The surge in task-intensive applications underscores the critical need for mobile edge computing (MEC) to deliver low-latency services that cater to the computational demands of user equipment (UE). Computing density is inherently heterogeneous and dynamic, driven by the varying demands of UEs across space and time. To address the challenges posed by heterogeneous computing density, we propose a novel hybrid offloading framework that enables resource-constrained devices to dynamically choose between direct offloading and intelligent reflecting surface (IRS)-enhanced parallel offloading, thereby minimizing overall latency, improving resource utilization and adapting to varying conditions. Additionally, for prompt computation distribution, we propose an online semi-distributed optimization based on a two-timescale approach. Combining long-term centralized planning with short-term distributed computation assignment, this framework enables low-complexity distributed task allocation while ensuring performance and stability. Furthermore, we develop an equivalence and relaxation approach to derive an upper bound for completion delay of servers, which greatly simplifies the computation complexity of completion delay of tasks. Our proposed online semi-distributed algorithm converges, with reduced delay 24.03% and 32.68% compared to existing IRS-enhanced parallel offloading scheme and direct offloading scheme.

When the phase shift variation of a reconfigurable intelligent surface (RIS) transitions from a quasi-static pattern to a rapidly changing pattern, it engenders a novel capability to support concurrent communications. This paper proposes a novel grouped RIS-enabled multi-user parallel communication framework, where each RIS group is assigned a specified spread spectrum (SS) sequence, e.g., Zadoff-Chu (ZC) code, to tag a distinct user. Although the RIS group establishes a customized virtual channel for its served user through the specified SS sequence, the received signal is still contaminated by other users’ signals. Thus, the quasi-static phase shift of the RIS is optimized to amplify the desired signal while suppressing inter-user interference. However, the transmission delays hinder the perfect detection of the SS sequences. Moreover, the optimization of phase shift necessitates the availability of channel state information (CSI) and exhibits a high sensitivity to the channel phase. Therefore, we propose a pilot-free subspace-based method combined with scalar ambiguity estimation to jointly estimate direct and cascaded channels without phase ambiguity, along with propagation delays. After obtaining the CSI and delays, the phase shift optimization problem could be reformulated as a generalized Rayleigh quotient maximization problem with closed-form solutions. Simulation results validate the accuracy of the blind channel estimation method. Meanwhile, the sum rate of the RIS-enabled parallel communications is ten times higher than that of the conventional RIS-assisted communication, attributed to the SS sequence design of the RIS.

Conventional end-to-end learning-based point cloud compression requires training multiple models to adapt to different target bit rates. Moreover, the rate difference between geometry and attribute components of point clouds is not well-considered. In this paper, we propose an end-to-end Rate-Reconfigurable Deep Point Cloud Compression (RR-DPCC) with on/off-line Perceptual Bit Allocation Optimization (PBAO-ON/OFF), which achieves arbitrary bit rate control with one trained deep model and high efficiency joint geometry and attribute coding. First, we propose the framework of the RR-DPCC using PBAO-ON/OFF, which includes Point Cloud Quality Assessment (PCQA) for perceptual quality measurement, PBAO-ON/OFF modules for bit allocation and RR-DPCC for high efficiency point cloud coding. Second, we propose a one-stream network of the RR-DPCC to encode the attribute and geometry of point clouds jointly. Moreover, in RR-DPCC, a bitrate reconfigurable module is proposed to encode multiple fine-grained bitrate points with one trained model and a rate allocation module is proposed to allocate bits between geometry and attribute. Third, we propose on/off-line PBAO algorithms to maximize the perceptual quality of the reconstructed point cloud, where the bits are properly allocated based on the importance of geometry and attribute. Meanwhile, rate-distortion models (R- $\alpha $ / $\beta $ and D- $\alpha $ / $\beta $ ) are derived for high accuracy rate control and bit allocation. Experimental results show that the proposed RR-DPCC achieves fine-grained bitrate control and allocation through a single trained model. When combined the proposed RR-DPCC with PBAO-ON, it reduces -6.56% and -18.68% bit rate on average as comparing with the state-of-the-art V-PCC and Deep Joint Geometry and Attribute Compression (Deep-JGAC), respectively. When combined with the PBAO-OFF, it achieves -4.90% and -15.34% bit rate reductions on average, and reduces 98.38%/22.05% and 53.75%/10.04% encoding/decoding time on average with respect to V-PCC and Deep-JGAC.

Sporadic ambient radio frequency signals can offer opportunistic spectrum and energy sources for backscatter communications, but they also induce unpredictable transmission interruptions in ambient backscatter wireless networks (AmBWNs). Integrating self-carrier-generative active transmissions with backscatter communications could significantly enhance transmission stability but require frequent mode switching to accommodate the ever-changing ambient radio frequency signals. However, this will result in frequent changes in network topology and link capacity, posing significant challenges in solving the network maximum flow problem in hybrid AmBWNs. To address this problem, we design a link-aware online maximum flow (OnMAXFlow) scheme to tackle agile and adaptive flow scheduling and communication mode selection. Specifically, we first employ an online learning framework to dynamically track changes in ambient signal strength and channel states, enabling real-time evaluation of link capacity. We then model the network maximum flow problem as a stochastic multi-armed bandit (MAB) problem and solve it with a Kullback-Leibler upper confidence bound (KL-UCB) algorithm. Our experimental evaluation results reveal that our OnMAXFlow scheme exhibits rapid convergence and superior adaptability against the varying network states, while maintaining spectrum efficiency and latency performance comparable to the Oracle scheme, which always selects the optimal transmission modes and paths.

WiFi-based human activity recognition (HAR) has demonstrated significant potential in diverse intelligent applications. However, sensitive to environmental factors, cross-domain WiFi channel state information (CSI) poses significant challenges in the generalization of HAR models across different environments. In this paper, the structural causal model (SCM) is introduced to model causal relationships among activities, latent variables, and CSI data, laying a solid foundation toward developing a domain-invariant model for WiFi-based HAR tasks. In this paper, we propose CausalFi, a novel framework that leverages causal inference to mitigate the confounding effects of latent variables, significantly improving generalization performance. Integrating novel feature selection and importance sampling algorithms as the condition and intervention operations, CausalFi can effectively identify the stable action-relevant features from WiFi CSI for activity recognition. Furthermore, a novel counterfactual style augmentation approach is proposed to increase the stylistic diversity of the training data, reducing the risk of biased data distributions even with limited training samples in source domains. We implement a prototype of CausalFi using commercial ASUS RT-AC86U WiFi devices and conduct extensive cross-domain experiments to validate the effectiveness of the proposed approach. With an average recognition accuracy of 92.6%, CausalFi significantly outperforms state-of-the-art baselines in complex cross-domain environments, confirming the practicality of our framework for real-world WiFi-based HAR applications. © 2026 IEEE.

Timely channel conditions are essential for vehicles to determine which base station (BS) to connect to, but acquiring them in mmWave vehicular networks is costly. Without additional channel estimations, the proposed asynchronous contextual kernelized upper confidence bound (ACK-UCB) algorithm estimates the current instantaneous transmission rates based on the historical transmission rates and contexts, such as the vehicle’s historical locations, velocities, and numbers of concurrent transmissions at the BS. ACK-UCB captures the nonlinear relationship between context and transmission rate, mapping the context into a reproducing kernel Hilbert space (RKHS), where a linear relationship becomes observable. To enhance estimation accuracy, a novel kernel function incorporating mmWave signal propagation characteristics is introduced in RKHS, allowing for a more precise evaluation of context similarity in relation to transmission rates. Furthermore, ACK-UCB encourages vehicles to share only reward distribution features after sufficient explorations, accelerating the learning process while keeping communication costs manageable. Numerical results show that ACK-UCB achieves 99.5%–100.5% network throughput and reduces 89%–91% communication cost of a benchmark algorithm that directly shares all local historical contexts and transmission rates, demonstrating the sharing efficiency of the ACK-UCB algorithm.

Vehicles require timely channel conditions to determine the base station (BS) to communicate with, but it is costly to estimate the fast-fading mmWave channels frequently. Without additional channel estimations, the proposed Distributed Kernelized Upper Confidence Bound (DK-UCB) algorithm estimates the current instantaneous transmission rates utilizing past contexts, such as the vehicle's location and velocity, along with past instantaneous transmission rates. To capture the nonlinear mapping from a context to the instantaneous transmission rate, DK-UCB maps a context into the reproducing kernel Hilbert space (RKHS) where a linear mapping becomes observable. To improve estimation accuracy, we propose a novel kernel function in RKHS which incorporates the propagation characteristics of the mmWave signals. Moreover, DK-UCB encourages a vehicle to share necessary information when it has conducted significant explorations, which speeds up the learning process while maintaining affordable communication costs.

The design of the dual-function signal is critical to integrated sensing and communication (ISAC). The communication and sensing signals share the channel estimation techniques since both communication and sensing capabilities rely on channel state information (CSI). A communication system typically estimate channels with specific preambles can be reused for sensing, which facilitates the integration of sensing with communication. With augmented sensing capability within existing communication frameworks, the ISAC systems based on preamble sharing achieves improved spectral efficiency and reduced costs. However, in multi-target scenarios, echoes from different targets cannot be accurately distinguished at the receiver, leading to severe sensing ambiguity. To address this issue, we propose a PArallel Multi-target Sensing and Echo Separation (PAMSES) scheme for ISAC systems operating in multi-user and multi-target environments. The receiver exploits the waveform diversity to discriminate echoes from distinct targets, effectively resolving multi-target sensing ambiguity. Furthermore, since perfect CSI is usually unavailable at the access point, we formulate a robust optimization problem to ensure reliable communication and sensing performance in the worst-case scenario. The proposed problem can be formulated as a semidefinite program (SDP) and solved through semidefinite relaxation (SDR) techniques. Simulation results demonstrate that the proposed method can achieve 0.01 m/s velocity estimation accuracy and 5 bps/Hz spectrum efficiency, while maintaining an energy efficiency of 6.27 bit/J/Hz.

Millimeter wave (mmWave) communication has emerged as a key technology enabling ultra-low latency and high throughput in vehicular communication. Usually, an appropriate decision on user association requires timely channel information between vehicles and base stations (BSs), which is challenging given a fast-fading mmWave vehicular channel. In this paper, we propose a low-complexity semi-distributed contextual correlated upper confidence bound (SD-CC-UCB) algorithm to establish an up-to-date user association between vehicles and BSs without explicit measurement of channel state information (CSI). Under a contextual multi-arm bandits framework, SD-CC-UCB learns and predicts the transmission rate given the location and velocity of the vehicle, which can adequately capture the intricate channel condition for a prompt decision on user association. Further, SD-CC-UCB efficiently identifies the set of candidate BSs which probably support supreme transmission rates by leveraging the correlated distributions of transmission rates on different locations. To further refine the learning transmission rate to candidate BSs, each vehicle deploys the Thompson Sampling algorithm by taking the interference among vehicles and handover into consideration. Numerical results show that our proposed algorithm achieves the network throughput within 100%–103% of a benchmark algorithm which requires perfect instantaneous CSI, demonstrating the effectiveness of SD-CC-UCB in vehicular communications.

Feature Coding for Machines (FCM) aims to compress intermediate features effectively for remote intelligent analytics, which is crucial for future intelligent visual applications. In this article, we propose a Multiscale Feature Importance-based Bit Allocation (MFIBA) for end-to-end FCM. First, we find that the importance of features for machine vision tasks varies with the scales, object size, and image instances. Based on this finding, we propose a Multiscale Feature Importance Prediction (MFIP) module to predict the importance weight for each scale of features. Second, we propose a task loss-rate model to establish the relationship between the task accuracy losses of using compressed features and the bit rate of encoding these features. Finally, we develop an MFIBA for end-to-end FCM, which is able to assign coding bits of multiscale features more reasonably based on their importance. Experimental results demonstrate that when combined with a retained Efficient Learned Image Compression (ELIC), the proposed MFIBA achieves an average of 38.202% bit-rate savings in object detection compared to the anchor ELIC. Moreover, the proposed MFIBA achieves an average of 17.212% and 36.492% feature bit-rate savings for instance segmentation and keypoint detection, respectively. When the proposed MFIBA is applied to the LIC-TCM, it achieves an average of 18.103%, 19.866%, and 19.597% bit-rate savings on three machine vision tasks, respectively, which validates the proposed MFIBA has good generalizability and adaptability to different machine vision tasks and FCM base codecs.

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology for network performance enhancement by intelligently manipulating the propagation environment. In contrast to conventional reflecting-only RIS, the simultaneous transmitting and reflecting RIS (STAR-RIS) extends coverage to 360 degrees through transmission and reflection, offering more flexibility in channel reconfiguration. The application of STAR-RIS in wireless networks holds tremendous potential for significantly improving network connectivity by fostering cascaded paths. However, it remains open on the deployment strategy for STAR-RIS to guarantee network connectivity cost-effectively, especially when considering random blockages. In this paper, we focus on investigating the impact of STAR-RIS on network connectivity in blockage-prone scenarios. Leveraging percolation theory, we derive upper and lower bounds for the critical density of nodes in both no-STAR-RIS and STAR-RIS-aided networks. Our results quantify the impact of STAR-RIS on network connectivity, demonstrating its significant enhancement on network connectivity and providing valuable insights into cost-effective deployment schemes for STAR-RIS.

Dynamic point cloud is a volumetric visual data representing realistic 3D scenes for virtual reality and augmented reality applications. However, its large data volume has been the bottleneck of data processing, transmission, and storage, which requires effective compression. In this paper, we propose a Perceptually Weighted Rate-Distortion Optimization (PWRDO) scheme for Video-based Point Cloud Compression (V-PCC), which aims to minimize the perceptual distortion of reconstructed point cloud at the given bit rate. Firstly, we propose a general framework of perceptually optimized V-PCC to exploit visual redundancies in point clouds. Secondly, a multi-scale Projection based Point Cloud quality Metric (PPCM) is proposed to measure the perceptual quality of 3D point cloud. The PPCM model comprises 3D-to-2D patch projection, multi-scale structural distortion measurement, and fusion model. Approximations and simplifications of the proposed PPCM are also presented for both V-PCC integration and low complexity. Thirdly, based on the simplified PPCM model, we propose a PWRDO scheme with Lagrange multiplier adaptation, which is incorporated into the V-PCC to enhance the coding efficiency. Experimental results show that the proposed PPCM models can be used as standalone quality metrics, and they are able to achieve higher consistency with the human subjective scores than the state-of-the-art objective visual quality metrics. Also, compared with the latest V-PCC reference model, the proposed PWRDO-based V-PCC scheme achieves an average bit rate reduction of 13.52%, 8.16%, 10.56% and 9.54%, respectively, in terms of four objective visual quality metrics for point clouds. It is significantly superior to the state-of-the-art coding algorithms. The computational complexity of the proposed PWRDO increases by 1.71% and 0.05% on average to the V-PCC encoder and decoder, respectively, which is negligible. The source codes of the PPCM and PWRDO schemes are available at https://github.com/VVCodec/PPCM-PWRDO.

Recently, ambient backscatter communication has become a promising technology to support the low-power and low-cost Internet-of-Things (IoT). However, the nondeterministic and sporadic nature of ambient signals makes it a great challenge when designing multiple access in spectrum opportunistic ambient backscatter wireless networks (AmBWNs). Moreover, the stringent energy supply and ultra-low-cost design of the backscatter transmitter make most multiple access schemes no longer suitable for AmBWNs. To effectively share carrier frequency resources for backscattering, we propose a hierarchical multiple access scheme, which allows beamforming based spatial division multiple access among groups, and non-orthogonal multiple access (NOMA) for multiple users access within a group. Consequently, we formulate a multi-objective optimization problem to balance the sum rate and the fairness by exploiting grouping, beamforming, and reflection coefficients. To solve this problem, we employ the matching theory to tackle the grouping problem and achieve the corresponding beamforming. We then reformulate the non-convex reflection coefficient optimization and solve it with successive convex approximation and geometric programming. Our extensive evaluation results demonstrate that the spectrum and energy efficiency, latency, and fairness can be significantly improved with minimal overhead at the transmitter.

With the merit of self-sustainability, ambient backscatter aided wireless networks (AmBWNs) have attracted considerable attention for the potential application in Internet of Everything. The ambient backscatter transmission significantly reduces the power consumption by reflecting or absorbing ambient RF signals to transmit at the cost of fragile performance guarantee. The dual-mode node which can transmit in active mode (ATM) or backscatter mode (BTM) has been proposed to improve both energy efficiency and performance reliability. In the AmBWN composed of dual-mode nodes, information flows and energy flows coexist and are transferable. Existing maximum flow algorithms designed for static networks absence of frequent node and link state transitions are not suitable for AmBWNs. Therefore, we investigate the statistical guarantee of the maximum flow in evolving AmBWNs with energy constraint, flow conservation and Markov inequality constraint. The simulation result shows that the proposed statistical maximum flow algorithm can improve the energy efficiency by 2.5 times under poor channel condition.

Mobile edge computing offers a new paradigm to provide more convenient computing services for mobile devices. However, the mobility of devices and the limited coverage of edge servers bring considerable challenges to efficient computation offloading. Moreover, tasks with temporal dependency further complicate the offloading problem in the mobile edge network. In this paper, we take into account the mobility of devices and the fine-grained tasks generated by the mobile device to make full use of the computing resources of devices and edge servers. Considering the temporal dependency among tasks, the offloading problem is formulated as a mixed integer programming which achieves the tradeoff between time latency and energy consumption. Simulation results demonstrate that our proposed algorithm can achieve a significant improvement in terms of energy efficiency and latency compared with other bench mark algorithms.

Recently, energy-efficient ambient backscatter communication has emerged as a promising technology to build up self-sustainable wireless networks. However, the limited transmission range and rate restrict the communication capability of a single backscatter node. To ensure network coverage and connectivity, the nodes in ambient backscatter-aided wireless networks (AmBWNs) have to be densely deployed. Unfortunately, the fickle and sporadic nature of the energy source and carrier in AmBWNs would lead to unreliable and unstable transmissions. We argue that the network throughput can be significantly improved if we can take full advantages of both stable active transmission and energy-saving backscattering. However, it is challenging to jointly determine nodes' transmission mode to enhance the throughput and energy efficiency. Moreover, the fairness in terms of traffic load at each node of AmBWNs is not thoroughly considered in the existing research. The improper load allocation would hinder the network throughput and even cause network partition if some bottleneck nodes deplete the energy. In this article, a novel metric is defined to measure the aggregate energy efficiency of neighboring nodes, capturing the beneficial interaction of active links and backscatter links. Subsequently, we address the aggregate energy efficiency maximization problem constrained by Gini threshold. The Gini coefficient is used to adjust the load in proportion to the delivery capacity of each node, achieving proportional fairness in the dense AmBWN. The simulation result shows that the well-designed metric can improve the energy efficiency by 11 times and the throughput by 86%.

To support the demand of multi-Gbps sensory data exchanges for enhancing (semi)-autonomous driving, millimeter-wave bands (mmWave) vehicular-to- infrastructure (V2I) communications have attracted intensive attention. Unfortunately, the vulnerability to blockages over mmWave bands poses significant design challenges, which can be hardly addressed by manipulating end transceivers, such as beamforming techniques. In this paper, we propose to enhance mmWave V2I communications by augmenting the transmission environments through reflection, where highly-reflective cheap metallic plates are deployed as tunable reflectors without damaging the aesthetic nature of the environments. In this way, alternative indirect line-of-sight (LOS) links are established by adjusting the angle of reflectors. Our fundamental challenge is to adapt the time-consuming reflector angle tuning to the highly dynamic vehicular environment. By using deep reinforcement learning, we propose the learning-based Fast Reflection (LFR) algorithm, which autonomously learns from the observable traffic pattern to select desirable reflector angles in advance for probably blocked vehicles in near future. Simulation results demonstrate our proposal could effectively augment mmWave V2I transmission environments with significant performance gain.

Ambient backscatter communication (AmBC) enables information delivery over an ambient RF signal without carrier generation and has emerged as a promising technology to build up the self- sustainable Internet-of-Things (IoT). However, when a strong ambient signal appears, multiple backscatter nodes may initiate data transmission simultaneously, causing severe contention and wasting the precious transmission opportunity. The nondeterministic and sporadic nature of ambient signals makes it a great challenge for efficient multiple access design in ambient backscatter aided wireless network (AmBWN). Moreover, the stringent energy supply and ultra-low-cost design of the backscatter transmitter makes most multiple access schemes no longer suitable for AmBWN. To fully share carrier resources for backscattering, we resort to the non-orthogonal multiple access (NOMA) to allow multiple devices in the same regime to transmit over an ambient signal with low latency. Moreover, we propose a hierarchical multiple access scheme, which allows beamforming based spatial division multiple access among groups, and NOMA for multiple users access within a group. The evaluation result shows latency and SINR can be significantly improved with minimal overhead at the transmitter.

In recent years, generative adversarial network (GAN) has attracted great attention due to its impressive performance and potential numerous applications, such as data augmentation, real-like image synthesis, image compression improvement, etc. The generator in GAN learns the density of the distribution from real data in order to generate high fidelity fake samples from latent space and deceive the discriminator. Despite its advantages, GAN can easily memorize training samples because of the high model complexity of deep neural networks. Thus, training a GAN with sensitive or private data samples may compromise the privacy of training data. To address this privacy issue, we propose a novel \textit{Privacy Preserving Generative Adversarial Network} (PPGAN) that perturbs the objective function of discriminator by injecting Laplace noises based on functional mechanism to guarantee the differential privacy of training data. Since generator training is considered as a post-processing step while guaranteeing differential privacy of discriminator, the trained generator should be differentially private to effectively protect data samples. Through detailed privacy analysis, we theoretically prove that PPGAN can provide such strict differential privacy guarantee. With extensive simulation study on the benchmark dataset MNIST, we show the efficacy of the proposed PPGAN under practical privacy budgets.

The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. The theoretical and simulation results are provided to validate the feasibility of the proposed schemes.

The integration of wireless power transfer (WPT) with the backscatter communications provides a promising way to sustain batteryless wireless networks. In this paper, we consider a backscatter communication network, in which the passive radio uses the harvested energy from a power beacon station (PBS) to supply its data transmissions, while some other radios can help as the wireless relays. To improve the throughput performance of a distant transceiver pair, we propose a two-hop backscatter relay model and formulate a throughput maximization problem to jointly optimize WPT and the relay strategies. Noting that the proposed problem is non-convex, an iterative algorithm with reduced complexity is proposed to decompose the original problem into a power allocation subproblem in the outer loop and an optimization of the relay strategy in the inner loop. Numerical results reveal that the power allocation converges to the optimum and the relay strategy significantly improves the throughput when the radios' power demand is low.

The integration of wireless power transfer (WPT) with the low-power backscatter communications provides a promising way to sustain battery-less wireless networks. In this paper, we consider a backscatter communication network wirelessly powered by a power beacon station (PBS). Each backscatter radio uses the harvested energy to power its data transmissions, in which some other radios can help as the wireless relays with an aim to improve throughput performance by cooperative transmission. Under this setting, we formulate a throughput maximization problem to jointly optimize WPT and the relay strategy of the backscatter radios. An iterative algorithm with reduced complexity and communication overhead is proposed to decompose the original problem into two sub-problems distributed at the PBS and the backscatter receiver. Moreover, we take uncertain channel information into consideration and formulate robust counter-parts of the throughput maximization problem when either the backscatter or relay channel is subject to estimation errors. The difficulty of the robust counter-part lies in the coupling of the PBS' power allocation and relay strategy in matrix inequalities, which is addressed by alternating optimization with guaranteed convergence. Numerical results reveal that the cooperative relay strategy of the backscatter radios significantly improves the throughput performance.

Device-to-device (D2D) communications can potentially alleviate cellular network congestion by utilizing local available links, and have attracted intensive attention recently. Cognitive radio (CR) allows users to opportunistically access unused licensed spectrums. It thus serves as a great candidate technology for D2D communications, but has not been widely employed in cellular networks due to hardware development limitations. In this paper, we propose a new architecture, called cognitive mesh assisted cellular network (CMCN), in which several secondary service providers (SSPs) deploy CR routers to facilitate D2D communications among wireless users. To address the competition among the SSPs, we further construct a secondary spectrum auction market. Although a few works have studied spectrum auctions, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Uncertain spectrum availability is not considered in previous schemes either. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can address these problems. Extensive simulations are conducted to validate the efficiency of the CMCN architecture and that of the TOA scheme.

Due to the extremely low power consumption, ambient backscatter communications has attracted great interest from both industry and research communities. However, the short communication range and unpleasant reliability are the two major challenges which prevent ambient backscatter from wide deployment in WSNs. In this work, we propose the design of a robust cooperative routing protocol (RCRP) for ambient backscatter based wireless sensor network (AmB-WSN), to account for the volatility of ambient RF environment. In RCRP, the backscatter sensor nodes (BSNs) work cooperatively to reduce the probability of routing path failure. To ensure robustness, we propose novel routing metrics to construct a robust counter-part for each nominal routing path, which are related to the strength of ambient RF signals and the BSNs' residual energy. Extensive simulations reveal that RCRP can achieve enhanced routing stability, improved throughput performance, and reduced end-to-end delay, which make it preferable and scalable for multi-hop AmB-WSN.

Backscatter communications allows the wireless radio to work in passive mode that transmits information by reflecting incident radio frequency signals. It consumes significantly less power compared to the conventional active radio that modulates information on self-generated carrier signals. However, the active radio is deemed more reliable as it can adapt to the varying channel conditions via transmit power control. In this paper, we aim to maximize the throughput of a multi-user network wirelessly powered by a power beacon station (PBS), assuming that each transceiver can switch between the passive and active radio modes. The joint optimization of the radios' mode selection, the PBS' energy beamforming and time allocation is formulated into a mixed integer nonlinear program (MINLP). Relying on an approximate upper bound of the MINLP, we employ a heuristic mode selection algorithm to determine each user's radio mode under uncertain channel state information. Simulation reveals that passive mode is preferred by the radios with better channel conditions and the active mode will be preferred if we ensure higher system reliability when the channels are subject to uncertainties.

This paper investigates the resource allocation problem in orthogonal frequency division multiple access multi-user networks, where subcarriers are grouped into chunks due to simplicity of implementation. The aim is to achieve max-min fairness among users by adjusting the transmission power allocation and chunk allocation while taking into account several important constrains. The problem is formulated as a mixed integer nonlinear programming problem, whose optimal solution is extremely hard to find. Then a low complexity suboptimal algorithm is proposed, which solves the chunk allocation and power allocation in two steps separately. A fast optimal power allocation algorithm is designed by exploiting the special structure of the problem. Simulations verify the performance of the proposed algorithm in terms of the users' minimal transmission rate and running time comparing with benchmark algorithms.

In this paper, we consider a device-to-device (D2D) network underlying a cellular system wherein the densely deployed D2D user devices can act as wireless relays for a distant transceiver pair. We aim to devise a beamforming strategy for the relays that maximizes the data rate of the distant transceiver while satisfying interference constraints at the cellular receivers. Towards that end, we first formulate a beamforming problem whose solution is robust against the channel uncertainties in the relay-destination hop. Motivated by practical observations, we assume that the random channels in this hop follow unimodal distributions and propose a novel unimodal distributionally robust model to capture the channel uncertainties. Then, we extend the formulation so that it can also guard against the channel uncertainty in the source-relay hop under the worst case robust model. The resulting robust beamforming problem is generally non-convex and intractable. Therefore, we design an iterative algorithm, which is based on solving semidefinite programs, to find an approximate solution to it. Simulation results show that under mild conditions, our robust model significantly improves the throughput of D2D relay transmissions when compared with the conventional robust models that merely rely on the channels' moment information. It also outperforms the Bernstein-type inequality-based convex approximation, which assumes that the channel follows a Gaussian distribution.

Motivated by the observation that energy harvesting (EH) from radio-frequency (RF) signal is subject to fluctuations, multiple EH-enabled relays are employed to collaboratively enhance data communications in a device-to-device (D2D) network underlying a cellular system. Each relay is equipped with a single antenna and unable to harvest energy and transmit data simultaneously. Thus, the D2D user equipment (DUE) needs to optimally schedule the channel time for the relays' EH and data transmissions, which depends on their EH capabilities and channel conditions. Considering that the relays' channel estimations are usually unreliable, we formulate a robust throughput maximization problem to optimize the relays' EH time and transmit power, subject to a probabilistic interference constraint at the cellular user equipment (CUE). We show that the proposed problem, though non-convex, can be tackled by exploiting its monotonicity structure. Specifically, we design a successive approximation algorithm that involves solving a sequence of semi-definite programs (SDPs) and show numerically that it always achieves the global optimum. This validates our analysis and demonstrates the efficacy of the proposed algorithm.

We consider a wireless network with densely deployed user devices (e.g., a device-to-device or wireless sensor network) underlaying a cellular system, in which some user devices act as relays to facilitate data transmissions between a distant transceiver pair under imperfect channel information. Motivated by the observation that most of the channel distributions are unimodal, we formulate a novel distributionally robust beamforming problem, in which the random channel coefficient follows a class of unimodal distribution with known first- and second-order moments. Our design objective is to maximize the worst-case signal-to-noise ratio (SNR) at the dedicated user device while satisfying a probabilistic interference constraint at the cellular user equipment (CUE). Though such a unimodal distributionally robust (UDR) beamforming problem is non-convex, we show that an approximate solution can be computed efficiently using semidefinite programming. Our simulation results show that under mild conditions, the UDR model yields significant beamforming performance improvement over conventional robust models that merely rely on first- and second-order moments of the channel distribution.

In this paper, we investigate the resource allocation for orthogonal frequency division multiple access relay networks, where the relay does not have embedded energy supply and needs to first harvest energy from the received signals from the source before forwarding transmission. The relay uses time switching scheme for wireless information and power transfer. We aim to maximize the weighted sum rate under several constraints by varying the source transmission power, the relay transmission power, and the time switching ratio. We formulate the joint resource allocation problem as an optimization problem, which is non-convex. Although it is difficult to solve the non-convex problem, we derive its closed-form solution by exploiting its special structure. We also prove that the closed- form solution is a partial optimum. Finally, simulations verify the proposed closed-form solution is superior to the equal power solution.

Many truthful spectrum auction schemes have been recently proposed to to ensure that the dominant strategy for bidders is to bid truthfully and thus protect the auctioneer's benefits. However, most of them assume the auctioneer is trustful and do not protect bidders' interests. An auctioneer can manipulate the winner's charging price if it knows bidders' bids. Thus, it is critical to protect bids from the auctioneer. Towards this end, we develop a Privacy-Preserving Economic-Robust spectrum auction scheme, namely PPER. Not only does it well protect users' bid privacy, but also guarantees economic-robustness which is another important auction property. Besides, only transmitters but not receivers are considered in most previous spectrum auctions, resulting in many unexpected collisions during transmissions. In this work, we consider interference constraints from transmissions instead of transmitters in spectrum allocation. Extensive privacy analysis and simulation results show the effectiveness and efficiency of our scheme.

This paper proposes a deMOtion-based, fLash-awARe hybrid storage cache model, named MOLAR, to effectively integrate Flash-based Solid-State Disks (SSDs) into traditional dynamic random access memory (DRAM)-based memory storage systems where SSDs serve as the Tier-2 cache, while DRAM is considered as the Tier-1 cache. We found that conventional cache algorithms designed for DRAM perform poorly in SSDs due to the limited write cycles and asymmetric read/write performance of Flash memory. In MOLAR, a Flash-aware I/O path structure is designed to adapt the asymmetric read and write performance of SSDs and, moreover, to reduce useless write operations. A new control metric, demotion count, is validated to wisely select the evicted blocks from DRAM to reside in the SSD. Besides, for SSD can improve internal data placement from data access hints, the Logical Block Addresses in the SSD are grouped into the long-lived region and the short-lived region self-adaptively via a heuristic control algorithm based on the change of the block demotion count. Through trace-driven simulations, the overall hit ratio in MOLAR outperforms two traditional policies by 1.44–5.34%. The average access latency in SSDs is reduced by 3.5× to 4.5×. Moreover, write amplification is effectively reduced by ∼36% in two typical Flash address-mapping policies.

Cellular networks are faced with serious congestions nowadays due to the recent booming growth and popularity of wireless devices and applications. Opportunistically accessing the unused licensed spectrum, cognitive radio can potentially harvest more spectrum resources and enhance the capacity of cellular networks. In this paper, we propose a new multihop cognitive cellular network (MC2N) architecture to facilitate the ever exploding data transmissions in cellular networks. Under the proposed architecture, we then investigate the minimum energy consumption problem by exploring joint frequency allocation, link scheduling, routing, and transmission power control. Specifically, we first formulate a maximum independent set (MIS) based energy consumption optimization problem, which is a non-linear programming problem. Different from most previous work assuming all the MISs are known, finding which is in fact NP-complete, we employ a column generation based approach to circumvent this problem. We develop an ϵ-bounded algorithm, which can obtain a feasible solution that are less than (1 + ϵ) and larger than (1 - ϵ) of the optimal result of MP, and analyzed its computational complexity. We also revisit the minimum energy consumption problem by taking uncertain channel bandwidth into consideration. Simulation results show that we can efficiently find ϵ-bounded approximate results and the optimal result as well.

Due to the emerging various data services, current cellular networks have been experiencing a surge of data traffic and are already overloaded; thus, they are not able to meet the ever exploding traffic demand. In this study, we first introduce a multi-radio multi-channel multi-hop cognitive cellular network (M$^3$C$^2$N) architecture to enhance network throughput. Under the proposed architecture, we then investigate the minimum length scheduling problem by exploring joint frequency allocation, link scheduling, and routing. In particular, we first formulate a maximal independent set based joint scheduling and routing optimization problem called original optimization problem (OOP). It is a mixed integer non-linear programming (MINLP) and generally NP-hard problem. Then, employing a column generation based approach, we develop an$\epsilon$-bounded approximation algorithm which can obtain an$\epsilon$-bounded approximate result of OOP. Noticeably, in fact we do not need to find the maximal independent sets in the proposed algorithm, which are usually assumed to be given in previous works although finding all of them is NP-complete. We also revisit the minimum length scheduling problem by considering uncertain channel availability. Simulation results show that we can efficiently find the$\epsilon$-bounded approximate results and the optimal result as well, i.e., when$\epsilon =0\%$in the algorithm.

In ad hoc networks, selfish nodes deviating from the standard MAC (Medium Access Control) protocol can significantly degrade normal nodes' performance and are usually difficult to detect. In this paper, we propose detection and defense schemes to identify and defend against MAC-layer selfish misbehavior, respectively, in IEEE 802.11 multi-hop ad hoc networks. Specifically, the non-deterministic nature of the IEEE 802.11 MAC protocol imposes great challenges to distinguishing selfish nodes from well-behaved nodes. Most traditional selfish misbehavior detection approaches are for wireless local area networks (WLANs) only. They either rely on a large amount of historical data to perform statistical detection, or employ throughput or delay models that are only valid in WLANs for detection. In contrast, we propose a realtime selfish misbehavior detection scheme for multi-hop ad hoc networks. It requires only several samples, and hence is more efficient and can adapt to channel dynamics more quickly. Then, based on the proposed detection scheme, we design three selfish misbehavior defense schemes against three typical kinds of smart selfish nodes. We find that the smart selfish nodes cannot degrade normal nodes' performance much without getting detected. Extensive simulation results are finally presented to validate the proposed detection and defense schemes.

Secure neighbor discovery is fundamental to mobile ad hoc networks (MANETs) deployed in hostile environments and refers to the process in which two neighboring nodes exchange messages to discover and authenticate each other. It is vulnerable to the jamming attack in which the adversary intentionally transmits radio signals to prevent neighboring nodes from exchanging messages. Anti-jamming communications often rely on spread-spectrum techniques, which depend on a spreading code common to the communicating parties but unknown to the jammer. The spread code, however, is impossible to establish before the communicating parties successfully discover each other. While several elegant approaches have been recently proposed to break this circular dependence, the unique features of neighbor discovery in MANETs make them not directly applicable. In this paper, we propose JR-SND, a jamming-resilient secure neighbor discovery scheme for MANETs based on direct-sequence spread spectrum and random spread-code predistribution. JR-SND enables neighboring nodes to securely discover each other with overwhelming probability despite the presence of omnipresent jammers. Detailed theoretical and simulation results confirm the efficacy and efficiency of JR-SND.

ABSTRACT Cognitive radio (CR) is a promising technique for future wireless networks, which significantly improves spectrum utilization. In CR networks, when the primary users (PUs) appear, the secondary users (SUs) have to switch to other available channels to avoid the interference to PUs. However, in the multi‐SU scenario, it is still a challenging problem to make an optimal decision on spectrum handover because of the the accumulated interference constraint of PUs and SUs. In this paper, we propose an interference‐aware spectrum handover scheme that aims to maximize the CR network capacity and minimize the spectrum handover overhead by coordinating SUs’ handover decision optimally in the PU–SU coexisted CR networks. On the basis of the interference temperature model, the spectrum handover problem is formulated as a constrained optimization problem, which is in general a non‐deterministic polynomial‐time hard problem. To address the problem in a feasible way, we design a heuristic algorithm by using the technique of Branch and Bound. Finally, we combine our spectrum handover scheme with power control and give a convenient solution in a single‐SU scenario. Experimental results show that our algorithm can improve the network performance efficiently.Copyright © 2012 John Wiley & Sons, Ltd.

This paper proposes a deMOtion-based, fLAsh-awaRe hybrid storage cache model, named MOLAR, to effectively integrate Flash-based Solid State Disks (SSDs) into traditional DRAM-based memory storage systems. In MOLAR, a flash-aware I/O path structure is designed to adapt the asymmetric read and write performance of SSD and moreover to reduce useless write operations. A new control metric, demotion count, is proposed to wisely select the evicted data blocks from DRAM to reside in SSD. Besides, for SSD can improve internal data placement from data access hints, the Logical Block Addresses (LBAs) in SSD are grouped into the long-lived region and the short-lived region self-adaptively via a heuristic control algorithm based on the change of data block demotion count. Through trace-driven simulations, the overall hit ratio in MOLAR outperforms two traditional policies from 1.44% to 5.34%. The average write latency in SSD is reduced by 3.5 X. Moreover, write amplification is effectively reduced by about 36% in two typical flash address mapping policies.

The device-free (DF) object tracking in wireless networks requires a tracked object neither be equipped with a transceiver nor actively participate in the tracking process. One of methods to implement a DF tracking system is to leverage the variation of radio signals introduced by object intrusion. However, many factors may result in the variation of radio signals and it is hard to distinguish which variation is caused by object intrusion. In this paper, we investigate how some factors impact signal variations, and in further impact the effectiveness of DF tracking. Based on analysis, we conduct experiments with TelosB motes, which are carried out in three representative environments with different system parameters. Different wireless channel quality indicators are employed to monitor signal variations. The experiment results provide a significant guidance on how to choose environments, system parameters, channel quality indicators, and metrics to implement an effective DF tracking system.

Connectivity of large-scale wireless networks has received considerable attention in the past several years. Different from traditional wireless networks, in Cognitive Radio Ad-hoc Networks (CRAHNs), primary users have spectrum access priority of the licensed bands over secondary users. Therefore, the connectivity of the secondary network is affected by not only the density and transmission power of secondary users, but also the activities of primary users. In addition, the number of licensed bands also has impact on the connectivity of CRAHNs. To capture the dynamic characteristics of opportunistic spectrum access, we introduce the Cognitive Radio Graph Model (CRGM) which takes into account the impact of the number of channels and the activities of primary users. Furthermore, we combine the CRGM with continuum percolation model to study the connectivity in the secondary network. We prove that secondary users can form the percolated network when the density of primary users is below the critical density. Then, the upper bound of the critical density of the primary users in the percolated CRAHNs is derived. Simulation results show that both the number of channels and the activities of primary users greatly impact the connectivity of CRAHNs.

Throughput capacity in mobile ad hoc networks has been studied extensively under many different mobility models. However, most previous research assumes global mobility, and the results show that a constant per-node throughput can be achieved at the cost of very high delay. Thus, we are having a very big gap here, i.e., either low throughput and low delay in static networks or high throughput and high delay in mobile networks. In this paper, employing a practical restricted random mobility model, we try to fill this gap. Specifically, we assume that a network of unit area with n nodes is evenly divided into cells with an area of n^{-2\alpha }, each of which is further evenly divided into squares with an area of n^{-2\beta} (0 \le \alpha \le \beta \le {1\over 2} ). All nodes can only move inside the cell which they are initially distributed in, and at the beginning of each time slot, every node moves from its current square to a uniformly chosen point in a uniformly chosen adjacent square. By proposing a new multihop relay scheme, we present smooth trade-offs between throughput and delay by controlling nodes' mobility. We also consider a network of area n^\gamma (0\le \gamma \le 1) and find that network size does not affect the results obtained before.

Cognitive Radio (CR) emerges as a promising solution to current unbalanced spectrum utilization. The cognitive ad hoc network can take advantage of dynamic spectrum access and spectrum diversity over wide spectrum. It could achieve higher network capacity compared to traditional ad hoc networks, thus supporting bandwidth-demanding applications. A cognitive radio operates over wide spectrum with unpredictable channel availability. Moreover, the transmission opportunity of a cognitive node is not guaranteed due to the presence of primary users (PUs). These two unique features define new routing problems in cognitive ad hoc networks. To better characterize the unique features of cognitive radio networks, we propose new routing metrics, including accumulated spectrum temperature, highest spectrum temperature, and mixed spectrum temperature to account for the time-varying spectrum availability. The proposed metrics favor the "coolest'' path, or the path with the most balanced and/or the lowest spectrum utilization by the primary users. We also study the computational complexity of the routing algorithm in cognitive ad hoc networks. Experiment results on our USRP-2 testbed show that the proposed metrics are capable of capturing the fluctuation of spectrum availability and suitable for cognitive ad hoc networks.

Secure neighbor discovery is fundamental to mobile ad hoc networks (MANETs) deployed in hostile environments and refers to the process in which two neighboring nodes exchange messages to discover and authenticate each other. It is vulnerable to the jamming attack in which the adversary intentionally sends radio signals to prevent neighboring nodes from exchanging messages. Anti-jamming communications often rely on spread-spectrum techniques which depend on a spreading code common to the communicating parties but unknown to the jammer. The spread code is, however, impossible to establish before the communicating parties successfully discover each other. While several elegant approaches have been recently proposed to break this circular dependency, the unique features of neighbor discovery in MANETs make them not directly applicable. In this paper, we propose JR-SND, a jamming-resilient secure neighbor discovery scheme for MANETs based on Direct Sequence Spread Spectrum and random spread-code pre-distribution. JR-SND enables neighboring nodes to securely discover each other with overwhelming probability despite the presence of omnipresent jammers. Detailed theoretical and simulation results confirm the efficacy and efficiency of JR-SND.

Wireless cellular networks are large-scale networks in which asymptotic capacity investigation is no longer a cliché. A substantial body of work has been carried out to improve the capacity of cellular networks by introducing ad hoc communications, resulting in the so-called multihop cellular networks. Most of the previous research allows ad hoc transmissions between certain source and destination pairs to alleviate base stations' relay burden. However, since reports show that Internet data traffic is becoming more and more dominant in cellular networks, we explore in this paper the capacity of multihop cellular networks with all traffic going through base stations and ad hoc transmissions only acting as relay. We first investigate the capacity of regular multihop cellular networks where both nodes and base stations are regularly placed. By fully exploiting the link rate variability, we find that multihop cellular networks can have higher per-node throughput than traditional cellular networks by a scaling factor of log2n. Then, for the first time we extend our study to the capacity of heterogeneous multihop cellular networks where nodes are distributed according to a general Inhomogeneous Poisson Process and base stations are randomly placed. We show that under certain conditions multihop cellular networks can also outperform traditional cellular networks by a scaling factor of log2n. Moreover, both throughput-fairness and bandwidth-fairness are considered as fairness constraints for both kinds of networks.

In Cognitive Radio Networks (CRNs), when the Primary Users (PUs) appear, the SUs have to evacuate the licensed spectrum in use or reduce the transmit power so that no harmful interference is introduced to the PUs. In this paper, we explore the multiple Secondary Users (SUs) coexistence system in CRNs based on power control mechanism and interference temperature model. We propose an optimal solution that can maximize the channel capacity and minimize the spectrum handover overhead, constrained by the accumulated interference of both the SUs-to-PU and SUs-to-SUs. We formulate this problem as a non-linear optimization problem and propose a heuristic algorithm to solve it efficiently. Experimental results show that compared with two alternative approaches, our algorithm can improve the usage of the spectrum by up to 51% (with a random approach) and up to 278% (with a conservative approach).

This paper focuses on spectrum handover in cognitive radio networks where secondary users (SUs) opportunistically use licensed channels as long as the aggregate interference at the primary users (PUs) does not exceed a certain threshold. We incorporate power control into the proposed spectrum handover scheme to reduce the number of spectrum handovers and enhance the spectral efficiency. In our work, when a PU arrives, an SU first calculates the maximum transmission power that the SU does not interfere with the PU. If the SU can still reach its receiver, it lowers its power and continues to transmit; otherwise it switches to an idle band. Analysis results show that our proposed scheme can substantially reduce the spectrum handover ratio and improve the effective data rate by up to 30%.

In wireless sensor networks, path breakage occurs frequently due to node mobility, node failure, and channel impairments. It is challenging to combat path breakage with minimal control overhead, while adapting to rapid topological changes. Due to the Wireless Broadcast Advantage (WBA), all nodes inside the transmission range of a single transmitting node may receive the packet, hence naturally they can serve as cooperative caching and backup nodes if the intended receiver fails to receive the packet. In this paper, we present a distributed robust routing protocol in which nodes work cooperatively to enhance the robustness of routing against path breakage. We compare the energy efficiency of cooperative routing with noncooperative routing and show that our robust routing protocol can significantly improve robustness while achieving considerable energy efficiency.

Due to the inexpensive cost and small size of the sensor node, sensor networks are densely deployed for most applications. In the application oriented wireless sensor networks, traffic is usually mixed with time-sensitive packets and reliability-demanding packets. Hence, routing regardless of the packet characteristics is not efficient. Our goal is to provide soft-QoS to different types of packets since accurate path information can be hardly obtained in wireless networks. In this paper, we utilize the multiple paths between the source and sink pairs for QoS provisioning. Unlike E2E QoS schemes, soft-QoS mapped into links on a path is determined based on local link state information. Through the estimation and approximation of path quality, traditional NP-complete QoS problem is split into many small problems. The idea is to formulate the problem as a probabilistic programming, then based on some approximation technique, we convert it into an integer programming, which is much easier to solve. The resulting solution is also one to the original probabilistic programming. Simulation results demonstrate the effectiveness of our approach.