealed that the circuit power consumption at the CUs, the required minimum CU sum-rate, and the PU energy harvesting sensitivity and saturation thresholds play key roles in the PU performance comparison of the two schemes.

In this study, the uplink performance of cell‐free massive multi‐input multi‐output (mMIMO) system with multi‐antenna access points (APs) and users is investigated, assuming low‐resolution analogue–digital converters (ADCs) are employed at the APs. By exploiting the additive quantisation noise model, a tight closed‐form rate expression is derived. This tractable finding characterises the impacts of the multi‐antenna APs and users, the imperfect quantisation error and the channel estimation error. In order to maximise the uplink sum‐rate, a joint quantisation bit and power control problem is formulated, subjecting to the backhaul capacity and each user power constraints. The original resource optimisation problem is non‐convex and it is decomposed into two sub‐problems, namely quantisation bit design and power allocation problem, to alleviate the difficulties. In particular, the resultant two sub‐problems can be efficiently determined by utilising the Lagrange Multiplier and sequential convex approximation methods, respectively. Finally, numerical simulations are presented to examine the analytical findings and evaluate the effectiveness of the proposed algorithm.

Future network based on software-defined networking (SDN) and network function virtualization (NFV) technologies is the main evolution tendency of current Internet, enabling telecommunication service providers (TSPs) to share their virtualized network resources with their contracted users in a flexible and economical manner. One key technical issue is virtualized resources allocation. In order to solve this issue, multiple mapping algorithms have been proposed. However, prior mapping algorithms focus on solving the allocation problem in one centralized underlying substrate network (SN), having the only goal of maximizing the TSPs' mapping revenue. As energy cost accounts for more than half of the total underlying network cost, it is crucial to minimize the total energy cost, while keeping high mapping revenue. In addition, in the real networking environment, multiple geographically distributed SNs, called interdomain networks, coexist. Hence, it is essential to embed each virtual network (VN) service among the interdomain SNs. Based on this, we first propose the formal problem model and the energy cost model. Then, we propose a novel and efficient mapping strategy, labeled EERID. Our EERID is able to map each VN service among interdomain SNs within polynomial time. The experimental results vividly reveal that EERID significantly reduces energy cost by approximately 18% over the existing energy-aware algorithms. At the same time, our EERID achieves higher embedding revenues than the existing energy-aware mapping algorithms, up to 23%.

Environmental monitoring is one of the fundamental applications of the Internet of Things (IoT), and caching in energy harvesting-aided IoT is a promising solution to handle the energy charging of the IoT nodes in the vast monitoring area. However, the growth of the requirements for monitoring area and accuracy brings huge infrastructure costs to the network operators (OP), especially for the multiple OPs scenario. In this article, we utilize wireless virtualization to enable the IoT node sharing between multiple OPs in cache-enabled energy harvesting-aided IoT, so as to improve the utility of the OPs. A Stackelberg game is formulated to jointly handle the IoT node sharing and energy transmission incentives between OPs and energy transmitters. Then, the knapsack problem, convex and linear programming are utilized to approximate the game through problem transformation and derivations. On the basis of that, an alternative direction algorithm is proposed to solve the equilibrium efficiently. The simulation results verify the advantages of the proposed algorithm in utility improvement and fairness maintenance between multiple OPs.

This article investigates secure communication between a source and a destination via multiple radio frequency (RF) energy harvesting (EH) relays, in which the RF-EH relays are untrusted and apply the amplify-and-forward policy. On the one hand, to prevent the untrusted relays from eavesdropping the confidential information, the destination-aided jamming is employed, in which the destination emits jamming signal to interfere the relays while the source transmits information signal. On the other hand, the power splitting (PS) policy is adopted at the relays to harvest energy and process information, in which every relay has a finite energy storage to accumulate the energy harvested from the source’s information signal and destination’s jamming signal. To achieve energy-efficient and fully distributed implementation, we propose an energy-aware distributed beamforming (EADB) scheme, in which each relay only needs local information to decide whether to assist the source-destination communication. To evaluate the secrecy performance of the EADB scheme, the charging and discharging behaviors of the relays’ energy storage are first tracked using the Markov chain. On this basis, we derive analytical expressions for the hybrid outage probability (HOP) and secure energy efficiency (SEE) of the considered network. Finally, numerical results show that the EADB scheme has a better secrecy performance than the existing scheme. In addition, despite the curiousness of the untrusted relays, deploying more relays and increasing their energy storage capacity can enhance the security of the considered network.

Massive robust communication demands among machines and humans are required in ubiquitous Internet-of-Things (IoT) applications. To design the appropriate communication system, the knowledge of the propagation characteristics for various IoTs scenarios is necessary. In this article, a measurement-based neural-network-based root-mean-square (RMS) delay spread model for ubiquitous indoor IoTs scenarios is presented. The proposed model is a two-layer feedforward neural network plus a random variable, characterizing the average RMS delay spread and uncertain shadowing effect, respectively. The neural network consists of five inputs, including transmitting/receiving antennas (Tx/Rx) separation, frequency, antenna height, environment, and line-of-sight/non-line-of-sight (LOS/NLOS) propagation condition, seven hidden layer neurons, and one output layer neuron. Compared with different configurations of the neural network, the hyperbolic tangent sigmoid functions and the Levenberg-Marquardt backpropagation algorithm are selected as neurons' activation functions and training method, respectively. Additionally, the random variable is found to follow the normal distribution using the maximum-likelihood estimation. Finally, the novel model is experimentally validated to be accurate, general, and extensible compared with the conventional normally distributed RMS delay spread model. This model is well applicable to the design and planning of the ubiquitous communication links for future IoTs scenarios.

Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the reembedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

Accurately estimating network traffic from the partial measurements plays a crucial role in network management. However, the potential anomaly existing in real networks usually makes this goal difficult to achieve. Existing network traffic estimation methods generally impute network traffic independent of anomaly detection, which incurs significant performance degradation with network anomaly. To address this issue in the realistic network scenario, we propose a novel anomaly-aware network traffic estimation method to recover network traffic data concurrently with network anomaly detection. Specifically, by exploiting the inherent spatio-temporal characteristics, we first formulate the network traffic estimation as a low-rank tensor completion problem. Then, an outlier-robust tensor completion (OrTC) model is constructed by introducing both L2,1-norm regularization and LF-norm regularization, which can not only well fit the intrinsic low-rank property of real traffic data, but also is robust against both the dense noise and the sparse anomaly. Furthermore, an effective optimization algorithm OrTC-AM is designed to solve the non-convex and non-smooth OrTC model based on the popular alternating minimization method. Finally, the extensive experiments performed on the public dataset demonstrate that our proposed OrTC-AM method outperforms the previously widely used network traffic estimation methods.

Utilizing the idle computing resources from the distributed Internet of Things devices can sustainably increase the computational capacity and thereby effectively alleviate the pressure on resource-constrained devices, which is referred to as collaborative computing. However, extra computing consumption potentially impacts the local computation tasks of collaborative computing devices. Hence, it is essential to design an efficient incentive mechanism for computational resources sharing. Specifically, we consider the collaborative computing system where a user offloads the computation-intensive and latency-sensitive tasks to multiple idle computing devices (ICDs) by a centralized computing sharing platform (CSP). We first propose a computational latency-based pricing mechanism from the perspective of the quality-of-experience performance; then, a game-theoretic computing task allocation approach is developed among the CSP and multiple ICDs to maximize all participants' profit. The CSP first determines the optimal task partition dynamically upon the tasks' arrival; then, the ICDs derive the optimal central processing unit-cycle frequency correspondingly. Simulation results demonstrate that the overall computational latency of our proposed mechanism is significantly decreased, and achieves by at least 13.5 percent improvement compared with the existing schemes. Meanwhile, the profit of all participants is maximum in collaborative computing, which is improved by 41.5 and 27.9 percent for the CSP and the ICDs, respectively.

By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms.

Virtual network embedding (VNE) refers to the resource allocation problem for network virtualization. Since its inception, multiple mapping algorithms have been proposed for embedding virtual networks (VNs) effectively and efficiently. However, prior mapping algorithms mostly complete the VN embedding in two separated stages: first node embedding and subsequent link embedding. Certain mapping algorithms embed the VN in one stage by using mixed integer linear programming method or subgraph isomorphism approach, involving high embedding completion time. Meanwhile, prior researchers conduct the VN embedding, on the basis of one underlying substrate network (SN). While in future VNE application, each VN must be mapped among multiple geographically distributed SNs. On above backgrounds, we propose a location aware and node ranking value driven embedding algorithm, labeled as LANRVD. The LANRVD enables to conduct the embedding in two coordinated embedding stages within polynomial time. In addition, the LANRVD embeds the VN among multiple geographically distributed SNs. Numerical results reveal that the LANRVD significantly improves VN acceptance ratio by 10% over existing typical two-separated-stages algorithms.

Massive MIMO can dramatically improve capacity and spectral efficiency. However, it is not very clear whether it can significantly improve the signal blockage problem that exists in single antenna systems. In this paper, we investigate the impact of the human body on indoor massive MIMO channels, using practically measured channel data for a 32x8 massive MIMO system in a complex office environment. We introduce a parameter of Power Imbalance (PI) indices to estimate the wide-sense none-stationarity in multiple domains and another parameter of Channel Popularity Indices (CPI) to predict the popularity of MIMO channel. We find that in most cases, the presence of the human body still has a non- negligible negative impact. It decreases the ergodic capacity by about 8% and increases the path loss exponent by 1. In average, the ergodic capacity for NLOS channels are 15% higher than that for LOS.

In the area of network virtualisation, virtual network embedding (VNE) refers to the resource allocation problem. In the literature, researchers have proposed multiple VNE algorithms. These algorithms have the goal of accommodating as many requested virtual networks (VNs) as possible. However, most of prior embedding algorithms belong to the two‐stage (separated node and link embeddings) mapping algorithm category. Certain embedding algorithms embed each VN in one mapping stage by using mixed integer linear programming approach or graph theory, having very high computation time. There is a lack of heuristic algorithms, enabling to embed nodes and links per VN in one mapping stage. In addition, each requested VN embedding needs to be completed in polynomial time so as to be promoted to future dynamic VN service application and real‐time VNs embedding. Based on these backgrounds, the authors propose a novel real‐time and one‐stage heuristic mapping algorithm (VNE‐RTOS). Numerical evaluations are conducted to strengthen that VNE‐RTOS earns more embedding revenues by 8% over typical two‐stage heuristic embedding algorithms (e.g. VNE‐TAGRD) while achieving the same substrate resource utilisation.

This article investigates physical layer security of a downlink multiuser orthogonal frequency division multiplexing (OFDM) Internet of Things (IoT) system with an access point communicating with multiple legitimate IoT devices in the presence of multiple eavesdroppers. For coordinating multiuser communication, the orthogonal frequency division multiple access (OFDMA) and time division multiple access (TDMA) are considered. The IoT devices are assumed to support simultaneous wireless information and power transfer (SWIPT) and can use the harvested energy to jam the eavesdroppers based on full-duplex. The resource allocation problems of maximizing the sum secrecy rate for the OFDMA and TDMA systems are investigated. We first consider the scenario that perfect channel state information (CSI) is available and derive suboptimal algorithms based on alternating optimization. Then we consider the scenario that CSI is imperfect and propose heuristic algorithms. The secrecy performances of the OFDMA and TDMA systems with jamming from the SWIPT IoT devices are compared using extensive simulations. It is shown that the secrecy performance of the OFDMA system is superior over the TDMA system. It is also shown that the algorithm with imperfect CSI is inferior over the algorithm with perfect CSI, but it can outperform the benchmark algorithm with perfect CSI and without jamming.

The machine-to-machine (M2M) communication is an emerging technology that is widely utilized in a vast number of industrial Internet-of-Things (IIoT) applications. Due to the diversity of IIoT applications, provisioning of heterogeneous delay requirements of delay-sensitive machine type devices (MTDs) while optimizing the access efficiency of delay-tolerate MTDs becomes a critical challenge for M2M communications. To address this issue, a multigroup analytical framework for massive random access of M2M communications in IIoT is proposed in this article. Specifically, we consider delay-sensitive MTDs and delay-tolerate MTDs coexist in the network, and those MTDs are divided into multiple groups according to their delay requirements. The access behavior of each MTD is characterized by a double-queue model. Based on this model, the throughput and the mean access delay of each group are characterized. It is found that for each group, the mean access delay decreases as the throughput increases and is minimized when the throughput is maximized. To achieve the maximum throughput of delay-tolerate MTDs under delay constraints of delay-sensitive MTDs, the backoff parameters of delay-sensitive MTDs should be tuned according to the delay constraints while that of delay-tolerate MTDs should be tuned further according to the aggregate packet arrival rate and the number of MTDs in each group. It is further demonstrated that the optimal tuning of backoff parameters is robust against the burstiness of input traffic. The analysis sheds important light on the access design of M2M communications in IIoT with delay constraints.

Time-domain wireless channels are generally modeled by Tapped Delay Line (TDL) model and its variants. These models are not effective for channel representation and estimation when the number of multipath taps is large. Compressive sensing (CS) provides a powerful tool for sparse channel modeling and estimation. Most of the research has been focusing on sparse channel estimation, while sparse channel modeling (SCM) is rarely considered for centimetre-wave channels. In this paper, we investigate statistical sparse channel modeling, using both measured and simulated channels over a frequency range of 6 to 8.5 GHz. We first introduce the triple equilibrium principle to explore the trade-off between sparsity, modeling accuracy, and algorithm complexity in SCM, and provide a methodology for characterizing the sparsity of time-domain channels using single-measurement-vector compressive sensing algorithms. Using mainly the selected wavelet dictionary and various CS reconstruction (aka recovery) algorithms, we then present comprehensive statistical sparse channel models, including channel sparsity, magnitude decaying profile, sparse coefficient distribution and atomic index distribution. Connections between the parameters of conventional TDL and sparse channel models are mathematically established. We also propose three methods for generating simulated channels from the developed sparse channel models, which validates their effectiveness.

Traffic delay is a key metric to measure the quality-of-service of next-generation wireless communication networks. In this paper, we consider a cloud radio access network architecture with a hierarchical structure of virtual controllers and multiple clusters of remote radio heads (RRHs). A high-level controller coordinates control plane decisions among local controllers and each local controller is in charge of a cluster of RRHs. Moreover, each local controller is equipped with one server for creating virtual machines (VMs) to execute the users' baseband processing tasks. Then, under the considered architecture, we aim to minimize the average delay consisting of task execution delay and signal transmission delay under total power constraint, by joint optimization of task scheduling and resource allocation, including VM allocation and RRH assignment. Due to the non-deterministic polynomial-time hardness (NP-hardness) of the joint optimization problem, we translate it into a matroid constrained submodular maximization problem and propose heuristic algorithms to find solutions with 0.5-approximation. Besides, both centralized and distributed control schemes are considered. In the centralized control scheme, all decisions about task scheduling, VM allocation, and RRH assignment are made in the high-level controller. But in the distributed control scheme, the high-level controller is only in charge of task scheduling based on graph theory and the local controllers are responsible for their respective VM allocation and RRH assignment. The simulation results show that the proposed algorithms can achieve better performance than the separate optimization of VM allocation and RRH assignment.

Virtual network embedding (VNE) refers to the resource allocation problem in network virtualization (NV). In the literature, researchers have proposed multiple VNE algorithms. These algorithms aim at embedding more and more requested virtual networks (VNs) onto the underlying networks and maximizing embedding revenues. Prior VNE algorithms mostly belong to the two-stage (separated node and link embedding) mapping algorithm category. Some other VNE algorithms embed each VN in one stage by using mixed integer linear programming (MILP) approach. There is a lack of one-stage heuristic algorithm, enabling to embed nodes and links in one mapping stage. In addition, each requested VN needs to be mapped in polynomial time so as to be promoted to future dynamic VN service application and real-time VNs embedding. Therefore, we propose a real-time and one-stage heuristic mapping algorithm (VNE-RTOS). Numerical simulations are conducted to validate that our VNE-RTOS earns more embedding revenues by approximately 3.4% over typical two-stage heuristic embedding algorithms (e.g. GRD-VNE) while achieving the same substrate resource utilization.

Channel sparsity is well exploited for channel estimation, but there is very limited work on sparse channel modelling, which studies and characterizes the statistical properties of sparse channel coefficients. In this paper, we study sparse channel modelling using real measured channel data in off-body signal propagation. We propose multi-measurement vector based compressive sensing algorithms for extracting sparse channel coefficients, study the statistical properties of these extracted coefficients, and develop an algorithm for generating simulated channels using the statistical sparse model. The proposed method can be directly applied to other channel measurements, and is very useful for channel simulation and developing advanced sparse channel estimation schemes.

In this paper, a multi-tag selection combining (SC) scheme is proposed in a radio frequency identification (RFID) backscatter communication system which contains a reader and L tags. The proposed scheme could efficiently combat the double-fading channel in RFID system since the diversity of multiple tags is utilized. Different from the conventional one-way communication system, the forward link channel and backscatter link channel could be correlated in backscatter communication system. Hence, we investigate the system performance under fully correlated and partially correlated Nakagami-m fading channels. The closed-form analytical outage probabilities for the two correlation circumstances are derived. Furthermore, the asymptotic outage probability is derived in a high signal-to- noise ratio (SNR) range, from which the insight on how the channel and system parameters affect the outage performance is gained. Finally, the simulation results are presented to verify the theoretical analysis.

In this paper, we study the hybrid beamforming for the wideband mmWave MIMO-OFDM system with observation of beam squint. Firstly, we present the beam squint effect in the wideband mmWave system. We further characterize the mmWave wideband channel from Saleh-Valenzuela model. Secondly, in the full-connected hybrid architecture, we seek for the optimal hybrid precoder in the mmWave MIMO-OFDM system, which aims to maximize the spectral efficiency. The precoder design problem is formulated as the matrix factorization in this paper. To find the optimal precoder, wideband hybrid precoding (WHP) algorithm is proposed by using the manifold optimization. Finally, simulation results show the proposed algorithm could approximate to the optimal digital precoder in term of spectral efficiency for the wideband mmWave MIMO-OFDM system.

In this paper, we aim to minimize the network power consumption (NPC) in a downlink cloud radio access network. Not only the powers consumed at remote radio heads and fronthaul links for transmission, but also the power consumed at the baseband unit pool for computation is considered. We formulate a joint NPC minimization problem as a mixed timescale issue which can be regarded as a combination of two power minimization problems for computation and transmission, where the former is a slow timescale issue since task scheduling and computation resource allocation are usually executed in a large time space whereas the latter is a fast timescale issue due to the dependence on small-scale fading. To deal with timescale challenge, we introduce approximate results of the joint NPC minimization problem according to large system analysis and turn it into a slow timescale issue because the approximations are only dependent on statistical channel information. We propose an iterative coordinate descent algorithm based on branch-and-bound algorithm to find solutions to the joint NPC minimization problem. Numerical results show that the NPC decreases as the delay constraint increases but increases if the execution efficiency or computing capability of servers is degraded.

Network virtualization (NV) is a promising approach to remove the ossification of current Internet. Virtual network embedding (VNE) is the key issue in NV which efficiently and effectively maps various of virtual networks (VNs), with different node and link resource requests, onto the shared substrate network(s) with finite underlying resources. Previous VNE algorithms in the literature are mostly heuristic. Single network topology attribute and each node's local resources are assisted to rank nodes in most heuristic algorithms, leading to inefficient resource utilization of substrate network in the long run. To deal with this issue, we propose the network topology attribute and network resource-considered algorithm (VNE-NTANRC). The VNE-NTANRC algorithm adopts a novel node-ranking approach to rank all substrate and virtual nodes before embedding each given VN. The novel node-ranking approach has two subapproaches and considers five important network topology attributes and global network resources altogether. One subapproach is able to calculate all node values (NoV) directly. The other subapproach, stimulating from the Google PageRank website algorithm, enables to calculate NoVs in a stable state. Simulation results reveal that VNE-NTANRC algorithm outperforms typical and latest heuristic algorithms, only considering single network topology attribute and local resources.

In this paper, we consider an uplink heterogeneous cloud radio access network (H-CRAN), where a macro base station (BS) coexists with many remote radio heads (RRHs). For cost savings, only the BS is connected to the baseband unit (BBU) pool via fiber links. The RRHs, however, are associated with the BBU pool through wireless fronthaul links, which share the spectrum resource with radio access networks. Due to the limited capacity of fronthaul, the compress-and-forward scheme is employed, such as point-to-point compression or Wyner-Ziv coding. Different decoding strategies are also considered. This paper aims to maximize the uplink ergodic sum-rate (SR) by jointly optimizing quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links, which is a mixed time-scale issue. To reduce computational complexity and communication overhead, we introduce an approximation problem of the joint optimization problem based on large-dimensional random matrix theory, which is a slow time-scale issue, because it only depends on statistical channel information. Finally, an algorithm based on Dinkelbach's algorithm is proposed to find the optimal solution to the approximate problem. In summary, this paper provides an economic solution to the challenge of constrained fronthaul capacity and also provides a framework with less computational complexity to study how bandwidth allocation and fronthaul compression can affect the SR maximization problem.

In this paper, we consider the network power minimization problem in a downlink cloud radio access network (C-RAN), taking into account the power consumed at the baseband unit (BBU) for computation and the power consumed at the remote radio heads and fronthaul links for transmission. The power minimization problem for transmission is a fast time-scale issue, whereas the power minimization problem for computation is a slow time-scale issue. Therefore, the joint network power minimization problem is a mixed time-scale problem. To tackle the time-scale challenge, we introduce large system analysis to turn the original fast time-scale problem into a slow time-scale one that only depends on the statistical channel information. In addition, we propose a bound improving branch-and-bound algorithm and a combinational algorithm to find the optimal and suboptimal solutions to the power minimization problem for computation, respectively, and propose an iterative coordinate descent algorithm to find the solutions to the power minimization problem for transmission. Finally, a distributed algorithm based on hierarchical decomposition is proposed to solve the joint network power minimization problem. In summary, this paper provides a framework to investigate how execution efficiency and computing capability at BBU as well as delay constraint of tasks can affect the network power minimization problem in C-RANs.

In cloud‐assisted electronic health care (eHealth) systems, a patient can enforce access control on his/her personal health information (PHI) in a cryptographic way by employing ciphertext‐policy attribute‐based encryption (CP‐ABE) mechanism. There are two features worthy of consideration in real eHealth applications. On the one hand, although the outsourced decryption technique can significantly reduce the decryption cost of a physician, the correctness of the returned result should be guaranteed. On the other hand, the malicious physician who leaks the private key intentionally should be caught. Existing systems mostly aim to provide only one of the above properties. In this work, we present a verifiable and traceable CP‐ABE scheme (VTCP‐ABE) in eHealth cloud, which simultaneously supports the properties of verifiable outsourced decryption and white‐box traceability without compromising the physician’s identity privacy. An authorized physician can obtain an ElGamal‐type partial decrypted ciphertext (PDC) element of original ciphertext from the eHealth cloud decryption server (CDS) and then verify the correctness of returned PDC. Moreover, the illegal behaviour of malicious physician can be precisely (white‐box) traced. We further exploit a delegation method to help the resource‐limited physician authorize someone else to interact with the CDS. The formal security proof and extensive simulations illustrate that our VTCP‐ABE scheme is secure, efficient, and practical.

We study the WiFi offloading problem in smart communications and adaptively seek for the optimal offloading strategies with the consideration of the mobility management and the dynamical nature of network state. With users mobility management, we formulate the offloading ratio optimization problem based on Markov process. Then, we propose a novel Congestion‐Optimal WiFi Offloading (COWO) algorithm based on subgradient method, which aims to obtain the optimal offloading ratio for each access point (AP) to maximize the throughput and minimize the network congestion. Due to the computational complexity of subgradient method, we further improve the COWO algorithm by the equivalent transformation. By viewing all the APs as one virtual WiFi network, we try to optimize the identical offloading ratio for virtual WiFi network and develop a Virtualized Congestion‐Optimal WiFi Offloading (VCOWO) algorithm with lower complexity. Under the equivalent conditions, the performance of the VCOWO algorithm could well approximate the optimal results obtained by the COWO algorithm. It is found that the VCOWO algorithm could obtain the upper bound of multiple APs WiFi offloading performance. Moreover, we investigate the impacts of user mobility on the WiFi offloading performance. Simulation results show that the proposed algorithm could achieve higher throughput with lower network congestion compared with other current offloading schemes.

In this paper, we analyze the effect of phase noise on the downlink ergodic sum-rate of a cloud radio access network. The system comprises of one baseband processing unit (BBU) on the cloud server which coordinates M multi-antenna remote radio heads (RRHs) serving K single-antenna users using regularized zero-forcing precoding. We assume the BBU has all users' data and imperfect channel state information and communicate with RRHs via optical fibers which are referred to as fronthaul links. The effect of phase noise both at RRHs and users is also taken into consideration. A deterministic approximation of downlink ergodic sum-rate is derived based on large dimensional random matrix theory when the numbers of antennas at RRHs and users are asymptotically large with a fixed ratio. From simulation results, it is confirmed that the deterministic approximation is accurate and the effect of phase noise is shown to result in a significant reduction in system performance.

In this paper, we consider the uplink transmission of a heterogeneous cloud radio access network, where a macro base station (BS) and many remote radio heads (RRHs) coexist to serve user equipment units. For cost-savings, only the BS is connected to the baseband unit (BBU) pool via fiber links, whereas the RRHs are associated with the BBU pool through wireless fronthaul links with limited capacities. By employing Wyner-Ziv (WZ) coding scheme, the RRHs first compress the received signal and then transmit the corresponding quantized version to the BBU pool. We derive deterministic equivalent for ergodic uplink sum rate and use this result to jointly optimize quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links. An algorithm based on Dinkelbach's algorithm is also proposed to determine the optimal solutions. Numerical results show that as the normalized fronthaul capacity increases, more bandwidth is allocated to radio access networks. Besides, uniform quantization with WZ coding across RRHs can achieve near-optimal performance under high signal-to-quantization-noise ratio.

The unprecedented data increase has imposed great challenges to cellular networks. Traffic offloading by heterogeneous radio access technologies (RATs) is an effective approach for enhancing network capacity. Without costly and time-consuming infrastructure investments, WiFi offloading is deemed as the prospective evolution for the heterogeneous integration. However, previously proposed schemes mainly focus on alleviating burden by offloading data to WiFi as much as possible, without systematic considerations of the network congestion. In this paper, based on Markov process, we investigate the congestion offloading problem in user random mobility model, and prove it could be solved in subgradient method with equivalent transformation. Moreover, the congestion-aware WiFi offloading algorithm is proposed for multiple WiFi APs network offloading scenario, aiming to balance the capacity increase with congestion characterized by blocking probability. In addition, the upper bound and lower bound of blocking probability is deduced in optimization. Simulation results show that, by optimizing user offloading probability, the algorithm proposed could achieve maximum throughput with lower blocking probability.

In 5G heterogeneous evolution, the unlicensed band has captured much attention. Specified by 3GPP Release 13, LTE WLAN aggregation (LWA) is deemed as an effective approach for spectrum integration of 5G heterogeneous networks (HetNet). However, most of previous works about LWA lie in the architecture design, and rarely investigate LWA algorithm analytically. In this paper, we formulate the network access and aggregation problem for delay- tolerant application in multiple slots, and further develop a delay-aware LTE WLAN aggregation algorithm (DLWA) based on dynamic programming, which is aimed to minimize the user payment with QoS requirement. To reduce the complexity, we prove optimal decision policy and simplify the searching space of scheme sets. Simulation results show that, comparing with the current WLAN interworking solutions, the algorithm could lower the payment and achieve high completion probability under specified transmission deadline. The framework presented can support WLAN offloading scheme as well, which enables the best use of unlicensed resource.

In heterogeneous networks (HetNets), the network energy efficiency is critically determined by the base station (BS) deployment density. In this paper, we consider a BS density optimization problem by turning on only a fraction of micro BSs according to an activation ratio to minimize the network average power consumption per area in a 2- tier HetNet. In contrast to previous studies where a BS is assumed to be transmitting packets all the time, such that the network power consumption monotonically increases as the BS density increases, we assume that each BS can be busy or idle depending on the dynamic packet arrivals. The network power consumption is thus closely related to the average traffic intensity of each tier. With the assumption of universal spectrum reuse, the average traffic intensity of each tier is found to be uniquely determined by a set of fixed-point equations, based on which the network average power consumption per area is characterized. Simulation results demonstrate that the network average power consumption per area can be minimized by properly tuning the activation ratio. It is further revealed that the optimal activation ratio increases as the mean packet arrival rate of each user increases.

This paper focuses on a multi‐user full‐duplex massive multiple‐input multiple‐output amplify‐and‐forward relaying system with low‐resolution analogue‐to‐digital convertors (ADCs). By modelling the loop‐interference and quantisation noise as additive noise, we perform the analyses of the achievable spectral efficiency (SE) by considering the two canonical processing schemes, maximum ratio combining/maximal ratio transmission (MRC/MRT) and zero‐forcing receive/zero‐forcing transmission (ZFR/ZFT), respectively. Specially, we first obtain the tractable closed‐form expressions of the achievable SE. Then, we present the asymptotic analyses under three different power‐scaling scenarios. Comparison analysis shows that the low‐resolution quantisers impose more performance loss on ZFR/ZFT schemes than MRC/MRT ones. At the same time, it is found that with different power‐scaling schemes, the systems have different capabilities to restrict the effect of loop interference and low‐resolution ADCs. Specially, the impact of both the loop‐interference and quantisers on achievable SE can be restricted effectively only under the power‐scaling scenario where the relay's transmission power is scaled down with the number of transmit antennas and the sources’ transmission power is fixed. For the power‐scaling case where the transmission powers of both relay and sources are scaled down simultaneously with the number of antennas, only the effect of the loop interference can be restricted, but the one of low‐resolution quantisers remains.

This study studies a multi‐pair massive multiple‐input multiple‐output (MIMO) relaying network, where multiple pairs of users are served by a single relay station with a large number of antennas, and the amplify‐and‐forward protocol and zero‐forcing (ZF) beamforming are used at the relay. The authors investigate the ergodic achievable rates for the users and obtain tight approximations in closed form for finite number of antennas. The rate performance and power efficiency are studied based on the analytical results for asymptotic scenarios, and the effect of scaling factors of transmit powers for users and relay are discussed. The closed‐form expressions enable us to determine the optimal user scheduling which maximizes the ergodic sum‐rate for the selected pairs. A simplified user scheduling algorithm is proposed which greatly reduces the average complexity of the optimal use pair search without any rate loss. Moreover, the complexity reduction for the proposed algorithm increases nonlinearly with the increase of the number of user pairs, which indicates that the simplified scheduling algorithm has notable advantages when the number of users is increased. The tightness for the analytical approximations and the superiority of the proposed algorithm are verified by Monte‐Carlo simulation results.

Efficient use of available energy resources for real‐time multimedia communication with a quality guarantee is one of the main challenges for future mobile computing systems. To meet the high quality‐of‐experience (QoE) demand of multimedia services, we propose a novel quality‐driven joint source‐channel coding (JSCC) scheme with an unequal error protection (UEP) technique that allocates bits by optimising source intra refreshing rates and channel correction coding rates. The key contribution of this work is that the frame importance diversity at the application layer is jointly considered with the error correction techniques and resource constraints at lower layers. For any given bit budget, the JSCC model is capable of allocating adaptive bits between source coding and channel coding among media streams. The source‐aware UEP scheme is capable of dynamically allocating the error correction bits among frames, achieving the maximum overall QoE. Simulation results demonstrated that significant improvement in multimedia quality and remarkable energy/time conservation can be achieved by deploying the proposed group‐based JSCC strategy, although the complexity is limited.

An empirical stochastic discrete tapped delay line (DTDL) power delay profile (PDP) model is presented. It is used for characterising the multipath effects under indoor stair environment. In this model, the amplitude at each DTDL tap and stair step follows the Nakagami distribution. Its scale parameters are lognormally distributed, and its shape parameters are distance and propagation delay dependent. Then, the procedure for simulating the PDPs is given. In addition, the average PDP, root mean square delay spread and capacity extracted using the measured and simulated channels are compared to validate the accuracy of the proposed model. Finally, a measurement‐based channel simulator is developed by implementing an orthogonal frequency division multiplexing communication procedure on the simulated channel. These works can provide important information about the designs of the physical layer algorithms in small cells scenarios.

A root mean square (RMS) delay spread model in indoor femtocell scenarios is proposed. The proposed model is based on extensive channel sounding of indoor stair, corridor and office environments. In this model, the RMS delay spread is described as a linear function of the path loss, and a normal stochastic variable is introduced and utilised to characterise the deviation of the measured RMS delay spread from the linear function. The proposed model can be used to simulate the RMS delay spread directly from extracted model parameters and the separation of the transmitting and receiving antennas. The closed‐form formulas for fast calculating the mean value and variance of the RMS delay spread using some deterministic values, including the femtocell coverage distance and the parameters of the proposed model, are derived. The validity of the proposed model is verified by comparing the cumulative distribution functions and the statistical values of the measured and simulated RMS delay spread.

We study a novel communication technique, ambient backscatter, that utilizes radio frequency signals transmitted from an ambient source as both energy supply and information carrier to enable communications between low-power devices. Different from existing noncoherent schemes, we here design the semi-coherent detection, where channel-related parameters can be obtained from unknown data symbols and a few pilot symbols. In order to obtain a benchmark for overall detection, we first derive a maximum likelihood detector assuming a complex Gaussian ambient source, and the closed-form bit error rate (BER) is computed. To release the dependence on prior knowledge of the ambient source, we next derive a type of robust design, called an energy detector, with the ambient signal being either complex Gaussian or phase shift keying (PSK). The closed-form detection thresholds, analytical BERs, and outage probability are provided correspondingly. Interestingly, the complex Gaussian source would cause an error floor, while the PSK source does not, which brings nontrivial indication of constellation design as opposed to popular Gaussian-embedded literatures. We also propose an effective approach to estimate detection-required parameters rather than channels themselves. Numerical simulations are finally presented to verify theoretical results.

Small-cell networks and massive multiple-input multiple-output (MIMO) systems are regarded as important candidate techniques for 5G communication systems. This paper considers a heterogeneous network composed of a macrocell tier overlaid with an extremely dense tier of small-cells. In the network, the macrocell base station (BS), which applies massive MIMO, does not only serve macro user equipment units but also provides wireless backhaul for small-cell access points (APs). The wireless backhaul shares the same spectrum resource with radio access networks without creating extra spectrum resources. However, due to the densification of small-cells, the inter- and intra-tier interferences become severe. To mitigate the interferences, we use the regularized zero-forcing precoding combined with a projection technique is used at the BS in downlink (DL) to avoid interference to the APs in uplink (UL). Meanwhile, the joint linear minimum mean square error detection is applied in UL to mitigate the inter-tier interference. We derive deterministic expressions for ergodic UL and DL sum rates (SRs) by leveraging the large-dimensional random matrix theory. The expressions only depend on statistical channel information and can be used to optimize the bandwidth division between radio access links and wireless backhaul, as well as the time allocation between DL and UL operation intervals. Numerical results show that the deterministic SR equivalents are accurate and that the proposed resource allocation method can significantly improve system performance.

Ambient backscatter, an emerging communication mechanism where battery-free devices communicate with each other via backscattering ambient radio frequency (RF) signals, has achieved much attention recently because of its desirable application prospects in the Internet of Things. In this paper, we formulate a practical transmission model for an ambient backscatter system, where a tag wishes to send some low-rate messages to a reader with the help of an ambient RF signal source, and then provide fundamental studies of noncoherent symbol detection when all channel state information of the system is unknown. For the first time, a maximum likelihood detector is derived based on the joint probability density function of received signal vectors. In order to ease availability of prior knowledge of the ambient RF signal and reduce computational complexity of the algorithm, we design a joint-energy detector and derive its corresponding detection threshold. The analytical bit error rate (BER) and BER-based outage probability are also obtained in a closed form, which helps with designing system parameters. An estimation method to obtain detection-required parameters and comparison of computational complexity of the detectors are presented as complementary discussions. Simulation results are provided to corroborate theoretical studies.

In this paper, we consider a heterogeneous network in which a macro-cell tier is overlaid with a very dense tier of small cells. The macro-cell base station (BS) that applies a massive MIMO scheme not only serves the macro user equipment but also provides a wireless backhual for small-cell access points (APs). These APs serve their associated small-cell user equipment. A reverse time division duplex transmission protocol is utilized. To avoid interference toward the APs in the uplink (UL), regularized zero-forcing precoding combined with a projection technique is utilized at the BS in the downlink (DL). We derive deterministic expressions for ergodic UL and DL sum rates (SRs) under the assumption that perfect channel state information is available and use these results to optimize the spectrum division between radio access links and the wireless backhaul. Simulation results suggest that the deterministic SR approximations are accurate and that system performance can be significantly improved through the optimization of spectrum division.

We investigate the achievable uplink rate of multi-cell massive multi-input multi-output (MIMO) systems considering pilot reuse across cells. Based on tractable approximations for the achievable rates, we derive the optimal pilot length to maximize the sum rate per cell. Interestingly, it is found that, for moderately large numbers of base station (BS) antennas, the optimal pilot length is above the minimum feasible value, and the gains brought by the extra amount of training diminish as the reuse factor grows. Simulation results confirm the accuracy of our analysis.

The influence of the person density on the indoor MIMO channel models is experimentally investigated. The path loss is modeled as a log-distance function adding additional attenuations related to both of the distance and person density. Then, the shadowing, the root mean square delay spread, and the channel capacity are described as normal distributed random variables. Their mean value and the standard deviation are depicted as the sum of the basic values (no person in the channel) and the person density correction factors. Finally, the eigenvalues of the channel matrix are found to be a Gamma random variable with the person density dependent shape and scale parameters.

This study proposes a quality‐driven approach that jointly selects the frequency reuse mode of device‐to‐device (D2D) links at lower layers, the video coding mode of frames, and multiple paths at higher layers for multimedia transmission in a cellular network with multi‐antenna relays placed at the intersection of adjacent cells. Based on power control of the shared relay, the user‐centered mode selection problem is formulated by maximising the quality of service (QoS) with total energy consumption constrained. Underlay mode and overlay mode are both considered. For each frame, one coding mode (I, P, or B) and multiple paths (cellular links and/or D2D links with/without relays) are selected to optimise the received video quality expectation. Different retransmission polices are considered to give higher protection levels to paths with higher transmission rates. The authors’ evaluation shows that the proposed unified power allocation and mode control scheme could enhance the perceived QoS as changing the energy constraint and the distances of different paths. The underlay mode is optimal in most cases. It provides evidences that it is not always the best to code all frames as I‐frames, to initialise the relay transmission power as the maximum, or to select one single path for video transmission.

In modern days, breakthroughs in information and communications technologies lead to more and more devices of every imaginable type being connected to the Internet. This also strengthens the need for protection against cyber-attacks, as virtually any devices with a wireless connection could be vulnerable to malicious hacking attempts. Meanwhile, honeypot-based deception mechanism has been considered as one of the methods to ensure security for modern networks in the Internet of Things (IoT). In this paper, we address the problem of defending against attacks in honeypot-enabled networks by looking at a game-theoretic model of deception involving an attacker and a defender. The attacker may try to deceive the defender by employing different types of attacks ranging from a suspicious to a seemingly normal activity, while the defender in turn can make use of honeypots as a tool of deception to trap attackers. The problem is modeled as a Bayesian game of incomplete information, where equilibria are identified for both the one-shot game and the repeated game versions. Our results show that there is a threshold for the frequency of active attackers, above which both players will take deceptive actions and below which the defender can mix up his/her strategy while keeping the attacker's success rate low.

This paper presents an analytical performance investigation of both beamforming (BF) and interference cancellation (IC) strategies for a device-to-device (D2D) communication system underlaying a cellular network with an M-antenna base station (BS). We first derive new closed-form expressions for the ergodic achievable rate for BF and IC precoding strategies with quantized channel state information (CSI), as well as, perfect CSI. Then, novel lower and upper bounds are derived which apply for an arbitrary number of antennas and are shown to be sufficiently tight to the Monte-Carlo results. Based on these results, we examine in detail three important special cases including: high signal-to-noise ratio (SNR), weak interference between cellular link and D2D link, and BS equipped with a large number of antennas. We also derive asymptotic expressions for the ergodic achievable rate for these scenarios. Based on these results, we obtain valuable insights into the impact of the system parameters, such as the number of antennas, SNR and the interference for each link. In particular, we show that an irreducible saturation point exists in the high SNR regime, while the ergodic rate under IC strategy is verified to be always better than that under BF strategy. We also reveal that the ergodic achievable rate under perfect CSI scales as log2M, whilst it reaches a ceiling with quantized CSI.

© 2015 IEEE. In this paper, we study the multiple-input multiple-output wiretap channel for simultaneous wireless information and power transfer, in which there is a base station (BS), an information-decoding (ID) user, and an energy-harvesting (EH) user. The messages intended to the ID user is required to be kept confidential to the EH user. Our objective is to design the optimal transmit covariance matrix at the BS for maximizing the ergodic secrecy rate subject to the harvested energy requirement for the EH user exploiting only statistical channel state information at the BS. To this end, we begin by deriving an approximation for the ergodic secrecy rate using large-dimensional random matrix theory and the method of Taylor series expansion. This approximation enables us to derive the asymptotic-optimal transmit covariance matrix that achieves the tradeoff for ergodic secrecy rate and harvested energy. The simulation results are provided to verify the accuracy of the approximation and show that a bigger rate-energy region can be achieved when the Rician factor increases or the path loss exponent decreases. We also show that when the transmit correlation increases or the distance between the eavesdropper and the BS decreases, the harvested energy will be increased, while the achieved ergodic secrecy rate decreases.

This paper considers a downlink nonorthogonal multiple access (NOMA) system where the source intends to transmit independent information to the users at target data rates under statistical channel state information. The outage balancing problem is studied with the issues of power allocation, decoding order selection, and user grouping being taken into account. Specifically, with regard to the max-min fairness criterion, we derive the optimal power allocation in closed form and prove the corresponding optimal decoding order for the elementary downlink NOMA system. By assigning a weighting factor for each user, the analytical results can be used to evaluate the outage performance of the downlink NOMA system under various fairness constraints. Furthermore, we investigate the case with user grouping, in which each user group can be treated as an elementary downlink NOMA system. The associated problems of intergroup power and resource allocation are solved. The implementation complexity issue of NOMA is also considered with focus on that caused by successive interference cancellation and user grouping. The complexity and performance tradeoff is analyzed by simulations, which provides fruitful insights for the practical application of NOMA. The simulation results substantiate our analysis and show considerable performance gain of NOMA when compared with orthogonal multiple access.

In this paper, the mode selection (underlay or overlay) for device-to-device (D2D) links at low layers is jointly analyzed with the video coding mode selection and multiple transmission path selection at high layers. For each packet, three video coding modes (I, P, and B) and three transmission paths (cellular link with/without the shared relay and D2D link) are considered to optimize the received video quality expectation. The cross-layer mode selection problem is formulated by maximizing the Quality of Service (QoS) with total energy consumption constrained. Our evaluation shows that the proposed joint mode selection strategy could enhance the perceived QoS with less energy consumption as changing the channel conditions and transmitting power of different paths in a single sector. The coding mode selection is mainly decided by the energy constraint and the path selection mostly depends on the channel conditions.

Energy efficiency (EE) of wireless communications has attracted growing attention in recent years. In this paper, we focus on the EE optimization in cognitive radio networks (CRN) from a perspective of designing MAC frame structure, namely scheduling the sensing-then-transmission time slots. Considering secondary users adopting channel handoff to avoid collisions with primary users, the EE of CRN is modeled, which is a function of the sensing time and the transmission time. Under the constraint of protecting primary users sufficiently, an EE optimization problem is formulated, and the energy- efficient MAC frame is thus obtained by solving the problem. Simulation results confirm theoretical analysis, i.e., the EE of CRN depends on the sensing capability and interference constraint. Moreover, the proposed optimization method for cognitive MAC frame can enhance the EE of CRN effectively.

To obtain an insight about the effect of channel parameters on buffer‐aided relay selection systems, the max‐link selection (MLS) schemes are investigated over independent and non‐identically distributed (i.ni.d) Rayleigh fading channels. Specially, by employing Markov chain, the authors first model the state transition matrix and outage probability. Secondly, they obtain the closed‐form expressions of the corresponding statistic properties. The presented results show: (i) when the relaying channels are asymmetric (but the source–relay links are independent and identically distributed fading, so does the relay–destination links), the MLS scheme outperforms the traditional best relay selection (T‐BRS) and max–max best relay selection (MM‐BRS) schemes. However, when the relaying links are unbalanced severely, the MLS scheme does not provide diversity gain over the T‐BRS and MM‐BRS schemes; and (ii) For the more general i.ni.d fading case, it is observed that the MLS schemes can be inferior to the MM‐BRS schemes. The derivations of this work have values for reference. For example, by using the achieved statistic properties they can further perform the investigation on the delay for the buffer‐aided MLS relaying systems over i.ni.d fading channels, which is a critical issue of buffer‐aided relay selection systems.

A novel stochastic multiple‐input multiple‐output (MIMO) channel model based on the singular value decomposition of the channel matrix is proposed in this study. Under the framework of the proposed model, each of the right singular vectors can be modelled as the product of a stochastic scalar and a non‐random vector, as is each of the left singular vectors. The non‐random vectors, defined as the eigenmodes of the transmitter and receiver, respectively, can be easily extracted from the measurements, so are the singular values of the channel matrix. The implications of the proposed model's parameters that provide further insight into the MIMO channel are interpreted and a way of exploiting the parameters is given. To validate the proposed model, MIMO channel measurement is carried out under different indoor environments and the channel capacity is analysed. It is shown that the proposed model provides a better fit to the measurement results than the other popular stochastic channel models. The proposed stochastic MIMO channel model can be used for the MIMO communication system design and evaluation.

This study investigates the outage probability of device‐to‐device communication assisted by a relay node utilising a one‐way amplify‐and‐forward relaying strategy. The authors assume that all the terminals are equipped with a single antenna and all the users know perfect channel state information. They first derive the exact closed‐form expression for characterising the outage probability performance of the system. They subsequently discuss several special scenarios and obtain the asymptotic results for each of the considered scenarios. The results can be easily computed with only the channel statistics. Based on the analysis in the high signal‐to‐noise ratio regime, closed‐form power allocation policies are developed to improve the outage probability performance. The author's analytical results are validated via Monte Carlo computer simulations.

We consider the downlink relay network, for which a pairwise transmission strategy is suggested with the users being grouped into pairs and the source communicating to each pair of users using a novel pairwise relaying protocol. By utilizing superposition coding (SC), the messages of each two paired users are intentionally transmitted non-orthogonally or simultaneously in the same time-frequency channel to compensate for the bandwidth loss of relaying. We investigate the optimal decoding at the relay and the users, respectively, which are then combined to guide the selection of the power allocation factors of SC and to decide the corresponding decoding order. Then, a largely simplified outage expression of the pairwise relaying protocol is given, based on which we investigate, for the multi-user scenarios, the user pairing problem from the perspective of minimizing the total power consumption under targeted outage probabilities of the users. An easily implemented user pairing algorithm with computational complexity of O(K2) is provided, which achieves a performance that is very close to that of the optimal user pairing which can be solved in polynomial time, O(K3). Numerical results show that the pairwise transmission strategy can significantly decrease the average transmit power.

Energy harvesting (EH) technology has emerged as a promising energy-supplier to unattended wireless systems. In the commonly used harvest-store-use (HSU) scheme, harvested energy is always stored in a battery before its subsequent use. The existence of storage loss in practical battery systems, however, unavoidably reduces the energy efficiency. In this paper, we therefore propose the use of a more efficient harvest-use-store (HUS) architecture for point-to-point data transmission, where the harvested energy is prioritized for use in data transmission while its balance/debt is stored in or extracted from the storage device. We derive the optimal energy polices, under the criterion of throughput maximization, for the HUS architecture on static and block fading channels, and investigate the properties of the resulting power allocation pattern. The optimization is done in the Lagrangian framework, uncovering the special structure of the optimal power pattern and obtaining a closed-form solution conditioned on the knowledge of the block locations for zero battery level. A dynamic programming (DP) based algorithm is developed for locating such blocks in the optimal power patterns. Numerical results are presented to demonstrate the properties of the proposed HUS architecture and its superior performance over the existing schemes.

This paper investigates the sum-rate gains brought by power allocation strategies in multicell massive multiple-input- multiple-output systems, assuming time-division duplex transmission. For both uplink and downlink, we derive tractable expressions for the achievable rate with zero-forcing receivers and precoders, respectively. To avoid high-complexity joint optimization across the network, we propose a scheduling mechanism for power allocation, where, in a single time slot, only cells that do not interfere with each other adjust their transmit powers. Based on this, corresponding transmit power allocation strategies are derived, aimed at maximizing the sum rate per cell. These schemes are shown to bring considerable gains over equal power allocation for practical antenna configurations (e.g., up to a few hundred). However, with fixed number of users N, these gains diminish as M → ∞, and equal power allocation becomes optimal. A different conclusion is drawn for the case where both M and N grow large together, in which case improved rates are achieved as M grows with fixed M/N ratio, and the relative gains over the equal power allocation diminish as M/N grows. Moreover, we also provide applicable values of M/N under an acceptable power allocation gain threshold, which can be used to determine when the proposed power allocation schemes yield appreciable gains and when they do not. From the network point of view, the proposed scheduling approach can achieve almost the same performance as the joint power allocation after one scheduling round, with much reduced complexity.

This paper addresses the problem that devices are reluctant to participate in local device-to-device (D2D) communications in the purpose of improving the data transmission quality. We propose a novel game-theoretic approach of joint source selection and power control, which enhances the multimedia transmission quality with latency constraint. The proposed approach first analyzes the interactions between the base station (BS) and the devices using a Stackelberg game model. The optimal transmission power and price are obtained for each source device through deriving Stackelberg equilibrium off-line, in which the BS and the device both achieve maximum utilities. Second, the BS schedules the devices according to its equilibrium of signal-to-interference-and-noise ratios in D2D links. Finally, the selected best source devices complete the multimedia transmission with their power controlled. Simulations demonstrate that our proposed scheme can significantly improve multimedia transmission quality with low complexity.

In this paper, we investigate the uplink rate of multicell massive multiple-input multiple-output (MIMO) systems. We assume the channel is estimated through uplink training with MMSE estimation. Unlike previous studies, the channel between users and the base station (BS) in the same cell is modeled to be Ricean fading, in which the fast fading matrix is assumed to have a deterministic component as well as a Rayleigh-distributed random component, and the Ricean K-factor of each user is supposed to be different. The effect of pilot contamination is analyzed and we derive a closed-form approximation for the achievable uplink rate that holds for any finite number of BS antennas (M). Based on it, we find that increasing the proportion of line-of-sight (LOS) component can improve the uplink performance. In particular, with both very large M and Ricean K-factor, the uplink rate grows infinite, which means that the pilot contamination can be eliminated completely. However, the increase of users' transmit power will make the uplink rate approach a constant value even with unlimited M. In addition, we show that with no reduction in the rate performance, each user's power can be most scaled down to 1/√M with Rayleigh fading channel and to 1/M with non-zero Ricean K-factor.

Energy harvesting (EH) technology has become a promising alternative to solve the problem of the energy-limited wireless systems. As such, in this paper, we consider optimization of a single-user wireless communication system, where an EH transmitter operates in harvest-use-store (HUS) mode with the non-ideal rechargeable battery. With the storage efficiency and causality constraints, our key design goal is to maximize the throughput over a finite horizon transmission blocks under a non-fading channel scenario. Assuming the energy harvesting profile is known prior to transmission, we explore the properties of the optimal power allocation solution by the Lagrangian optimization algorithm (e.g., KKT). Further, based on this, we propose an optimal offline policy with the dynamic programming (DP) approach. Numerical results show that our proposed optimal transmission policy is indeed able to improve the overall throughput as compared to existing schemes.

In this paper, we investigate a joint relaying and superposition coding (SupC-R) transmission strategy for downlink networks. By relaxing the orthogonality constraint between different users, the SupC-R strategy facilitates coordination and channel sharing among different data flows, by which the disparity in the channel qualities of different users, as an inherent feature of downlink network topology, is utilized as benefits for potential performance gains. The inter-user interference due to non-orthogonality can be steered by smart design of superposition coding (SupC), including both the selection of the superposition factor and the decoding order. At the same time, channel sharing saves time and leads to more efficient utilization of network resources. The analytical and numerical results show significant gains of SupC-R over its conventional relaying counterpart. The effect of the channel disparity on the design of SupC and the performance of the resultant SupC-R strategy is highlighted.

This paper studies the outage probability of device-to-device (D2D) communication aided by another D2D user using the two-way amplify-and-forward (AF) relaying protocol. We first discuss the outage behavior under strong and weak interference from the cellular network. Then the exact expressions for the outage probability under the two cases are derived. Based on these results, we give tight approximations in the high signal-to-noise (SNR) regime under the two cases. Numerical results show that the outage behavior for the relay aided D2D link can be greatly enhanced without extra power. Analytical results are validated via comparisons with the Monte-Carlo simulations.

In the existing literature about cognitive radio opportunistic relaying (CR‐OR) systems, the first‐order statistics such as outage probability are investigated widely. However, for the second‐order statistics, such as average outage duration (AOD) and average outage rate (AOR), there is not open works, still. To obtain a comprehensive cognition on the behaviour of mobile communication systems, this study focuses on the second‐order statistical properties of CR‐OR systems. There are two CR‐OR schemes considered, in which the canonical amplify‐and‐forward (AF) and reactive decode‐and‐forward (RDF) are employed, respectively. Since the equivalent end‐to‐end signal‐to‐noise ratio (SNR) of AF CR‐OR is complicated such that it is very difficult to obtain the closed‐form solution to AOR of AF CR‐OR schemes, the high SNR approximation in AF CR‐OR schemes is employed. For the two schemes, first the closed‐form solutions to AORs and AODs are obtained by using appropriate mathematical proof. Based on the derivations, the comparison analyses about AORs and AODs of the two schemes are provided. The comparison results show that, under high SNR approximation, the AF CR‐OR scheme achieves the same AOR and AOD as RDF CR‐OR. Finally, the impact of system parameters on AORs and AODs is provided.

This study considers communication from a source to a destination with the aid of a set of cooperative relaying nodes. Unlike previous studies in energy efficiency, the authors studied the effect of relay placements together with different relay‐selection timing on the performance. The cooperative relaying schemes for a general relay placement and some specialised relay placements are characterised and analysed by a Markov chain model. They derive the expressions for the throughput and the expected energy consumption for both proactive and reactive relay selection for different relay placements and densities. By using the analytical expressions, the authors find the optimal relay locations for different relay‐selection schemes to achieve higher energy efficiency with the consideration of system throughput. The performance improvements offered by the authors proposed relay placement are demonstrated by numerical results. Moreover, the two new cooperative relaying schemes with selection combining for a certain relay placement are discussed. Their throughput and energy consumption are also derived and compared with the existing techniques.

In this study, the authors focus on the tradeoff between spectrum sensing and energy efficiency of cognitive radio networks (CRN). Considering two interference‐avoidance schemes, that is, channel handoff and stop‐and‐wait, the authors, respectively, model the energy efficiency (EE) of CRN as the mean of the throughput‐to‐power ratio. Research shows that stop‐and‐wait scheme is a special case of channel handoff from an EE perspective. Based on the proposed EE model, the authors build up an EE optimisation problem under the constraint of the sensing quality, and formulate the sensing‐energy efficiency tradeoff (SET) for CRN. The similarity and difference between the SET and the sensing‐throughput tradeoff are also investigated. Simulation analysis confirms that there is indeed an optimal sensing time to make the EE maximum, and it is larger than that for maximum throughput. Another interesting result is that the EE and the throughput of CRN can be enhanced together by optimising MAC frame structure in combination with sensing bandwidth adjustment and power control. Our work provides some insights for green CRN in view of MAC frame optimisation.

Providing end-to-end reliability for data transmission is still an intractable challenge for delay-sensitive wireless multimedia networks. This paper deals with an unequal error protection (UEP) scheme for scalable video delivery over packet-lossy networks using forward error correction (FEC). The proposed cross-layer approach allows the wireless multimedia networks to jointly optimize the application layer and the error protection strategies available at the lower layers. The redundancy is tuned in accordance with both the wireless channel condition (as indicated by symbol error rate) and the time limit (as indicated by parity budget). Simulations demonstrate that the proposed cross-layer grouping scheme can significantly improve video transmission quality by allocating more correction bits to the vital frames, while the time constraint is satisfied and the complexity associated with performing this group allocation algorithm is reduced.

In this paper, we study the achievable uplink rates of massive multiple-input multiple-output (MIMO) systems using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming imperfect channel state information (CSI). Unlike all previous studies, the fast fading MIMO channel matrix here is modeled to have an arbitrary-rank deterministic component as well as a Rayleigh-distributed random component. In particular, it is found that with a non-zero Ricean K-factor, the approximations and the exact uplink rates converge to the same constant value if the number of base station antennas, M, grows large, while the transmit power of each user is scaled down proportionally to 1/M. However, if the channel is Rayleigh fading, we can only cut the transmit power of each user proportionally to 1/√M. In addition, we show that with increasing Ricean K-factor, the uplink rates will converge to fixed values for both MRC and ZF receivers.

To overcome the performance loss caused by the N th single best relay selection schemes, in this study, the authors present the N–R th dual best relays selection schemes. In the proposed schemes, there are two relays selected out of the available relays to forward the received signals to the destination by using the decode‐and‐forward protocol. The statistical descriptions of instantaneous end‐to‐end received signal‐to‐noise ratios (SNR) are investigated over independent and non‐identically distributed Nakagami‐ m fading channels. By using the appropriate mathematical proof, the authors firstly obtain the closed‐form expressions to the probability density function and cumulative distribution function of instantaneous end‐to‐end SNR. Then, based on the derived results, the outage probability and average symbol error rate (SER) of the proposed N–R th dual best relays selection schemes are investigated. The comparison analyses between the N th schemes and the N–R th schemes show that the proposed N–R th dual best relays selection schemes can improve greatly the performance of opportunistic relaying systems such as outage probability and average SER. Especially, the proposed dual best relays selection scheme can obtain more outstanding improvement on SER.

Using harvestable energy in Wireless Sensor Networks (WSNs) has gained considerable popularity recently. However, how to utilize the unstable power supply to achieve quality performance is still a challenge. In this paper, we propose a Weight-Optimized Source Rate Optimization (WOSRO) approach which allows the WSN system to adaptively control source coding rates by choosing the most valuable data packets to transmit. In this approach, the packet selection strategy is optimized by considering multimedia distortion reduction, energy cost, energy neutrality constraint and power saving efficiency. Simulation results show that the proposed data packet selecting strategy significantly improves data transmission quality by exploring harvesting-storage energy neutrality and multimedia data packet importance.

Sensing-throughput tradeoff is involved in opportunistic spectrum access (OSA). At the physical layer it's expressed as the tradeoff between false alarm and misdetection, while at the MAC layer it's between maximizing the throughput of secondary users and reducing collisions with primary users. To balance both tradeoffs together drives the optimization of MAC frame structure for OSA. Since channel handoff results in a time overhead for MAC frame, it's first researched by modeling OSA dynamics in the paper. Three handoff cases and their probabilities are deduced, which lead to three application scenarios of MAC frame. Thus the throughput model of secondary network involving channel handoff is proposed. By balancing the misdetection and false alarm probabilities and maximizing the throughput of secondary network subject to the sensing quality and collision avoidance constraints, the optimal sensing time and frame duration are deduced as closed forms. The characteristics of the optimal MAC frame structure are researched as well. Theoretical and simulated results disclose the impacts of channel handoff and spectrum sensing on MAC frame structure and the achievable throughput of secondary network, which provide an insight for OSA design and improvement.

This paper addresses the issue of non-uniform motion deblurring due to hand shake for a single photograph. The main difficulty of spatially variant motion deblurring is that the deconvolution algorithm can not directly be used to estimate the blur kernel as the kernel of different pixels are different to each other. In this paper, the blurred image is considered as a weighed summation of all possible poses, and we proposed to use a PSO (particle swarm optimization) to optimize the weighed parameters of the corresponding poses after building the motion model of the camera. The main issue of using a PSO for deblurring is that it is generally impossible to obtain the ground true of the observed blurred image, which must be used as the input of the PSO algorithm. To solve this problem, firstly a novel image prediction method is proposed which combines a shock filter and a non-linear structure tensor with anisotropic diffusion. The main advantage of the proposed prediction method is that the deblurring process is not misled by rich texture in the image. Secondly an alternatively optimizing procedure is used to gradually refine the motion kernel and the latent image. Experimental results show that our approach makes it possible to model and remove non-uniform motion blur without hardware support.