The rapid development of communication technology has enabled intelligent vehicles and edge networks, making vehicular crowdsensing an emerging paradigm for data collection and dissemination. Vehicles can be mandated to collaboratively perform large-scale data sensing. However, due to high mobility, availability, and resource constraints, it is difficult to design effective mechanisms to encourage suitable vehicles to complete sensing tasks. To overcome these challenges, this work proposed a two-stage bus-aided vehicular crowdsensing framework, which involves collaboration between the bus system and normal vehicles in the vehicular network. In the first stage, the buses perform sensing, and a modified stable matching is applied to effectively allocate tasks to vehicles while optimizing sensing cost. In the second stage, normal vehicles are selected through contract to perform sensing in areas inaccessible to the buses. The vehicles are offered optimal contracts based on the sensing quality index (SQI). The SQI of a vehicle is obtained by considering some vital metrics, including promptness, reputation, willingness, and commitment. To achieve fairness in contract design, information asymmetry is also considered. Simulations were conducted to validate the effectiveness of the proposed schemes against baseline methods. The proposed schemes achieve remarkable results in various experiments.

Motivated by the increasingly dense deployment of satellite constellations and the passive reflection capabilities of metasurface (MS), this paper proposes a novel satellite-mounted MS-enabled communication (MSC) architecture for beyond-line-of-sight transmission. Different from traditional methods that rely on active satellite relays, the proposed approach utilizes passive signal reflection from the satellite-mounted MS to overcome Earth’s curvature and extend communication range. To ensure the stability and efficiency of the highly dynamic reflection link, the architecture incorporates large-scale satellite selection and communication window prediction methods. The large-scale satellite selection method is developed based on three key geometric factors: the line-of-sight condition, the orbital inclination condition, and the specular reflection point condition, selecting satellites that are more likely to serve as the carriers of the MS for stable connections between transceivers. Meanwhile, the communication window prediction method estimates the probability of link outages and identifies reliable transmission periods, thereby avoiding ineffective link establishment attempts. Numerical results demonstrate the feasibility of the proposed MSC framework, achieving data rates on the order of tens of kbps over several thousand kilometers. The findings also highlight the critical roles of the proposed large-scale satellite selection and communication window prediction methods in enhancing both communication duration and data transmission, and further illustrate how frequency, MS size, satellite altitude, and attitude affect the performance of the MSC system.

In this paper, we propose to employ satellite-mounted reconfigurable intelligent surfaces (RIS) to passively reflect transmitted signals, thereby establishing a virtual line-of-sight link between distant ground stations. Specifically, we take into account the non-negligible scattering effects introduced by the large physical dimensions and high reflectivity of satellite payloads, such as antenna arrays (AAs) and solar arrays (SAs), on the performance of the RIS beamforming. To address this issue, we first develop a comprehensive reflection model that jointly considers the scattered fields of the RIS, AA, and SA, integrating wireless channel modeling with bistatic radar cross-section analysis. Based on this reflection model, we then derive the closed-form expressions for the outage probability and coverage performance, considering dynamic satellite motion and time-varying channel conditions. Moreover, we show how the optimal RIS beamforming for such conditions can be obtained. Numerical results show that, when deploying our proposed system onto the Starlink satellites, a rate of 60 kbps and a coverage area of more than 9.05×105km2can be achieved, demonstrating its applicability in real-world scenarios. The results provided in this paper also offer valuable guidelines for the design of practical RIS-enabled satellite systems.

The spectrum shift to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose the space-frequency Fourier transformation (SFFT)-based wideband beamforming design for RIS-assisted systems. In the proposed design, we exploit SFFT and stationary phase method to yield an approximate closed-form solution of RIS phase shifts, which significantly reduces signal processing complexity. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band to overcome the beam squint effect. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve communication and sensing performance. Furthermore, the proposed method can be extended to generate any expected frequency-domain beampattern which provides valuable insights into the design of novel wideband beamforming for RIS-assisted systems.

Integrated sensing and communication (ISAC) is emerging as a key enabler of future wireless networks. However, existing analyses of ISAC performance commonly assume discrete-time systems, overlooking the impacts of temporal, spectral, and spatial properties. To address this limitation, we establish a unified information model for band-limited continuous-time ISAC systems. In this model, we employ a novel sensing performance metric called the sensing mutual information (SMI). Our analysis demonstrates how SMI serves as a bridge between the mutual information domain and the minimum mean squared error (MMSE) domain. Additionally, we characterize the communication mutual information (CMI)-SMI and CMI-MMSE regions to identify the performance bounds of practical ISAC systems and reveal the trade-off between communication and sensing performances. Moreover, through analysis and numerical results, we derive two valuable insights for the design of ISAC-enabled systems: i) communication prefers waveforms with random amplitude, sensing prefers waveforms with constant amplitude, and both benefit from waveforms with low correlations and random phases; ii) a linear positive proportional relationship exists between allocated time-frequency resources and the achieved communication rate/sensing accuracy.

The technique of digital twin network (DTN) has been considered as a promising technique of network management and control to accommodate disruptive applications for the sixth generation communication (6G) systems. However, it is still challenging for DTN to provide ultimate experience for the emerging services and applications, due to the lack of effective DTN model management strategies. To solve this problem, the model selection and offloading for DTN is studied in this paper. First, a framework of cooperative model selection and offloading for DTN is designed, which can adapt with the limited computation capability of users, and improve the communication efficiency of model offloading. Second, the performance metrics are proposed to effectively evaluate the accuracy loss and the privacy leakage risk of model management for DTN, and tractable expressions are provided for our studied framework. Third, a joint optimization algorithm is designed for model management and transmit power allocation, which can efficiently reduce the accuracy loss and the privacy leakage risk with low processing latency. Finally, the experiment results are provided to show the effectiveness of our introduced performance metrics, and verify the performance gains of proposed optimization algorithm for our studied framework.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) network, where multiple UAVs with caching and computation functionalities are deployed to satisfy the heterogeneous content and service requests from the user equipments (UEs). In order to comprehensively characterize the capability of our considered network in satisfying the UEs’ requests, we define the weighted sum of the content cache hit ratio and the service delay shrinkage ratio as the average quality-of-experience (QoE) of our network and adopt it as the performance metric. Through analysis, we show how the average QoE of our network is dependent on the content cache and service placement decisions at the UAVs, as well as the computation task offloading decisions at the UEs, thus enabling us to formulate an average QoE maximization problem, subject to practical constraints on the UAVs’ caching and computation capabilities. To solve this NP-hard problem, we decompose it into two sub-problems, namely, the content cache and service placement optimization sub-problem and the task offloading optimization sub-problem. Gibbs sampling-based and matching game-based algorithms are proposed to efficiently solve these sub-problems iteratively. Via numerical results, we validate the effectiveness of our proposed algorithms. Compared to various benchmarks, we demonstrate that our proposed algorithms can significantly improve the average QoE of our considered network, especially when the caching and computation resources of the UAVs are limited.

This paper addresses the challenges of “double-fading” effect and coverage limitations encountered by passive intelligent reflecting surface (IRS) by introducing a novel IRS architecture, termed the dual-sided active-intelligent omni-surface (DSA-IOS). This architecture is capable of processing incident signals on both sides with controllable amplitudes and phases. Furthermore, the DSA-IOS is deployed in a multi-cell multiple-input single-output system to alleviate inter-cell interference and combat potential wiretapping from multi-antenna eavesdroppers. Considering eavesdroppers’ statistical channel state information, we introduce a system metric, the expected secrecy rate (ESR), to capture the tradeoff between secrecy rate (SR) and secrecy outage probability (SOP). Our objective is to maximize the system’s expected secrecy energy efficiency by jointly optimizing the beamformers and artificial noise at the base stations and the reflection and transmission coefficients for both sides at the DSA-IOS. To address the design problem, we propose a low-complexity alternating optimization scheme to acquire an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IOS outperforms other advanced IRS architectures in enhancing secure performance due to additional degrees of freedom for superior resource utilization. Our results also validate that the proposed ESR metric effectively balances the tradeoff between SR and SOP by customizing SOP thresholds for individual users.

Integrated sensing and communication (ISAC) has been recognized as a key enabler and feature of future wireless networks. In the existing works analyzing the performance of ISAC, discrete-time systems were commonly assumed, which, however, overlooked the impacts of temporal, spectral, and spatial properties. To address this issue, we establish a unified information model for the band-limited continuous-time ISAC systems. In the established information model, we employ a novel sensing performance metric, called the sensing mutual information (SMI). Through analysis, we show how the SMI can be utilized as a bridge between the mutual information domain and the minimum mean squared error (MMSE) domain. In addition, we illustrate the communication mutual information (CMI)-SMI and CMI-MMSE regions to identify the performance bounds of ISAC systems in practical settings and reveal the trade-off between communication and sensing performances. Moreover, via analysis and numerical results, we provide two valuable insights into the design of novel ISAC-enabled systems: i) communication prefers the waveforms of random amplitude, sensing prefers the waveforms of constant amplitude, and both communication and sensing favor the waveforms of low correlations with random phases; ii) There exists a linear positive proportional relationship between the allocated time-frequency resource and the achieved communication rate/sensing accuracy.

In the design of grant-free multiple access (GFMA) mechanisms, the number of active users (UEs) with heterogeneous delay tolerance is commonly assumed to be known, which is less practical in real-world implementations. In this paper, we propose a network state sensing assisted resource allocation algorithm for the GFMA systems, in which both the delay-sensitive unmanned aerial vehicles (UAVs) and the delay-tolerant terrestrial UEs simultaneously access to the base stations (BSs). Taking the limited radio resources and the practical assumption that the number of active UEs is not available at the BSs into account, we first design a Bayesian-based estimation algorithm that can determine the number of delay-sensitive UAVs and delay-tolerant terrestrial UEs in real-time. Based on the estimated result, we further develop a dynamic algorithm to judiciously allocate the limited radio resources according to the weights and number between the UAVs and the terrestrial UEs. Moreover, we dynamically adjust the access class barring factors to ensure the access delay requirements of the UEs. Through numerical results, we show how our proposed algorithm can achieve almost the same performance as that of the ideal algorithm assuming perfect information on the number of active UEs is available.

In this paper, to address the “double fading” effect and coverage limitations encountered by conventional passive intelligent reflecting surface (IRS), we propose a novel IRS hardware architecture, termed the dual-sided active (DSA)-IRS, which is capable of simultaneously processing dual-sided incident signals with controllable both amplitude and phase. Furthermore, the DSA-IRS is deployed in a multi-cell multiple-input single-output (MISO) system to alleviate inter-cell interference. Our design objective is to maximize the weighted sum-rate (WSR) among all users by jointly optimizing the beamformers at the base stations (BSs) and the reflection and transmission coefficients for both sides at the DSA-IRS, which is formulated as a non-convex optimization problem. To address the problem, we propose an alternating optimization (AO) scheme to obtain an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IRS outperforms other advanced IRS architectures in enhancing system performance in multi-cell communications due to the additional degrees of freedom for superior resource utilization.

Active reconfigurable intelligent surface (RIS) has attracted a lot of attention due to its ability to drastically change the communication environment by adjusting the phase shift and amplifying the amplitude of signals. In this paper, we consider to apply the active RIS to enhance the performance of the multi-cluster cooperative non-orthogonal multiple access (CNOMA) system. Specifically, in terms of the power consumption, we first formulate a transmit power minimization problem by jointly optimizing the beamforming at the base station, power splitting ratio at cluster heads, power allocation in each cluster, and RIS matrices in direct transmission and cooperative transmission phases. Then, to improve the fair energy efficiency (EE), we solve a minimum EE maximization problem. To tackle the coupling of optimization variables, we propose the block coordinates descent (BCD) based algorithms. By applying the successive convex approximation (SCA), semi-definite relaxation (SDR), arithmetic-geometric mean (AGM) inequality, Schur complement, and convex upper bound substitution methods, the developed approaches are guaranteed to converge to local optimal solutions. Simulations results demonstrate that the proposed algorithms outperform the baseline schemes under passive RIS aided case, non-cooperation case, and no-RIS case in terms of power consumption and energy efficiency. It is also revealed that active RIS is not always superior to passive RIS schemes with a large number of RIS elements in terms of the system power consumption.

Jittering effect is a critical issue in the unmanned aerial vehicle (UAV) millimeter wave (mmWave) communications. In this paper, we identify and characterize the asymmetric impact of jitter on the angular domain information in the UAV mmWave channels, showing how it can lead to significant performance degradation if not properly handled. To address this issue, we propose an adaptive hybrid beamforming for the UAV mmWave communications to maximize the average transmission rate against the asymmetric jitter. The proposed adaptive hybrid beamforming consists of an optimal beam angular range design and an adaptive beamforming vector design. Specifically, we first analytically derive a compact expression of the average transmission rate of our systems. Based on the derived expression, we optimize the beam angular range to maximize the average transmission rate under arbitrary asymmetric jitter. Moreover, we derive the asymptotic expression of the optimal beam angular range in the high signal-to-noise ratio regime. We further develop a simple-yet-efficient algorithm to obtain an adaptive beamforming vector that delivers the optimal beam angular range. Through numerical results, we verify the destructive impacts of the asymmetric jitter, and demonstrate how our proposed scheme can be robust to it, compared to the existing methods without considering the asymmetric jitter.

Ensuring secure access to cellular networks is of paramount importance, in which system information (SI) protection plays a crucial role at the initial access stage. While the 3rd generation partnership project (3GPP) released many standardizations to enhance SI protection for preventing users from false base station (FBS) attacks, most of them are centralized solutions which are vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-enabled SI protection (BeSI), as a compatible and effective secure access scheme, is developed in this work, which aims at guaranteeing the authenticity and reliability of SI by considering the features of blockchain in immutability, traceability, and decentralization. Then, we derive a mathematical framework to justify the superiority of using blockchain in SI protection. Moreover, by resorting to a Poisson point process as the geographical model for both base stations and FBSs, we thus theoretically analyze the security gain of blockchain and understand the impact of network parameters including redundancy rate, number of confirmation blocks, and the density of base stations. Finally, numerical results are demonstrated to validate the effectiveness of BeSI.

Recently, reconfigurable intelligent surface (RIS) has gained growing research interests in sensing fields. While extensive efforts have been devoted to designing RIS space beamforming for improving sensing accuracy, the potential of RIS in improving sensing resolution has so far not been fully dug. In this paper, we investigate the fundamental performance of RIS-enabled multi-target sensing, and exploit the potential of RIS in simultaneously boosting resolution and accuracy. Specifically, based on the identification that sensing is to obtain target state information from the received echo signals, we adopt the sensing mutual information as the performance metric, thus enabling comprehensive evaluation of both sensing resolution and accuracy. Then, we derive the analytical expression of the sensing mutual information in our considered systems, revealing that in addition to providing beamforming gains to enhance sensing accuracy in the space domain, RIS can also flexibly vary its beamforming in the time domain to improve sensing resolution. Based on this result, we propose a novel space-time beamforming, in which space-domain and time-domain beamforming gains are utilized for enhancing sensing accuracy and resolution, respectively. Numerical results demonstrate the significant advantages of the proposed space-time beamforming scheme over the traditional space-only beamforming scheme in terms of sensing resolution.

Reconfigurable intelligent surface (RIS) has shown its great potential in facilitating device-based integrated sensing and communication (ISAC), where sensing and communication tasks are mostly conducted on different time-frequency resources. While the more challenging scenarios of simultaneous sensing and communication (SSC) have so far drawn little attention. In this paper, we propose a novel RIS-aided ISAC framework where the inherent location information in the received communication signals from a blind-zone user equipment is exploited to enable SSC. We first design a two-phase ISAC transmission protocol. In the first phase, communication and coarse-grained location sensing are performed concurrently by exploiting the very limited channel state information, while in the second phase, by using the coarse-grained sensing information obtained from the first phase, simple-yet-efficient sensing-based beamforming designs are proposed to realize both higher-rate communication and fine-grained location sensing. We demonstrate that our proposed framework can achieve almost the same performance as the communication-only frameworks, while providing up to millimeter-level positioning accuracy. In addition, we show how the communication and sensing performance can be simultaneously boosted through our proposed sensing-based beamforming designs. The results presented in this work provide valuable insights into the design and implementation of other ISAC systems considering SSC.

In this paper, we establish an integrated sensing and communication (ISAC) system based on a distributed semi-passive intelligent reflecting surface (IRS), which allows location sensing and data transmission to be conducted on the same time-frequency resources. The detailed working process of the proposed IRS-based ISAC system is designed, including the transmission protocol, location sensing and beamforming optimization. Specifically, each coherence block consists of the channel estimation period, the ISAC period with two time blocks, and the pure communication (PC) period. During the channel estimation period, the low-dimensional effective user-BS channel is estimated. During each time block of the ISAC period, data transmission and user positioning are carried out simultaneously. The estimated user location in the first time block will be used for beamforming design in the second time block. During the PC period, only data transmission is conducted, by invoking the user location estimated in the second time block of the ISAC period for beamforming design. Simulation results show that a millimeter-level positioning accuracy can be achieved by the proposed location sensing scheme. Besides, the proposed two beamforming schemes based on the estimated location achieve similar performance to the benchmark schemes assuming perfect channel state information, verifying the effectiveness of beamforming design using sensed location information.

The performance of unmanned aerial vehicle (UAV)-enabled networks is generally bottlenecked by severe inter-cell interference and limited backhaul connectivity. Against this backdrop, we analyze the performance of the UAV-enabled heterogeneous networks, where antenna downtilt at each UAV-mounted base station (BS) is employed to mitigate inter-cell interference, and the tethered UAV (TUAV)-mounted BS is deployed to provide backhaul connectivity for multiple spatially randomly distributed untethered UAV-mounted BSs, while they are cooperatively serving the terrestrial users. Through leveraging stochastic geometry, the user association probability and conditional distance distributions of serving links are derived. Then, the compact expressions of coverage probability and ergodic rate of the UAV-enabled heterogeneous networks are derived, and the impacts of antenna downtilt and backhaul connectivity are investigated. Numerical results validate our analysis and show the effectiveness of antenna downtilt and the TUAV-enabled backhaul connectivity. Moreover, we find that a larger antenna downtilt angle is required for a higher deployment altitude. We show that the optimal size of the network area that maximizes the average coverage probability reduces, when the backhaul connectivity is considered. We also demonstrate that the average network performance can be significantly improved by judiciously selecting the number of untethered UAVs, which is closely related to the size of the network area. Finally, the advantage of the proposed UAV-enabled heterogeneous network on the average ergodic rate has been validated by comparing it with two benchmarks under the 3GPP channel model.

In this paper, we exploit the potential benefits of intelligent reflecting surface (IRS) in achieving integrated sensing and communication (ISAC) at the same frequency and time resources. To this end, we establish a novel framework, in which a single-antenna user transmits to a multi-antenna base station, with the aid of a distributed semi-passive IRS. In the established framework, the transmission period is divided into two time blocks. At each time block, the distributed semi-passive IRS conducts the location sensing and data transmission simultaneously. Simple-yet-efficient location sensing and beamforming design schemes are respectively proposed. Particularly, the estimated user location in the first time block is used to facilitate the beamforming design of the IRS in the second time block. Through numerical results, we demonstrate that the proposed location sensing scheme can achieve a millimeter-level positioning accuracy, even when the number of semi-passive reflecting elements is small and the allocated sensing time is short. In addition, we show that, utilizing the imperfect location information obtained from our proposed location sensing scheme, the proposed beamforming design scheme can achieve almost the same performance as the optimal beamforming scheme assuming the perfect channel state information, thus verifying the effectiveness of our proposed framework and providing valuable insights into the design of other IRS-enabled ISAC systems.

Unmanned aerial vehicle (UAV) communications have been recognized as an important component of future wireless networks, due to the UAVs’ inherent maneuverability and flexibility. However, ultra-dense located buildings and the jittering of the UAVs may significantly degrade the performance of UAV wireless networks. Against this backdrop, this paper analyzes the coverage and throughput performance of three-dimensional UAV networks, taking the probabilistic line-of-sight (LoS) channel and the outdated channel state information introduced by the obstacles and the UAVs’ jittering, respectively, into account. By leveraging the tools from stochastic geometry, the analytical expressions for the coverage probability and throughput in large-scale UAV networks are derived. Through numerical results, the correctness of our derived expressions is validated. In addition, it is shown that the coverage probability and throughput performance of the considered UAV networks can be significantly improved by judiciously selecting the deployment density of the UAVs, even in the presence of blockage and jittering. The results presented in this work provide valuable insights into the design and deployment of large-scale UAV networks in practical implementations.

Fog radio access networks (F-RANs), in which the fog access points are equipped with communication, caching, and computing functionalities, have been anticipated as a promising architecture for enabling virtual reality (VR) applications in wireless networks. Although extensive research efforts have been devoted to designing efficient resource allocation strategies for realizing successful mobile VR delivery in downlink, the equally important resource allocation problem of mobile VR delivery in uplink has so far drawn little attention. In this work, we investigate a mobile VR F-RAN delivery framework, where both the uplink and downlink transmissions are considered. We first characterize the round-trip latency of the system, which reveals its dependence on the communication, caching, and computation resource allocations. Based on this information, we propose a simple yet efficient algorithm to minimize the round-trip latency, while satisfying the practical constraints on caching, computation capability, and transmission capacity in the uplink and downlink. Numerical results show that our proposed algorithm can effectively reduce the round-trip latency compared with various baselines, and the impacts of communication, caching, and computing resources on latency performance are illustrated.

Fog-enabled vehicular network (FVNET) has been envisioned as a promising solution to provide seamless coverage for the vehicles. However, the mobility of the vehicles and consequently more frequently handoffs make it particularly challenging to characterize the system performance of FVNET. In this paper, we propose an analytical framework to evaluate the coverage performance of FVNET, taking the impact of the vehicles' mobility in two successive time slots into account. Specifically, we first identify all the handoffs cases that can occur due to the vehicles' moblity, namely, no-handoff, horizontal handoff, and vertical handoff. Then, using tools from stochastic geometry, we derive the compact expressions of access probability and joint coverage probability of our system. Numerical results validate our analysis and show that the degrading impacts of the vehicles' mobility on the coverage performance can be well relieved by properly deploying the fog access points.

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables the IoT devices of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the devices. In this article, we apply non-orthogonal multiple access (NOMA) technique to enable massive connectivity and investigate how it can be exploited to achieve energy-efficient MEC in IoT networks. In order to maximize the energy efficiency for offloading, while simultaneously satisfying the maximum tolerable delay constraints of IoT devices, a joint radio and computation resource allocation problem is formulated, which takes both intra- and inter-cell interference into consideration. To tackle this intractable mixed integer non-convex problem, we first decouple it into separated radio and computation resource allocation problems. Then, the radio resource allocation problem is further decomposed into a subchannel allocation problem and a power allocation problem, which can be solved by matching and sequential convex programming algorithms, respectively. Based on the obtained radio resource allocation solution, the computation resource allocation problem can be solved by utilizing the Knapsack method. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in IoT networks compared to the existing baselines.

In this paper, we examine the potential benefits of non-orthogonal multiple access (NOMA) in achieving energy-efficient mobile edge computing (MEC) in wireless networks. To this end, we consider an uplink communication system where the edge users (EUEs) adopt NOMA protocol to offload their own tasks to the edge access points in the presence of cellular users (CUEs) performing regular uplink transmissions. We first characterize the energy consumption of our considered system. Then, taking the delay constraints of the CUEs and EUEs into consideration, we show how the energy consumption of the system can be optimized by judiciously determining the task offloading allocation, the subchannel allocation, as well as the power allocation. In order to solve the non-convex problem, an iterative Stackelberg-game-based scheme is proposed, in which the EUEs perform the task and power allocation as leaders, while the CUEs perform the subchannel allocation as followers. Numerical results show that, compared to exiting solutions, our proposed NOMA-based scheme can significantly reduce the energy consumption of the system, and the performance improvement becomes more profound when the delay constraints of the CUEs and EUEs become stringent.

Mobile edge computing (MEC) enables the users of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the users. In this paper, we investigate how non-orthogonal multiple access (NOMA) techniques can be exploited to achieve energy-efficient MEC in multi-cell networks. To this end, we first characterize the energy efficiency of the considered system, taking into account the impact of both intra- and inter-cell interference in multi-cell networks. We then jointly optimize the subchannel allocation, power allocation, and the computation resource allocation to maximize the energy efficiency of NOMA-enabled MEC, while simultaneously satisfying the maximum tolerable delay constraints of the users. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in multi-cell networks compared to the existing baselines.

In this paper, we propose a novel secure random caching scheme for large-scale multi-antenna heterogeneous wireless networks, where the base stations (BSs) deliver randomly cached confidential contents to the legitimate users in the presence of passive eavesdroppers as well as active jammers. In order to safeguard the content delivery, we consider that the BSs transmits the artificial noise together with the useful signals. By using tools from stochastic geometry, we first analyze the average reliable transmission probability (RTP) and the average confidential transmission probability (CTP), which take both the impact of the eavesdroppers and the impact of the jammers into consideration. We further provide tight upper and lower bounds on the average RTP. These analytical results enable us to obtain rich insights into the behaviors of the average RTP and the average CTP with respect to key system parameters. Moreover, we optimize the caching distribution of the files to maximize the average RTP of the system, while satisfying the constraints on the caching size and the average CTP. Through numerical results, we show that our proposed secure random caching scheme can effectively boost the secrecy performance of the system compared to the existing solutions.

Non-orthogonal multiple access (NOMA) has been considered as a promising communication technology to enhance the spectral efficiency and support massive connections in fog radio access networks (F-RANs). In this paper, with the aim of maximizing the weighted sum rate while taking co-channel interference into consideration, a joint resource block (RB) and power allocation problem is formulated. To solve this problem, we first propose the optimal resource allocation scheme. Specifically, the monotonic optimization is applied and an outer polyblock approximation algorithm is proposed to get the global optimal solution. In order to reduce the computational complexity, we then propose the suboptimal resource allocation scheme. In particular, the original problem is decomposed into separated RB and power allocation problems. The RB allocation problem is modeled as a many-to-one matching game and a modified swap-enabled matching algorithm is proposed. The power allocation problem is converted into a convex form through some approximations and solved by a successive convex approximation algorithm. Simulation results demonstrate that the suboptimal scheme can achieve almost the same performance as the optimal scheme, while requiring much less computational complexity. In addition, the superiority of NOMA-enabled F-RANs over the conventional OMA-enabled F-RANs is verified.

The emerging demands of the immersive virtual reality (VR) experience require current and future wireless networks to provide ultra-low end-to-end latency. Against this backdrop, fog radio access networks (F- RANs), which take full advantages of both fog computing and caching technologies, are anticipated as a promising solution for meeting the stringent latency requirement of mobile VR delivery. In this paper, we propose a mobile VR delivery framework, in which certain VR videos and computing tasks are cached at and offloaded to the edge of F-RANs, respectively. In the considered framework, we jointly optimize the bandwidth, caching, and computing resource allocation in order to minimize the average latency. To this end, we first derive the closed-form expression of the average latency. Based on which, we then analytically examine the optimal resource allocation decision. Moreover, through the numerical results, we reveal the non-trivial trade-offs among communication, caching, and computation, showing how the bandwidth, caching, and computing capabilities can have significant impacts on the average latency.

Wireless jamming is a critical challenge for sprawling and ubiquitous devices, which are connected using radio-frequency (RF) waves. Traditionally direct-sequence spread spectrum (DSSS) and its derivatives have been recognized as an effective jamming resilient technique. However, the effectiveness of DSSS relies on the use of either pre-shared secret code or a large bank of public codes, the generation, distribution, and management of which would be particularly difficult in future large-scale decentralized wireless networks. To tackle this problem, we present a framework which exploits the shared randomness inherent in wireless channels to generate and refresh secret seeds at each communicating node. We highlight why channel randomness cannot be used as it is and develop processing algorithms which ensure that RF based sources are suitable for entropy pooling and random seed generation.

In this paper, we establish a framework for enabling ultra-reliable and low-latency communications in the control and non-payload communications (CNPC) links of the unmanned aerial vehicle (UAV) communication systems. We first derive the available range of the CNPC links between UAVs and a ground control station. The available range is defined as the maximal horizontal communication distance within which the round-trip delay and the overall packet loss probability can be ensured with a required probability. To exploit the macro-diversity gain of the distributed multi-antenna systems (DAS) and the array gain of the centralized multi-antenna systems (CAS), we consider a modified DAS (M-DAS), where the ground control station is equipped with the distributed access points (APs), and each AP can have multiple antennas. We then show that the available range can be maximized by judiciously optimizing the altitude of UAVs, the duration of the uplink and downlink phases, and the antenna configuration. To solve the non-convex problem, we propose an algorithm that can converge to the optimal solution in DAS and CAS, and then extend it into more general M-DAS. The simulation and numerical results validate our analysis and show that the available range of M-DAS can be significantly larger than those of the DAS and CAS.

In this paper, we examine the physical layer security of a cooperative relay network where two source-destination pairs communicate through a decode-and-forward (DF) relay in the presence of multiple eavesdroppers. To safeguard the legitimate communications against eavesdropping, we propose a novel two-stage secure relay selection (TSSRS) with a non-orthogonal multiple access (NOMA) scheme to maximize the capacity of one source-destination pair, while guaranteeing the successful communication of the other source-destination pair. To explicitly reveal the benefits of our proposed scheme, we derive the exact and asymptotic expressions for its secrecy outage probability. As a benchmark, we also analyze the secrecy performance of the TSSRS strategy with an orthogonal multiple access (OMA) scheme. Both theoretical analysis and simulation results demonstrate that our proposed TSSRS-NOMA scheme significantly outperforms the TSSRS-OMA scheme when the transmit power at the source and relay is in the low and medium regimes. In addition, we show that the advantage of the TSSRS-NOMA scheme over the TSSRS-OMA scheme becomes obvious when the two source-destination pairs have profoundly different secrecy requirements.

Unmanned aerial vehicle (UAV) wireless communication has recently been recognized to be inevitable and prevalent in the fifth-generation (5G) wireless networks. In this paper, we propose a secure transmission scheme for a wiretap channel, where a source communicates with a legitimate UAV in the presence of an eavesdropper. We consider the full-duplex active eavesdropper, which performs both eavesdropping and malicious jamming simultaneously. The source transmits artificial noise (AN) signals, in addition to information signals, to confuse this eavesdropper. By considering the ground-to-UAV channel model, we analyze the hybrid outage probability, which takes both the transmission outage probability and the secrecy outage probability into consideration. We further provide the asymptotic hybrid outage probability in a more compact form, where both the transmit power at the source and the jamming power at the eavesdropper become large with a fixed ratio. Through the analysis and the numerical results, we determine the optimal power allocation factor between information signals and AN signals as well as the operating height of UAV that minimize the hybrid outage probability. We also provide the most harmful antenna configuration of the eavesdropper to the UAV communications, and this paper can be a useful framework for the design of confidential UAV communication system.

We propose a secure transmission scheme for a wiretap channel, where a source transmits to a legitimate destination in the presence of a full- duplex eavesdropper. We assume that the full- duplex eavesdropper performs both eavesdropping and malicious jamming simultaneously. In order to combat the full-duplex active eavesdropper, the source transmits artificial noise (AN) signals, apart from information signals, to confuse the eavesdropper. We adopt the hybrid outage probability as the performance metric, which takes both the transmission outage probability and the secrecy outage probability into consideration. We derive a compact expression for the hybrid outage probability. Utilizing the derived expression, we determine the optimal power allocation factor between information signals and AN signals that minimizes the hybrid outage probability. We also examine the asymptotic hybrid outage probability in the high signal-to-noise ratio regime. Furthermore, we compare the secrecy performance achieved by a full-duplex active eavesdropper with that achieved by a passive eavesdropper, showing that active eavesdropping can be more harmful to the legitimate system.

We propose a new location-based beamforming (LBB) scheme for wiretap channels, where a multi-antenna source communicates with a single-antenna legitimate receiver in the presence of a multi-antenna eavesdropper. We assume that all channels are in a Rician fading environment, the channel state information from the legitimate receiver is perfectly known at the source, and that the only information on the eavesdropper available at the source is her location. We first describe how the optimal beamforming vector that minimizes the secrecy outage probability of the system is obtained, illustrating its dependence on the eavesdropper’s location. We then derive an easy-to-compute expression for the secrecy outage probability when our proposed LBB scheme is adopted. We also consider the positive impact a friendly jammer can have on our beamforming solution, showing how the path to optimality remains the same. Finally, we investigate the impact of location uncertainty on the secrecy outage probability, showing how our solution can still allow for secrecy even when the source only has a noisy estimate of the eavesdropper’s location. This paper demonstrates how a multi-antenna array, operating in the most general channel conditions and most likely system setup, can be configured rapidly in the field so as to deliver an optimal physical layer security solution.

We design a new relay-aided secure transmission scheme, in which a source communicates with a destination through a trusted decode-and-forward relay in the presence of spatially random-distributed non-colluding eavesdroppers. We consider a general antenna configuration, in which the source, relay, destination, and eavesdroppers are equipped with multiple antennas. We assume that both the source and the relay transmit artificial noise signals in addition to information signals. We also assume that the source and the relay adopt different codebooks, and that the transmitted signals from the source and relay are not jointly processed at each eavesdropper. We first derive a closed-form expression for the transmission outage probability and a new expression for the secrecy outage probability. Notably, these expressions are valid for an arbitrary number of antennas at the source, relay, and destination. We then derive simple yet valuable expressions for the asymptotic transmission outage probability and the asymptotic secrecy outage probability, which reveal the secrecy performance when the number of antennas at the source grows sufficiently large. Using our expressions, we quantify a practical performance metric, namely, the secrecy throughput, under a secrecy outage probability constraint. We further determine the system and channel parameters that maximize the secrecy throughput, leading to analytical security solutions suitable for real-world deployment.

Location information has been shown to be useful for a wide variety of applications in wireless networks, while its role in physical layer security has so far drawn little attention. In this work, we propose a new location-based secure transmission scheme for wiretap channels, where the accurate locations of the sources, destinations and any other authorized transceivers are known, but only an estimate of the eavesdropper's location is available. We outline how such an estimate of the eavesdropper's location can still allow for quantitative assessment of key security metrics. To provide focus, we describe how optimization of the effective secrecy throughput of a relay wiretap channel is obtained in our scheme, and investigate in detail the impact of the location uncertainty on the system performance. The work reported here provides insights into the design of new location-based physical layer security schemes in which the only information available on an eavesdropper is a noisy estimate of her location.

We propose two beamforming schemes supporting multi-stream transmission in multi-input multi-output multi-antenna eavesdropper wiretap channels with imperfect channel state information of the eavesdropper. We first propose a generalized eigenvalue decomposition (GEVD)-based beamforming scheme by designing the beamforming matrix and determining the power allocation matrix. In particular, we determine a general power allocation matrix for arbitrary signal-to-noise ratio (SNR) and a simplified power allocation matrix for high SNR. We demonstrate that our GEVD-based beamforming scheme delivers a higher achievable secrecy rate than the existing beamforming schemes in the medium and high SNR regime. We also demonstrate that the simplified power allocation matrix delivers the same achievable secrecy rate as the general power allocation matrix at high SNRs. We then propose an easy-to-construct EVD-based beamforming scheme which reduces signal processing cost and eliminates power allocation. We demonstrate that our EVD-based beamforming scheme delivers a higher secrecy rate than the GEVD-based beamforming scheme and the existing beamforming schemes in the low SNR regime.

In this paper, we propose a new beamforming scheme for multi-input multi-output (MIMO) Gaussian wiretap channels where the channel state information (CSI) from the eavesdropper is imperfectly known to the transmitter. A stochastic model is constructed to characterize the imperfect CSI of the eavesdropper, in which a factor 0 ≤ τ ≤ 1 is introduced to describe the degree of the available eavesdropper's channel knowledge at the transmitter. When τ varies from 0 to 1, the eavesdropper's channel knowledge available at the transmitter ranges from statistically known to perfectly known. We design the proposed beamforming scheme by maximizing a lower bound on the achievable secrecy rate. We first demonstrate that our scheme achieves higher secrecy rate than the existing eigenvalue decomposition-based beamforming scheme which is optimal for τ = 0. We then demonstrate that the proposed scheme achieves higher secrecy rate than the existing generalized eigenvalue decomposition-based beamforming scheme which is optimal for τ = 1. Furthermore, we derive tight approximations for the proposed beamforming scheme in the high signal-to-noise ratio (SNR) regime and the low SNR regime. The accuracy of these approximations is validated via numerical results. Finally, we demonstrate that our proposed scheme achieves almost the same secrecy performance as the optimal beamforming solution that is obtained through numerical search.