Orthogonal Time-Frequency Space (OTFS) modulation is an innovative modulation technique that operates in the two-dimensional delay-Doppler (DD) domain. It is specifically designed for high Doppler scenarios, where the channel can be transformed into an almost non-fading channel for DD domain symbol transmission. In multiple-input multiple-output (MIMO) OTFS systems, the DD domain input-output relation has considerable complexity, especially under fractional delays and Dopplers. The condition number is a key indicator of channel matrix quality as it reflects the sensitivity to noise and disturbance. In this paper, a novel Zak-OTFS modulation in the DD domain has been proposed as a competitive alternative to the conventional multi-carrier (MC) OTFS scheme, where MC-OTFS is implemented in two steps in the time-frequency (TF) domain. We focus on both MIMO-Zak-OTFS and MIMO-MC-OTFS schemes and study the condition numbers for the residual error covariance matrix and the channel matrix. We investigate the condition number under various system parameters, and the results indicate that OTFS outperforms MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) in terms of condition number distributions. Meanwhile, the condition numbers of MIMO-Zak-OTFS matrices also outperform those of MIMO-MC-OTFS, while MIMO-Zak-OTFS shows a better BER performance compared to MIMO-MC-OTFS and MIMO-OFDM using both LMMSE and sphere decoders in the experimental results.

Low-earth orbit (LEO) satellite communication (SatCom) has emerged as a promising technology to improve wireless connectivity in global areas. Cell-free massive multiple-input multiple-output (CF-mMIMO), an architecture proposed for next-generation networks, has yet to be fully explored for LEO satellites. In this paper, we investigate the downlink performance of a CF-mMIMO LEO SatCom network, where multiple satellite access points (SAPs) simultaneously serve the corresponding ground user terminals (UTs). Using tools from stochastic geometry, we model the locations of SAPs and UTs on surfaces of concentric spheres using Poisson point processes (PPPs) and present expressions on transmit and received signals, signal-to-interference-plus-noise ratio (SINR). Then, we derive the coverage probabilities in fading scenarios, considering significant system parameters such as the Nakagami fading parameter, the number of UTs, the number of SAPs, the orbital altitude, and the service range affected by the dome angle. Finally, the analytical model is verified by extensive Monte Carlo simulations. Simulation results indicate that stronger line-of-sight (LoS) effects and a more comprehensive service range of the UT result in a higher coverage probability, despite the presence of multi-user interference (MUI). Moreover, we found that there exist optimal numbers of UTs that maximize system capacity for different orbital altitudes and dome angles, providing valuable insights for system design.

Satellite edge computing (SEC) ignites ubiquitous computation offloading across the Earth’s surface. However, non-uniformly distributed users generate uneven traffic demands, resulting in excessive load on particular satellites and insufficient use of satellite computing resources. In this paper, we explore the possibility of cooperative SEC using inter-satellite links, where we design a two-stage cooperative SEC algorithm in satellite-terrestrial networks. Specifically, in stage one, users can offload their partial computational tasks to their directly connected satellites and locally execute the rest of the tasks. In stage two, the connected satellites further offload partial computational tasks to the terrestrial station and available satellites in a cooperative manner, especially for high-traffic satellites. We aim to minimize the system weighted-sum energy consumption during SEC by jointly optimizing task allocation, power control, bandwidth allocation, task partition, and computing resource allocation under the constraints of maximum tolerated latency, computation capacity at each satellite, total bandwidth, and maximum allowable transmission power. Furthermore, we introduce an iterative algorithm by decomposing the original non-convex problem into several sub-problems and solve each sub-problem with attempts to derive theoretical analysis. Simulation results demonstrate that our introduced cooperative SEC algorithm can efficiently use satellite computing resources and significantly reduce system weighted-sum energy consumption compared to other algorithms.

This paper tackles the challenges of high energy consumption, limited computational resources, and communication delays in Federated Learning (FL) for multi-UAV communication systems. We propose an innovative FL-based object detection training framework designed for UAV applications. The framework first introduces a lightweight modification to the YOLOv12 model, significantly reducing its parameter count and computational complexity, which enables efficient model training without compromising detection performance. Furthermore, the framework employs a joint optimization strategy for local computation and communication, thereby effectively reducing energy consumption and overall training time. Experimental results on the VisDrone2021 dataset demonstrate that, while maintaining a detection accuracy of 76.7% mAP, the model reduces its parameter count by 78.9% compared to the original YOLOv12 and lowers the global training cost by 19.58%, achieving an optimal balance between accuracy, latency, and energy efficiency.

Federated learning (FL) is a pivotal paradigm for decentralized model training while preserving data privacy. However, data heterogeneity among clients significantly degrades model performance and convergence efficiency. In response, we introduce a federated knowledge distillation mechanism, FedEXD, that addresses robustness and convergence in diverse client environments through a self-propelled learning architecture. FedEXD employs a novel density ratio-based data extraction algorithm, leveraging KLIEP to select representative data, enhancing global knowledge synthesis and local model adaptability while preserving privacy. Extensive evaluations on benchmark datasets demonstrate FedEXD's substantial improvements in efficiency and accuracy, demonstrating a substantial 1.51% accuracy improvement over state-of-the-art methods under firm heterogeneity while reducing communication rounds by over 46.3%. These findings underscore FedEXD's potential to advance FL systems' generalizability across complex, non-IID data distributions, offering a scalable solution for privacy-conscious, high-performance distributed learning.

The imperative of facilitating information transmission to maritime entities situated at substantial distances from terrestrial coastlines presents a critical challenge. Low Earth Orbit (LEO) satellite deployment represents a viable and efficient communication strategy with these geographically remote maritime users. In this paper, we investigate the coverage of satellite-maritime networks in the context of downlink and uplink modalities, where LEO satellites and maritime users are conceptualized as two Poisson point processes at disparate altitudes. According to the directional antenna pattern, the satellite’s serving area is segmented into main and side lobe serving areas. This delineation facilitates a comprehensive analysis of serving distance distributions, probabilities of different interference cases, and maritime user’s connectivity probability. With the evaporation ducting phenomena in satellite-maritime communications, we consider Rician distributed small-scale fading in downlink and line-of-sight (LoS) path in uplink. Furthermore, we derive the distributions of aggregated interference with uplink power control, culminating in distinct coverage probabilities (CPs) for downlink and uplink communications. Moreover, we introduce effective coverage probability (ECP) obtained by multiplying user’s connectivity probability and conditional coverage probability. Extensive simulations verify the theoretical results. Our results elucidate the existence of an optimal satellite altitude and serving angle to maximize ECP in downlink and uplink satellite-maritime communications, respectively.

Internet of Vehicles (IoV) supported by terrestrial networks can satisfy the necessities of multiple computation-intensive applications. However, current terrestrial networks and resource management mechanisms may only partially guarantee vehicle and in-vehicle user equipment (VUE)’s quality of service due to the limited coverage of roadside units (RSU), especially in remote areas. This paper investigates vehicular edge computing (VEC) in satellite-terrestrial integrated networks with multiple low-earth orbit (LEO) satellites, ground RSUs, and VUEs. In remote areas without RSU coverage, VUEs can offload their partial tasks to satellites to save energy and guarantee latency. We aim to minimize VUEs’ weighted sum energy consumption by jointly optimizing VUEs’ association, data partition, computing resource allocation, power control, and bandwidth assignment under the constraints of maximum tolerant latency, maximum number of outage time slots, computation capacity at each satellite and each RSU, and maximum allowable transmission power at VUEs. Furthermore, we introduce an iterative algorithm by decomposing the original non-convex problem into several sub-problems. We efficiently solve each sub-problem by utilizing variable substitutions, the difference of convex functions algorithms, the Lagrangian dual method, and the Karush-Kuhn-Tucke conditions. Simulation results show that the introduced satellite-terrestrial integrated networks-enabled VEC scheme significantly reduces VUEs’ energy consumption compared to other schemes.

Due to the challenges of traditional machine learning in terms of data privacy and transmission efficiency, an efficient and private distributed training framework, namely federated learning (FL), is emerged. In the FL training process, users only need to upload to the server, thus preserving user privacy data and improving transmission efficiency. The computing network can provide sufficient computing power support for federated learning training. However, FL still faces many difficulties, such as dynamic wireless channels, limited local computing resources, data heterogeneity, and malicious data attacks. To tackle these challenges, it is crucial to select reasonable clients to participate in training. In this paper, we propose a client selection strategy that considers data quality, computing capacity, and radio resources. We first define a data quality metric by measuring the heterogeneity and reliability of the local dataset. Based on this, we formulate a joint optimization problem of client selection and resource allocation to minimize the average time delay and power consumption while maximizing data quality. Considering the dynamic of wireless channels and computing frequency, an online learning algorithm based on multi-armed bandit (MAB) is developed to obtain the client selection. Finally, a large number of simulations are carried out to verify the effectiveness of the proposed algorithm. The evaluation in different scenarios shows that the DQ-UCB algorithm can discard the attacked clients and the clients with poor computing power or channel quality to achieve better performance.

The security of low-earth orbit (LEO) satellite communication systems faces challenges due to the high-speed movement characteristic. In this paper, we study the secure transmission for an ISAC-enabled LEO satellite system by considering the sensing function to enhance security. With the assistance of the sensing capability, more precise angle information of potential targets/eavesdroppers (Eve) can be estimated, thereby strengthening the system's performance. However, it is impossible to avoid the errors. To tackle this problem, we consider the channel uncertainty model and maximize the sum secrecy rate by jointly designing the secure transmit beamforming and radar receive filters. Furthermore, the non-convex problem is transformed into a series of convex optimization sub-problems, and the required optimization parameters are obtained through iterative calculation. Simulation results verify the advantages of the proposed scheme in achieving secure transmission of the LEO satellite system.

Edge computing is an efficient way to offload computational tasks for user equipment (UE) which has computation-intensive and latency-sensitive tasks in certain applications. However, UEs can not offload to ground edge servers when they are in remote areas. Mounting edge servers on low earth orbit (LEO) satellites can provide remote UEs with task offloading when the ground infrastructure is not available. In this paper, we introduce a multi-satellite-enabled edge computing system for offloading UEs’ computational tasks with the aim of minimizing system energy consumption by optimizing users’ association, power control, task scheduling, and computing resource allocation. Specifically, a UE’s partial task is executed locally and the rest of its task is offloaded to a satellite for processing. Such energy minimization problem is formulated as a mixed-integer nonlinear programming (MINLP) optimization problem. By decomposing the original problem into four sub-problems, we solve each sub-problem with convex optimization methods. In addition, an iterative algorithm is proposed to jointly optimize the task offloading and resource allocation strategy, which achieves a near optimal solution through several iterations. Finally, the complexity and convergence of the algorithm are verified. In our simulation results, the proposed algorithm is compared with different task offloading and resource allocation schemes in terms of system energy consumption, where 43% energy is saved.

This paper introduces the deployment of unmanned aerial vehicles (UAVs) as lightweight wireless access points that leverage the fixed infrastructure in the context of the emerging open radio access network (O-RAN). More precisely, we introduce the aerial radio unit (A-RU) that dynamically serves an underserved area and connects to the distributed unit (ODU) via a wireless fronthaul between the UAV and the closest fixed network infrastructure tower. In this paper we employ artificial intelligence (AI) for determining the UAV trajectory for serving User Equipment (UEs) while maintaining the fronthaul connectivity to the O-DU at the same time in a multiple-input multiple-output (MIMO) fading channel. We first formulate the trajectory time and throughput rate; however, owing to the nonconvexity of the problem of maximizing the network throughput based on UAV location, we put our effort to achieve these goals by RL approach. Three different approaches have been presented. We first divide the area into a grid and let the UAV explore the environment by flying from point A to point B using both the offline Q-learning and the online SARSA algorithm and the pathloss as the reward. With the intention of maximizing the average payoff, the trajectory in the first scenario is described as a Markov decision process (MDP). According to simulations, MDP produces better results in a smaller area and in less time. In contrast, SARSA performs better in larger environments at the expense of a longer flight duration.

Reconfigurable intelligent surface (RIS) offers tremendous spectrum-and-energy efficiency in wireless networks. With the agility and mobility of an unmanned aerial vehicle (UAV), RIS can be mounted on a UAV to enable three-dimensional (3D) signal reflections, reliable air-ground connections, and higher configuration flexibility. However, the scalability of the aperture gain and the spatial multiplexing could not be guaranteed in a single UAV-enabled aerial RIS due to UAV’s limited payload and line-of-sight-dominated air-ground connection. In this paper, we study a UAV swarm-enabled aerial RIS (SARIS)-assisted downlink communication system. The objective of the considered SARIS system is to maximize the weighted sum-rate of ground users by designing the transmit beamforming at the base station (BS), the phase shifts of SARIS reflecting elements, and SARIS 3D placement. For joint BS and SARIS beamforming design, we introduce two beamforming schemes with low computational complexity. For SARIS placement design, the optimal SARIS 3D position is obtained by leveraging the tools from stochastic geometry and considering the distributions of ground users. Simulation results confirm the validity of the analytical derivations. In particular, the SARIS placement plays a vital role in the system performance when the distances between users and the BS increase.

Scalable video caching is a promising technique to alleviate backbone traffic in sixth generation (6G) networks, and to serve users with video quality that adapts to varying channel conditions. In this paper, we develop a layer-based scalable video caching technique with non-orthogonal transmission by taking advantage of the layer feature in the scalable video. In addition, the impact of different serving base station selection algorithms is investigated. Our results indicate that both the caching placement design and transmission scheme design dominate the caching performance. To evaluate the interplay of these two policies, a tractable metric of Caching Aided Data Rate (CADR) is characterized and maximized by jointly optimizing the aforementioned two policies. Together with extensive Monte Carlo simulations, numerical results are also evaluated in this paper, demonstrating that the proposed Layer-based video Caching scheme with Non-Orthogonal Transmission (LCNOT) can achieve higher CADR performance than other baseline schemes.

In Industrial Internet of Things (IIoT), reliability and latency are two important performance indicators. However, these two performance indicators are contradictory with each other, which are difficult to be enhanced simultaneously. In reality, many IIoT applications are in video or image format with critical requirements of both reliability and latency. In this article, we propose a scalable task offloading scheme for IIoT visual applications considering the unique scalable feature compared with general content. The proposed scheme demonstrates that partial content can be adaptively offloaded to a specific computing node to meet the reliability and latency requirements, meanwhile obtaining an excellent tradeoff between them. For optimization, a utility function is defined to characterize the tradeoff between reliability and latency. Then, a greedy algorithm is introduced to solve the problem of maximizing the utility function, where a near-optimal scheduling policy is adopted to achieve offloading association and properly offload data volume. Simulation results reveal that the proposed scalable task offloading scheme performs better than other benchmark schemes in balancing reliability and latency in IIoT visual applications, especially when the network traffic is moderate and channel conditions are undesirable.

Existing routing protocols may not be applicable in UAV networks because of their dynamic network topology and lack of accurate position information. In this paper, an enhanced flooding-based routing protocol is designed based on random network coding (RNC) and clustering for swarm UAV networks, enabling the efficient routing process without any routing path discovery or network topology information. RNC can naturally accelerate the routing process, with which in some hops fewer generations need to be transmitted. To address the issue of numerous hops and further expedite routing process, a clustering method is leveraged, where UAV networks are partitioned into multiple clusters and generations are only flooded from representatives of each cluster rather than flooded from each UAV. By this way, the amount of hops can be significantly reduced. The technical details of the introduced routing protocol are designed. Moreover, to capture the dynamic network topology, the Poisson cluster process is employed to model UAV networks. Afterwards, stochastic geometry tools are utilized to derive the distance distribution between two random selected UAVs and analytically evaluate performance. Extensive simulation studies are conducted to prove the validation of performance analysis, demonstrate the effectiveness of our designed routing protocol, and reveal its design insight.

Mobile-edge computing (MEC) plays a significant role in enabling diverse service applications by implementing efficient data sharing. However, the unique characteristics of MEC also bring data privacy and security problem, which impedes the development of MEC. Blockchain is viewed as a promising technology to guarantee the security and traceability of data sharing. Nonetheless, how to integrate blockchain into MEC system is quite challenging because of dynamic characteristics of channel conditions and network loads. To this end, we propose a secure data sharing scheme in the blockchain-enabled MEC system using an asynchronous learning approach in this article. First, a blockchain-enabled secure data sharing framework in the MEC system is presented. Then, we present an adaptive privacy-preserving mechanism according to available system resources and privacy demands of users. Next, an optimization problem of secure data sharing is formulated in the blockchain-enabled MEC system with the aim to maximize the system performance with respect to the decreased energy consumption of MEC system and the increased throughput of blockchain system. Especially, an asynchronous learning approach is employed to solve the formulated problem. The numerical results demonstrate the superiority of our proposed secure data sharing scheme when compared with some popular benchmark algorithms in terms of average throughput, average energy consumption, and reward.

In Internet-of-Thing (IoT) networks, enormous low-power IoT devices execute latency-sensitive yet computation intensive machine learning tasks. However, the energy is usually scarce for IoT devices, especially for some without battery and relying on solar power or other renewables forms. In this paper, we introduce a cross-layer optimization framework for distributed computing among low-power IoT devices. Specifically, a programming layer design for distributed IoT networks is presented by addressing the problems of application partition, task scheduling, and communication overhead mitigation. Furthermore, the associated federated learning and local differential privacy schemes are developed in the communication layer to enable distributed machine learning with privacy preservation. In addition, we illustrate a three-dimensional network architecture with various network components to facilitate efficient and reliable information exchange among IoT devices. Moreover, a model quantization design for IoT devices is illustrated to reduce the cost of information exchange. Finally, a parallel and scalable neuromorphic computing system for IoT devices is established to achieve energy-efficient distributed computing platforms in the hardware layer. Based on the introduced cross-layer optimization framework, IoT devices can execute their machine learning tasks in an energy-efficient way while guaranteeing data privacy and reducing communication costs.

Unmanned aerial systems/vehicles (UAS/UAVs) are emerging in commercial spaces and will support many applications and services, such as smart agriculture, dynamic network deployment, and network coverage extension, surveillance and security. Emerging 5G terrestrial cellular communications networks will support UAS communications. This paper describes the communications security implications of integrating UAVs into cellular networks. We consider two roles for UAVs in a terrestrial cellular system—guardians and attackers—and analyze solutions against eavesdropping. Our approach leverages the mobility of UAV guardians that act as relays or jammers. The numerical analysis using common air-to-ground and air-to-air channel models demonstrates how the use of ground and aerial relay nodes can improve the secrecy rate in light of ground and UAV-based attacks. Specifically, the dependency on height and elevation angle between the ground and aerial communicating nodes is analyzed. The results show that the strategic use of single and multi-hop aerial relays can significantly increase the secrecy rate of ground cellular network users.

Unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future wireless networks to provide extensive coverage and additional computational capabilities for user equipments (UEs). In this article, we study mobile-edge computing (MEC) in air-ground integrated wireless networks, including ground computational access points (GCAPs), UAVs, and UEs, where UAVs and GCAPs cooperatively provide computing resources for UEs. Our goal is to minimize the total energy consumption of UEs by jointly optimizing users' association, uplink power control, channel allocation, computation capacity allocation, and UAV 3-D placement, subject to the constraints on deterministic binary offloading, UEs' latency requirements, computation capacity, UAV power consumption, and available bandwidth. Due to the nonconvexity of the primary problem and the coupling of variables, we introduce a coordinate descent algorithm that decomposes the UEs' energy consumption minimization problem into several subproblems which can be efficiently solved. The simulation results demonstrate the advantages of the proposed algorithm in terms of the reduced total energy consumption of UEs.

In this paper, we study a three-dimensional (3D) spectrum sharing between device-to-device (D2D) and unmanned aerial vehicles (UAVs) communications. We consider that UAVs perform spatial spectrum sensing to opportunistically access the licensed channels that are occupied by the D2D communications of ground users. The objective of the considered 3D spectrum sharing networks is to maximize the area spectral efficiency (ASE) of UAV networks while guaranteeing the required minimum ASE of D2D networks. Using the tools from machine learning, we obtain the probability of spatial false alarm and the probability of spatial missed detection at the UAV, which helps us to characterize the density of active UAVs. Then, based on the Neyman-Pearson criterion, we further derive the coverage probability of D2D and UAV communications by leveraging the tools from stochastic geometry. In addition, the ASE of the D2D and UAV networks are also obtained. Simulation results show that a decrease in the spatial spectrum sensing radius of UAVs reduces the coverage probability of UAV communications but improves the ASE of UAV networks. Furthermore, the proposed tools allow obtaining the optimal spatial spectrum sensing radius of UAVs given certain network parameters.

Scalable video coding (SVC) and video caching are two promising techniques in the 5th generation networks to improve the users' quality of experience (QoE) in terms of video retrieval. In this paper, we study the video content retrieval in cache-aided device-to-device (D2D) networks, where each video content is coded into multiple layers via SVC. In video caching placement phase, the probabilistic caching placement policy is applied, while in content retrieval phase, the non-orthogonal transmission scheme is utilized. Besides, different D2D transmitter selection algorithms are considered and cache-aided data rate (CADR) is formulated as a metric in this paper, and it is maximized by jointly optimizing the probability caching policy and the power allocation policy in two phases. Analytical results show that probabilistic caching policy incorporated with power domain non-orthogonal transmission scheme can achieve significant benefits compared with other benchmark schemes.

Spatial spectrum sensing (SSS) enables mobile devices to sense the spatial spectrum holes and reuse the scarce spectrum opportunistically. In this paper, we model and analyze the SSS in uplink two-tier user-centric deployed heterogeneous networks (HetNets) where secondary users (SUs) sense the spectrum holes of cellular users. In the two-tier user-centric deployed HetNets, small cell base stations (SBSs) are deployed in hotspots with high user density, and macro base stations (MBSs) are deployed uniformly. Based on the semi-static power control mechanism, the average transmit power of cellular users associated with MBS and SBS are derived, respectively. Furthermore, the spatial false alarm probability and the spatial miss detection probability of a typical SU are obtained, respectively. Moreover, we characterize the coverage probability and the area spectral efficiency (ASE) of SU and cellular networks. The SUs' optimal SSS radius is obtained to maximize the ASE of the entire network while guaranteeing the ASE of cellular networks above a certain threshold. Simulation results show that when the density of SUs is small, a decrease in SUs' SSS radius reduces the coverage probability of SUs. However, it improves the ASE of SUs networks, although the inter-SU interference increases.

This paper develops a novel framework for the modeling and analysis of spatial spectrum sensing (SSS) for device-to-device (D2D) communications in uplink two- tier user-centric deployed heterogeneous networks (HetNets), where small cell base stations (SBSs) are deployed in the places with high user density termed hotspots introduced by 3GPP. We study the average transmit power of uplink users, the probability of spatial false alarm and the probability of spatial miss detection of a typical D2D transmitter (D2D-Tx) during SSS. Based on the results, we further characterize the coverage probability of a typical D2D user and the area spectral efficiency (ASE) of D2D networks. Simulation results verify our analysis and demonstrate the advantages of SSS-based D2D communications in future wireless networks.

Caching is a promising technique for 5G networks to reduce the backhual traffic and increase the overall network efficiency. In this paper, we study the caching placement policy with consideration of cooperative transmission for a two-hop relay-enabled device-to-device (D2D) network. In the caching placement phase, the probabilistic caching placement policy is considered, and in the content transmission phase, the hybrid automatic repeat request (HARQ) scheme with soft information combining [i.e., energy accumulation (EA) and mutual-information accumulation (MIA)] is utilized to improve the content retrieval experience. Cache-aided successful transmission probability (CSTP) is adopted as the main performance metric in this paper. By using tools from stochastic geometry, analytical expressions for the CSTP under different transmission schemes are derived. In moderate or high SIR regime, the optimal caching placement policy is identified based on the analytical expression and the CSTP performance is maximized accordingly. Evaluation results suggest that the MIA-based caching strategy performs better as opposed to existing strategies in most cases, but this outperformance vanishes when the transmission environment becomes severe or when users’ requests become concentrated.

Traditional pricing of a cellular network operator considers the bandwidth utilization for users and operational cost. In this paper, we re-examine this critical subject through a holistic engineering view taken energy efficiency into account. We treat this issue as a two-stage Stackelberg game between the operator and mobile users, where the operator determines the price of unit bandwidth, and accordingly the users choose the amount of requested bandwidth to maximize their payoff. To reach a general result, randomly deployed base stations in large scale have been taken into consideration. By stochastic geometry, we derive the operator's profit and the energy efficiency per unit area, while considering the actual bandwidth demand of users based on their acceptable price. Simulation results show that the operator's profit and the energy efficiency can be achieved their maximum at nearly the same price (i.e., optimal price), and the optimal pricing strategy varies with the deployment density of base stations.

Wireless-powered cellular networks (WPCNs) emerge as a promising technology to satisfy the sufficiency of available energy at mobile devices. In WPCNs, a mobile device is charged from energy stations called power beacons (PBs) by microwave radiation, and it can harvest energy from ambient radio frequency (RF) of base stations etc., which suggests an energy efficient way for communications. In this paper, we study an analytical model of the WPCNs with randomly deployed power beacons (PBs). The impacts of users' data rate requirements on both uplink and downlink transmissions are characterized, where uplink users capture energy from both PBs and BSs to maintain their transmit power. Considering the maximum allowable transmit power (MATP) of mobile device, we derive the successful transmission probability of an uplink cellular user who intends to harvest enough energy to transmit data meeting its quality of service (QoS) requirement. In addition, different modes of PBs are investigated in terms of the distributions of harvested energy, where a PB can either radiate energy isotropically or directionally towards users resorting to beamforming, called isotropic mode or directed mode, respectively. Numerical results validate our theoretical analysis and provide design insights to the WPCNs.

Traffic offloading via device-to-device (D2D) communications has been proposed to alleviate the traffic burden on base stations and to improve the spectral and energy efficiency of cellular networks. The success of D2D communications relies on the willingness of users to share contents. In this paper, we study an economic aspect of traffic offloading via content sharing among multiple devices and propose an incentive framework for D2D assisted offloading. In the proposed incentive framework, the operator improves its overall profit, defined as the network economic efficiency (ECE), by encouraging users to act as D2D transmitters (D2D-Txs) which broadcast their popular contents to nearby users. We analytically characterize D2D-assisted offloading in cellular networks for two operating modes: 1) underlay mode and 2) overlay mode. We model the optimization of network ECE as a two-stage Stackelberg game, considering the densities of cellular users and D2D-Txs, the operator's incentives, and the popularity of contents. The closed-form expressions of network ECE for both underlay and overlay modes of D2D communications are obtained. Numerical results show that the achievable network ECE of the proposed incentive D2D-assisted offloading network can be significantly improved with respect to the conventional cellular networks, where the D2D communications are disabled.

Traffic offloading via device-to-device (D2D) communications has been proposed to improve the network capacity and alleviate the increasing traffic burden on cellular base stations (BSs). However, the success of D2D communications largely relies on the D2D transmitters' (D2D-Txs) willingness of sharing contents (due to the energy consumption for transmission). In this paper, we model and analyze wireless powered D2D-assisted offloading (WPDO) in underlying cellular networks, where the D2D-Tx is allowed to receive power from the nearest BS as well as other interfering BSs, and then D2D-Tx broadcasts the popular contents to nearby users. The average received power at D2D-Tx and the success probability of D2D-Tx transmission are derived. Furthermore, based on the proposed model, we maximize the network energy efficiency while guaranteeing users' required data rates. Our results confirm that the maximum energy efficiency of the WPDO network can be achieved by jointly optimizing the fraction of time for wireless power transfer and the offloading range of D2D-Tx.

LTE-Unlicensed (LTE-U) is considered as a groundbreaking technology to address the increasing scarcity of available spectrum by extending cellular communications to unlicensed band. In this paper, we investigate the performance of D2D communications in conjunction with LTE-U, which can alleviate traffic load of cellular networks. However, in the same unlicensed band, the coexistence of D2D and WiFi technologies should be carefully designed to satisfy user's quality of service (QoS) and to avoid severe interferences and contentions among devices using unlicensed spectrum. We model the transmissions in unlicensed band as hard core point processes (HCPPs) and thus the transmission probabilities of D2D and WiFi access points (APs) are obtained via the clear channel assessment (CCA) mechanism. Furthermore, by characterizing the intra-tier and inter-tier interferences in such complex communication networks, the average transmit power for the D2D link is investigated given that the user's QoS can be guaranteed. Moreover, the throughput of a typical WiFi AP in the large scale networks is theoretically analyzed, and the outage probability of a D2D link is characterized which results from insufficient transmit power under a pre-determined QoS requirement. Simulations justify successful D2D communications in the LTE-U operation and validate the accuracy of this analytical approach.

Device-to-Device (D2D) communication is expected to satisfy the rapidly increasing capacity, and it can also alleviate the burden of base stations (BSs) by offloading onto direct links in a 5G mobile system, which can support high-speed data rate for local users and provide power-saving services, while enhancing the energy efficiency (EE) of the global network. Therefore, we model the EE of global network underlaid or overlaid D2D direct communications, where the explicit relationships between EE and the offloading strategy radius are signified by quantifying various network parameters (i.e., density of BSs and users, data-rate and system bandwidth, etc). More importantly, we analytically comprehend the EE and user's average transmission power in both D2D modes, that is, underlay and overlay. Furthermore, offloading probability of cellular users and active probability of D2D transmitters are analytically obtained. Simulations are carried out and show that global network EE can be significantly improved by using D2D communication. Moreover, in overlay mode, when the D2D bandwidth is same with underlay mode, users consume less power for transmission, because the inter-tier interference is eliminated at the price of saving gain on the energy and spectrum as the total bandwidth becomes larger.