This work is concerned with the coordination gain in integrated sensing and communication (ISAC) systems under a compress-and-estimate (CE) framework, wherein inference performance is leveraged as the key metric. To enable tractable transceiver design and resource optimization, we characterize inference performance via an error probability bound as a monotonic function of the discriminant gain (DG). This raises the natural question of whether maximizing DG, rather than minimizing mean squared error (MSE), can yield better inference performance. Closed-form solutions for DG-optimal and MSE-optimal transceiver designs are derived, revealing water-filling-type structures and explicit sensing and communication (S\&C) tradeoff. Numerical experiments confirm that DG-optimal design achieves more power-efficient transmission, especially in the low signal-to-noise ratio (SNR) regime, by selectively allocating power to informative features and thus saving transmit power for sensing.

Artificial Intelligence Generated Content (AIGC) powered by Generative Diffusion Models (GDMs) has emerged as a transformative paradigm for automated content creation. To satisfy the stringent latency requirements of AIGC services in many edge intelligence scenarios (e.g., smart cities), Mobile Edge Computing (MEC) provides critical computational support by deploying GDMs at edge servers (ES) close to end users. This paper investigates an MEC-enabled AIGC network comprising multiple ES, wireless access points (APs), and mobile users (UEs) with heterogeneous latency and accuracy demands. We formulate aJoint Communication Association and Computation Offloading (JCACO)game, where each UE strategically selects its serving AP, ES, and inference steps to minimize the overall service completion time while meeting accuracy constraints. The problem is challenging due to the network dynamics and the incomplete information. We prove that the JCACO game is apotential gameunder both complete and stochastic information settings, ensuring the existence of Nash Equilibrium (NE) in both cases. To derive the NE efficiently, we develop a distributedMulti-Agent Stochastic Learning(MASL) algorithm that provably converges to the NE with strict performance guarantees. Unlike conventional best-response schemes, MASL requires neither the knowledge of other players’ strategies nor global network information, making it fully distributed and adaptive to dynamic environments. We further provide a strict theoretical convergence analysis for MASL by usingOrdinary Differential Equations(ODEs). Simulation results demonstrate that MASL significantly reduces service completion time compared with benchmark methods while satisfying accuracy constraints, confirming its effectiveness and practicality for real-world MEC-enabled AIGC networks.

Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, by bringing computation capability closer to end-users at the network edge. In this work, we investigate the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under the stochastic information scenario. Traditional approaches often treat these decisions as equal, disregarding the differences in information realization. However, in practice, the ES deployment decision must be made in advance and remain unchanged, prior to the complete realization of information, whereas the decisions regarding service placement and computation task offloading can be made and adjusted in real-time after information is fully realized. To address such temporal coupling between decisions and information realization, we introduce the stochastic programming (SP) framework, which involves a strategic-layer for deciding ES deployment based on (incomplete) stochastic information and a tactical-layer for deciding service placement and task offloading based on complete information realization. The problem is challenging due to the different timescales of two layers' decisions. To overcome this challenge, we propose a multi-timescale SP framework, which includes a large timescale (called period) for strategic-layer decision-making and a small timescale (called slot) for tactical-layer decision making. Moreover, we design a Lyapunov-based algorithm to solve the tactical layer problem at each time slot, and a Markov approximation algorithm to solve the strategic-layer problem in every time period. Simulation results demonstrate that our proposed solution significantly outperforms existing benchmarks that overlook the coupling between decisions and information realization, achieving up to 56% reduction in total system cost.

This work is concerned with robust distributed multi-view image transmission over a severe fading channel with imperfect channel state information (CSI), wherein the sources are slightly correlated. In contrast to point-to-point deep joint source-channel coding (DJSCC), the distributed setting introduces the key challenge of exploiting inter-source correlations without direct communication, especially under imperfect CSI. To tackle this problem, we leverage the complementarity and consistency characteristics among the distributed, yet correlated sources, and propose an robust distributed DJSCC, namely RDJSCC. In RDJSCC, we design a novel cross-view information extraction (CVIE) mechanism to capture more nuanced cross-view patterns and dependencies. In addition, a complementarity-consistency fusion (CCF) mechanism is utilized to fuse the complementarity and consistency from multi-view information in a symmetric and compact manner. Theoretical analysis and simulation results show that our proposed RDJSCC can effectively leverage the advantages of correlated sources even under severe fading conditions, leading to an improved reconstruction performance.

This paper investigates a bi-static integrated sensing and communication (ISAC) system for multi-target scenarios using impulse radio ultra-wideband (IR-UWB) signals, which offer fine temporal resolution, low power consumption, and strong resistance to multipath interference. Two typical modulation schemes, namely pulse position modulation (PPM) and binary phase shift keying (BPSK), are considered for communication over the delay and phase domains, respectively. An innovative differential decoupling strategy is proposed, which eliminates the need for pilot symbols by leveraging the known starting symbol position. The sensing performance under various modulation and demodulation schemes is analyzed and compared with the conventional pilot-based (time-delay) decoupling strategy under current UWB standards. A key contribution of this work is the development of a unified analytical framework based on the Fisher information matrix (FIM), which characterizes the fundamental coupling between communication and sensing in both delay and Doppler domains. This coupling is examined through the singularity structure of the FIM, providing new insights into the joint performance limits of UWB-ISAC systems. Performance evaluation is conducted using the Cramer-Rao Lower Bound (CRLB) for sensing and the data transmission rate for communication, offering theoretical insights into choosing suitable data signal processing methods in real-world applications.

We investigate a time-sensitive wireless networked control system (WNCS) where multiple Internet of Things (IoT) sensors embedded with their respective local controllers send their observations to a remote controller over shared wireless channels. From an infinite-time horizon perspective, each process should be stabilized essentially to prevent the process’s states from divergence. Nevertheless, limited channel resources may not fulfill users’ stability requirements due to possibly insufficient transmission attempts. Regarding the tradeoff between stability property and channel resources, we aim to design a transmission scheduling policy that minimizes the infinite-time control cost under channel constraints. Starting with the stability condition analysis under varying scheduling policies, the applied decentralized networked control architecture shows its superiority in extending the WNCS’s scale. By approximately expressing control cost as a function of the Age of Information (AoI), the considered scheduling issue is transformed into an AoI-dependent optimization problem under channel and stability constraints. Then, we develop a control-oriented Whittle index policy where AoI, system parameters, and stability incentives construct the Whittle indexes. Numerical results demonstrate that our proposed policy outperforms the baseline policies in terms of control cost, especially in heterogeneous WNCSs. Furthermore, results show that the proposed policy containing stability factor can support more users with respective stability guarantees.

The integrated communication and localization (ICAL) has become as a pivotal technology in the evolution towards B5G and 6G networks, particularly for a variety of emerging wireless applications. In ICAL networks, resource allocation and beamforming design are critical components that significantly influence both the precision of localization and the efficiency of communication. Moreover, high accuracy synchronization is extremely challenging in wireless networks. In this paper, we investigate the tradeoff between spectral efficiency (SE) and position error bound (PEB) by formulating a robust power and time-slot allocation and beamforming design problem for asynchronous ICAL networks with the imperfect initial position. We first illustrate the coupling between SE and position error through channel estimation error. Then, we derive a lower bound on the position error in terms of the Fisher information matrix (FIM). The alternating optimization, convex approximate and Bernstein-type inequality algorithms are proposed to solve the non-convex problems. Finally, the simulation results reveal the tradeoff between SE and PEB, and validate the robustness and effectiveness of the proposed algorithms.

In this paper, we investigate the effects of severe channel fading in the scenario of distributed deep learning-based joint source-channel coding (DJSCC) for image transmission without perfect channel state information (CSI). To tackle the challenges posed by imperfect CSI, we propose a robust DJSCC (RDJSCC) scheme that operates at three levels: modulation, encoding, and decoding, respectively. Firstly, at the modulation level, we adopt orthogonal frequency division multiplexing (OFDM) modulation for exploring the tradeoff between reconstruction performance and peak-to-average power ratio (PAPR). Secondly, at the encoding level, two parameter-efficient operators are introduced to combat channel fading with low encoding complexity. Finally, at the decoding level, we divide the decoding process into two stages, i.e., denoising and recovery, aiming to maximize the correlation between the encoded representations. Theoretic analysis and simulation results show that our proposed RDJSCC can effectively alleviate the effects of severe fading with imperfect CSI, leading to an improved reconstruction performance while maintaining low PAPR and encoding complexity.

In this paper, we investigate the system performance of deep joint source-channel coding (JSCC) for task-oriented transmission in the Wyner-Ziv scenario, i.e., a distributed coding scenario, aiming to improve the image reconstruction performance and task accuracy. Unlike existing deep JSCC based methods, we introduce regions of interest (ROI), which facilitates the effective utilization of side information for enhancing task performance. Meanwhile, we incorporate a spatial analysis mechanism to fuse the side information. By integrating these two mechanisms, we propose a novel distributed deep JSCC scheme that further leverages task relevance within the side information. Simulation results show that our proposed scheme outperforms the benchmark in terms of image reconstruction performance and task accuracy. The code is available on the project website1.

Mobile-edge computing (MEC) is a promising computing scheme to support computation-intensive AI applications in vehicular networks, by enabling vehicles to offload computation tasks to edge computing servers deployed on road side units (RSUs) that approximate to them. In this work, we consider an MEC-enabled vehicular edge network (VEN), where each vehicle can offload tasks to edge/cloud computing servers via vehicle-to-infrastructure (V2I) links or to other end-vehicles via vehicle-to-vehicle (V2V) links. In such acloud-edge–endcollaborative offloading scenario, we focus on the joint task offloading, scheduling, and resource allocation problem for vehicles, which is challenging due to the online and asynchronous decision-making requirement for each task. To solve the problem, we propose aMultilayer deep reinforcement learning(DRL)-based approach, where each vehicle constructs and trains three modules to make different layers’ decisions: 1)Offloading Module(first layer), determining whether to offload each task, by using the dueling and double deepQ-network (D3QN) framework; 2)Scheduling Module(second layer), determining where and how to offload each task in the offloading queues, together with the transmission power, by using the parameterized deepQ-network (PDQN) framework; and 3)Computing Module(third layer), determining how much computing resource to be allocated for each task in the computation queues, by using classic optimization techniques. We provide the detailed algorithm design and perform extensive simulations to evaluate its performance. Simulation results show that our proposed algorithm outperforms the existing algorithms in the literature, and can reduce the average cost by 25.86%–72.51% and increase the average satisfaction rate by 3.48%–90.53%.

Since the traffic administration at road intersections determines the capacity bottleneck of modern transportation systems, intelligent cooperative coordination for connected autonomous vehicles (CAVs) has shown to be an effective solution. In this paper, we try to formulate a Bi-Level CAVs intersection coordination framework, where coordinators from High and Low levels are tightly coupled. In the High-Level coordinator where vehicles from multiple roads are involved, we take various metrics including throughput, safety, fairness and comfort into consideration. Motivated by the time consuming space-time resource allocation framework, we try to give a low complexity solution by transforming the complicated original problem into a sequential linear programming one. Based on the “feasible tunnels” (FT) generated from the high-Level coordinator, we then propose a rapid gradient-based trajectory optimization strategy in the low-level planner, to effectively avoid collisions beyond high-level considerations, such as the unexpected pedestrian or bicycles. Simulation results and laboratory experiments show that our proposed method outperforms existing strategies. Moreover, the most impressive advantage is that the proposed strategy can plan vehicle trajectory in milliseconds, which is promising in real-world deployments. A detailed description include the coordination framework and experiment demo could be found at the supplement materials, or online at https://youtu.be/MuhjhKfNIOg.

Integrated sensing and communication (ISAC) is considered to be a promising paradigm for future wireless evolution and emerging services. In such a framework, the spectrum efficiency and hardware utilization are significantly enhanced, since the radar detection and data transmission share the same spectrum and hardware. This paper studies a rate-splitting multiple access enabled ISAC (RISAC), wherein the multi-antenna RISAC transmitter simultaneously senses a single target with the help of a multi-antenna radar receiver, and communicates with a group of users with single-antenna for data transmission based on RSMA. We focus on optimizing the sensing performance in terms of Cramér-Rao lower bound (CRLB), while achieving acceptable data transmission performance in terms of data rate. To this end, we aim to minimize the CRLB of the sensing response matrix at the radar receiver, by adequately designing the RSMA structure and the associated parameters, with constraints of data rate requirement and transmit power budget. To tackle the non-convex CRLB minimization problem, we split the original problem into outer and inner subproblems, which can be efficiently solved by particle swarm optimization algorithm and semidefinite relaxation method, respectively. Numerical results confirm that RISAC outperforms the space division multiple access based strategy in terms of CRLB.

The growing power of cloud computing prompts data owners to outsource their databases to the cloud. In order to meet the demand of multi-dimensional data processing in big data era, multi-dimensional range queries, especially over cloud platform, have received extensive attention in recent years. However, since the third-party clouds are not fully trusted, it is popular for the data owners to encrypt sensitive data before outsourcing. It promotes the research of encrypted data retrieval. Nevertheless, most existing works suffer from single-dimensional privacy leakage which would severely put the data at risk. Up to now, although a few existing solutions have been proposed to handle the problem of single-dimensional privacy, they are unsuitable in some practical scenarios due to inefficiency, inaccuracy, and lack of support for diverse data. Aiming at these issues, this paper mainly focuses on the secure range query over encrypted data. We first propose an efficient and private range query scheme for encrypted data based on homomorphic encryption, which can effectively protect data privacy. By using the dual-server model as the framework of the system, we not only achieve multi-dimensional privacy-preserving range query but also innovatively realize similarity search based on MinHash over ciphertext domains. Then we perform formal security analysis and evaluate our scheme on real datasets. The result shows that our proposed scheme is efficient and privacy-preserving. Moreover, we apply our scheme to a shopping website. The low latency demonstrates that our proposed scheme is practical.

In this paper, we study the joint resource allocation and the corresponding performance of non- orthogonal multiple access (NOMA) systems with imperfect successive interference cancellation (SIC), wherein we consider the precoding, user clustering and power allocation design in a single cell with one base station and multiple users. Specifically, we propose a precoding scheme based on zero-forcing beamforming, and a user clustering algorithm based on channel correlation to reduce inter-cluster interference. In order to maximize the sum capacity, the power allocation optimization problem is formulated and solved via interior point methods. Numerical and simulation results demonstrate that our proposed design has better sum capacity performance when SIC is imperfect.

The aim of this work is to improve the secrecy capacity of primary users (PUs), meanwhile, spectrum utilisation and energy efficiency are considered. the authors present a communication system model with secondary users (SUs). The SUs are provided access to the spectrum. Also, by means of beamforming, their signals will not interfere the PUs but eavesdropper, and the PUs' transmissions are protected. By leveraging the SUs instead of traditional jamming nodes can also make the energy efficiency higher. They formulate the system model, signalling plan, and key enabling techniques to enhance the spectrum efficiency and PUs' physical‐layer security with SUs' participation. They provide theoretic analysis of a sum capacity maximisation under a certain power constraint to evaluate the performance of this system. Numerical results show that the proposed scheme not only improves PU's secrecy capacity but also enhances the spectrum utilisation.

The long-haul communication systems can offer ultra high-speed data transfer rates but suffer from burst errors. The high-rate and high-performance staircase codes provide an efficient way for long-haul transmission. The staircase coding scheme is a concatenation structure, which provides the opportunity to improve the performance of high-rate polar codes. At the same time, the polar codes make the staircase structure more reliable. Thus, a high-rate polar-staircase coding scheme is proposed, where the systematic polar codes are applied as the component codes. The soft cancellation decoding of the systematic polar codes is proposed as a basic ingredient. The encoding of the polar-staircase codes is designed with the help of density evolution, where the unreliable parts of the polar codes are enhanced. The corresponding decoding is proposed with low complexity, and is also optimized for burst error channels. With the well designed encoding and decoding algorithms, the polar-staircase codes perform well on both AWGN channels and burst error channels.

The integration of high throughput satellite into Internet of Things (IoT) is regarded as an effective strategy to provide ubiquitous broadband access in a seamless, cost-efficient manner. Meanwhile, due to the demand of machine-to-machine (M2M) high throughput services, millimeter-wave (mmWave) IoT networks arouses huge interest. In this paper, we investigate the performance of an amplify-and-forward (AF) mmWave hybrid satellite-terrestrial relay networks (HSTRN) for IoT broadband communications, where we assume source-relay link undergos Shadowed-Rician fading and the relay-destination link undergos Rayleigh fading. Considering rain attenuation is the main factor at mmWave bands, we utilize the multidimensional rain attenuation model to analyze the effect of rain attenuation on system performance. Then we derive the closed-form expression of outage probability and tight approximation of ergodic capacity. Finally, numerical and simulation results are provided to validate our analytical results and show the effect of rain attenuation on the system performance.

Machine-to-Machine (M2M) communications are expected to support extremely harsh requirements on both latency and reliability, which is characterized by the ultra-reliable, low-latency coding (uRLLC) technology in physical layer. In this paper, motivated by the recent development on the finite-blocklength information theory, we propose an ultra-reliable short blocklength analog fountain code (AFC) for M2M communications. First, we use the extrinsic information transfer (EXIT) chart to analyze the AFC compressive sensing belief propagation (CS-BP) decoding algorithm, by tracking the mutual information of AFC CS-BP decoding process, which related to the channel dispersion for the short blocklength AFC. Then, based on the EXIT chart analysis, we propose a Weight-set optimization progressive edge-growth (WO-PEG) encoding algorithm for the short blocklength AFC. Simulation results show that the proposed WO-PEG AFC scheme can effectively improve block error rate (BLER) in the short blocklength regime.

Participatory sensing (PS) is a novel and promising sensing network paradigm for achieving a flexible and scalable sensing coverage with a low deploying cost, by encouraging mobile users to participate and contribute their smartphones as sensors. In this work, we consider a general PS system model with location-dependent and time- sensitive tasks, which generalizes the existing models in the literature. We focus on the task scheduling in the user-centric PS system, where each participating user will make his individual task scheduling decision (including both the task selection and the task execution order) distributively. Specifically, we formulate the interaction of users as a strategic game called Task Scheduling Game (TSG) and perform a comprehensive game-theoretic analysis. First, we prove that the proposed TSG game is a potential game, which guarantees the existence of Nash equilibrium (NE). Then, we analyze the efficiency loss and the fairness index at the NE. Our analysis shows the efficiency at NE may increase or decrease with the number of users, depending on the level of competition. This implies that it is not always better to employ more users in the user-centric PS system, which is important for the system designer to determine the optimal number of users to be employed in a practical system.

Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve a flexible and scalable sensing coverage with a low deployment cost, by employing mobile users/devices to perform sensing tasks. In this work, we propose a novel MCS framework with data reuse, where multiple tasks with common data requirement can share (reuse) the common data with each other through an MCS platform. We study the optimal assignment of mobile users and tasks (with data reuse) systematically, under both information symmetry and asymmetry, depending on whether the user cost and the task valuation are public information. In the former case, we formulate the assignment problem as a generalized Knapsack problem and solve the problem by using classic algorithms. In the latter case, we propose a truthful and optimal double auction mechanism, built upon the above Knapsack assignment problem, to elicit the private information of both users and tasks and meanwhile achieve the same optimal assignment as under information symmetry. Simulation results show that by allowing data reuse among tasks, the social welfare can be increased up to 100~380%, comparing with those without data reuse.

In this paper, we propose a novel compressed sensing (CS) algorithm by collaborative use of statistical and structural priors of natural images. The statistical priors include two aspects which are the statistical dependencies of wavelet coefficients in transform domain and non-local self- similarity among pixels in spatial domain. And the structural prior refers to the structural dependencies of wavelet coefficients in transform domain. Our algorithm which employs both multi- domain as well as multi-class prior information is realized under the framework of iterative hard thresholding (IHT). The reconstruction process is divided into two stages. In the first stage, the local statistical prior model is used to correct the signal estimation to obtain the preliminary estimation. In the second stage, first the non- local self-similarity model, and then the global structural prior model are employed to further refine the preliminary estimation. The results show that our algorithm outperforms the state of art. Our algorithm can be utilized in efficient communication in multimedia internet of vehicles (IoV). We demonstrate the effectiveness of our algorithm for multimedia IoV devices by showing its capacity in reducing the amount of multimedia data need to be transmitted while improving the recovery quality.

In this paper, we address the interference problem caused by the cross-polarization components in a massive dualpolarized MIMO (DP-MIMO) system over Ricean-K fading Channel. To effectively suppress the interference, a novel precoding design based on oblique projection is proposed. Furthermore, compared with an Nt × Nr uni-polarized MIMO (UP- MIMO), Nt×Nr DP-MIMO can maintain the same diversity order while achieve twice the multiplexing gain of UP-MIMO in symbol error rate (SER) performance by using the proposed precoding design. The expression of the moment generation function (MGF) of signal noise ratio (SNR) for the proposed scheme is derived, and an analytical expression of SER with M-ary phase-shift keying (M-PSK) modulation is obtained. The effectiveness of the proposed scheme is demonstrated through extensive numerical results.

Abstract Millimeter wave (mmWave) communication is a promising technology to support high‐rate (e.g., multi‐Gbps) multimedia applications because of its large available bandwidth. Multipacket reception is one of the important capabilities of mmWave networks to capture a few packets simultaneously. This capability has the potential to improve medium access control layer performance. Because of the severe propagation loss in mmWave band, traditional backoff mechanisms in carrier sensing multiple access/collision avoidance (CSMA/CA) designed for narrowband systems can result not only in unfairness but also in significant throughput reduction. This paper proposes a novel backoff mechanism in CSMA/CA by giving a higher transmission probability to the node with a transmission failure than that with a transmission success, aiming to improve the system throughput. The transmission probability is adjusted by changing the contention window size according to the congestion status of each node and the whole network. The analysis demonstrates the effectiveness of the proposed backoff mechanism on reducing transmission collisions and increasing network throughput. Extensive simulations show that the proposed backoff mechanism can efficiently utilize network resources and significantly improve the network performance on system throughput and fairness. Copyright © 2014 John Wiley & Sons, Ltd.

In a sensing‐based hybrid spectrum sharing paradigm, cognitive radio first performs spectrum sensing to identify primary users’ states (idle/busy) and then adapts its transmit power according to sensing outcomes and channel conditions. To investigate the capacity of such systems in fading channels, existing works modelled fading channel in transmission phase while additive white Gaussian noise channel in spectrum sensing; however, sensing channels also exhibit fading characteristics in practice. Therefore a more realistic system model with channel fading in both sensing and transmission is considered in this study. Under the new system model, spectrum sensing and power allocation are coupled in the ergodic capacity and an equivalent decoupling processing is proposed via mathematical manipulations. Further, joint sensing and power allocation over Rayleigh fading is studied under average interference and transmit power constraints. The optimal and suboptimal schemes are obtained by alternating optimisation and Lagrangian dual method. Finally, system performance is evaluated via extensive numerical simulations.

This study proposes a novel adaptive network‐coded rateless coding scheme for an erasure multiple‐access relay system with two distributed sources and an asymmetric network topology. To increase transmission efficiency, a two‐dimensional degree distribution, as part of network‐coded relay protocol, is designed based on the AND–OR tree analysis technique. The degree distributions of rateless coding at the sources and network coding at the relay are optimised by the linear programming approach under asymmetric channel conditions. Simulation results demonstrate that the proposed scheme outperforms existing classical relay protocols under time‐varying channel conditions, and achieves a significantly better performance.

The Feher quadrature phase shift keying (FQPSK) modulation is significantly susceptible to frequency and phase offsets under low signal‐to‐noise ratios. In this study, the authors proposed a serially concatenated signalling scheme with FQPSK modulation and low‐density parity‐check coding, which could efficiently resist residual frequency offset by employing an intended compensation algorithm. The designed maximum‐likelihood estimation‐enabled compensation algorithm is incorporated into the iterative concatenated demodulation‐decoding process by using soft‐input–soft‐output‐based maximum‐a‐posteriori‐probability criterion. On the other side, the codeword sequence to be transmitted at the sender is re‐arranged in a pre‐configured order different from original codeword, in order to help the compensation algorithm diminish the impacts of frequency offsets. Simulation results show that the bit error rate of the proposed scheme can be improved efficiently up to three orders of magnitude with the frequency offsets from 100 to 700 ppm.

This paper investigates the effects of spectrum sensing errors on the performance of cognitive radio based dynamic spectrum access system (CR-DSA). We first analyze the DSA process with imperfect sensing information by a continuous-time Markov chain (CTMC) model, and then derive the performance metrics with respect to the sensing errors. To alleviate effect of errors in the spectrum sensing process on the system performance, we propose a joint optimization of the spectrum sensing and DSA process. The design is based on the observation that there exists the unique optimal false alarm (FA) probability/miss detection (MD) probability such that the achievable throughput of secondary system maximal. To find the optimal FA probability, a gradient information based algorithm is proposed, and simulation results reveal a significant performance improvement by virtue of the proposed algorithm.

An orthogonal modulation enabled two-phase energy-efficient framework is presented for active cooperation between secondary users (SUs) and primary users (PUs) in a cognitive radio network. Since the PU has higher priority, and SUs compete for spectrum accessing, we model the power control problem as a Stackelberg game which incorporates throughput and energy consumptions into utility design. Due to the two-phase feature and SUs' power constraint, this game is played in an additive coupled sum constrained type. Unique Nash Equilibrium is achieved in analytical format, and simulations demonstrate the effectiveness of the proposed cooperation framework.

This work is concerned with enhancement of spectrum-energy efficiency whereby a primary user (PU) engages secondary users (SUs) to relay its transmission in an energy-aware cognitive radio network, i.e., forming a cooperative cognitive radio network (CCRN). The cooperation framework in CCRN can be multiple two-hop relaying with or without PU's direct link transmission using an amplify-and-forward or decode-and-forward mode. In the energy-aware CCRN, an individual cooperating partner attempts to maximize its own utility. The partner selection and parameter optimization, led by the PU, are formulated as two Stackelberg games, namely a sum-constrained power allocation game for two-phase cooperation and a power control game for three-phase cooperation, respectively. Unique Nash Equilibrium is proved and achieved in analytical format for each game. The optimal communication strategy is chosen which achieves the maximum PU utility among different optimal communication strategies. Moreover, an implementation scheme is presented to perform the partner selection and parameter optimization based on the analytical results. Theoretical analysis and performance evaluation show that the proposed CCRN model is a promising framework under which the PU's utility is maximized, while the relaying SUs can attain acceptable utilities.

Opportunistic network (OPPNET) is characterized by the intermittent connectivity among mobile nodes from their unpredictable mobility. Although it is promising, there still exist many security and privacy challenges. In this paper, we present an incentive and privacy-aware data dissemination (IPAD) scheme for OPPNETs, not only to exploit how to protect mobile node's identity privacy, location privacy and social profile privacy, but also to provide a secure incentive for privacy-aware data dissemination. Through extensive incentive analysis, we show that only if a source provides a secure incentive strategy, can a data packet be efficiently disseminated in OPPNETs.

The Feher quadrature phase shift keying (FQPSK) modulation based Physical-layer Network Coding (PNC) is investigated in this paper, by which the nonlinear distortion effects resulted from the high power amplifier (HPA) in the system can be avoided. In our presented framework, a novel remapping rule for the FQPSK modulation in the PNC system is proposed to make a better bit error rate (BER) performance. Moreover, a joint demapping-and-demodulation scheme based on Low Density Parity Check (LDPC) is employed to recover the data bits with a low computational burden. Numerical results demonstrate the efficiency of the proposed method.

To suppress co‐channel interference in polarisation‐enabled wireless communication systems, this work aims to provide an interference suppression scheme by exploiting polarisation domain, besides the state‐of‐the‐art temporal, frequency, spatial and code domains. System models, algorithms, characteristics and applications of polarisation filtering (PF) for co‐channel interference suppressions for polarisation‐enabled (e.g. orthogonal dually polarised antennas) wireless communications are investigated. Specifically, four system models for PF using subspace analysis are established and discussed. The four proposed system models are categorised based on different statistic characteristics of the target signal and that of the interfering signal: both the target signal and interference are temporal deterministic, the target signal is deterministic whereas interference is temporal random, the target signal is random whereas interference is deterministic and both the target signal and interference are random, respectively. Based on the statistic characteristics and subspace theory, the detailed PF implementation for each model is analysed and the closed‐form filtering operator is given. It is also shown that the PF implementation for each model can be attained by using one of the zero‐forcing matched subspace processing, decorrelating matched subspace processing or Wiener subspace processing. Furthermore, relationship among these four models indicates that, under certain conditions, the implementation of the other three models can be fulfilled by using the implementation of the first model. Numerical and simulation results show the effectiveness of the proposed scheme.

This work is concerned with enhancement of spectrum utilization by using polarization enabled two-phase cooperation between primary users (PUs) and secondary users (SUs) in cooperative cognitive radio networking (CCRN). The use of orthogonally dual-polarized antennas (ODPAs) enables concurrent transmissions of multiple independent signals of PUs and SUs, and interference suppression via polarization zero-forcing and polarization filtering to obtain significant performance improvement. To maximize a weighted sum throughput of PUs and SUs under energy/power constraints, the problem is formulated and solved based on a multi-timescale Markov decision process, and two modified backward iteration algorithms are devised to attain the optimal policies. Numerical results validate the effectiveness of the proposed CCRN framework, showing that the obtained policy outperforms both greedy and random ones.

A novel polarization enabled two-phase cooperation framework for cognitive radio networking is proposed in this paper. By leveraging the degrees of freedom provided by orthogonally dual-polarized antennas, secondary users can relay the traffic of primary users and transmit their own in the same time slot without interference. To evaluate the performance of the proposed framework, a sum throughput maximization problem is formulated. By using the geometric programming algorithms, the nonlinear and non-convex optimization problem is solved by applying different power constraints for high and medium (or low) signal-to-noise ratio regimes. Simulation results validate the effectiveness of the proposed two-phase framework.

Millimeter-wave (mmWave) communications is a promising technology to provide high data rates (multiGigabit) for indoor multimedia applications. However, indoor mmWave links are highly susceptible to blockage because of the limited ability to diffract around obstacles such as the human body and furniture. In order to realize high-rate reliable transmission, cooperative communication is utilized to tackle with the scenarios where LOS link of source node and destination node is blocked. Specifically, with directional antenna, we first select the node in the feasible region with best achievable rate of cooperative communication as the cooperative relay. Then, cooperative concurrent transmission scheduling is formulated as an optimization problem to maximize the transmission efficiency. A flip-based heuristic scheduling algorithm is proposed to obtain the real-time solution. Extensive simulations demonstrate that the proposed cooperative concurrent transmission scheduling (CCTS) scheme can significantly increase the transmission throughput and utilize network resource efficiently while maintaining network connectivity.

A quadrature signaling based two-phase cooperation framework for cooperative cognitive radio networking is proposed. By leveraging the degrees of freedom provided by orthogonal modulation, secondary users are able to relay the traffic of primary users and transmit their own in the same time slot without interference. To evaluate the cooperation performance of the proposed framework, a weighted sum throughput maximization problem is formulated, and closed-form solutions of the optimal power setting/allocation are obtained in the amplify-and-forward and decode-and-forward relaying modes. Simulation results validate the efficiency of the proposed framework.

The radar members are likely to interfere with each other if their waveforms and polarized states are not orthogonal in radar sensor network (RSN). In this paper, we propose the oblique projection polarization filtering (OPPF) based interference suppressions for RSN where each radar member is equipped with the orthogonally dual-polarized antenna (ODPA). In our discussed cooperative environment, under which radar members share their polarized states, members radiate EM waves using the same waveform but different polarized states, however, their polarized states are not needed to be orthogonal. Doppler-Shift and its uncertainty are not involved due to the independence from the polarized state, which makes the proposed method simple and effective. The results demonstrate that, after passing through the proposed OPPF scheme, each radar member can effectively suppress the echoes from the others while keep its own amplitude and phase unchanged, which improves the target detection performance of the RSN. Theoretical analysis is done, and the simulation results are illustrated, both showing the proposed method suitable for suppressing interferences for RSN.

Polarization filtering has attracted a great interests for it can be used to solve problems of signal separation and interference suppression those are difficult to process in the time, frequency and spatial domains. Polarization information of both target signal and interference are needed to design the polarization filter in the conventional method, while exact estimation of the polarization information is difficult and some estimation errors also render poor performance of polarization filtering. Based on the superior merits of oblique projection in signal processing applications, a novel blind adaptive oblique projection polarization filtering (OPPF) algorithm is proposed in this paper. The pseudo-inverse of the covariance matrix obtained from the received signal and the polarization state of target signal are used to construct the vector of polarization filtering, and the estimation of interference polarization is replaced by the power estimation of AWGN. Detailed analysis and deduction are made, and simulation and numerical results show the effectiveness of the proposed algorithm, which is in-line-with the theory of polarization filtering.

A novel and robust subspace-based blind adaptive detector for synchronous CDMA systems based on oblique projection is proposed in this paper, in respect that the oblique projection can be used to extract desired signal while nulling interferences. The suggested method requires the assumption that the desired user's spreading code and noise variance of AWGN are known to the receiver rather than the assumption that all users' spreading codes are known to the receiver in conventional subspace-based detection, and this assumption can be obtained more realistic. When the noise variance of AWGN is not available, the rank-reduced form is also given. It is shown that this detector performs a perfect rejection of MAI. It is known that the subspace-based approach is robust to the near-far effect, thus the proposed scheme based on this property is immune to the near-far effect.

Recent decades, more and more people both from academic and commercial pay attention to blind signal separation (BSS). As an important part, independent component analysis (ICA) is a valid and effective solution to the problem of BSS, under the assumption conditions of ICA, a BSS algorithm using oblique projection operators is proposed in this paper. The autocorrelation matrix of mixing matrix is used to construct the objective function while the principle of maximum kurtosis is adopted to iterate and extract the component, and the mixing matrix can be obtained in a direct way. The description of the problem is demonstrated, and the detailed flow of the proposed method is listed. Simulation results show the suggested scheme is valid even when the weakest signal is less than -80dB to others.