The pervasive threat of jamming attacks, particularly from adaptive jammers capable of optimizing their strategies, poses a significant challenge to the security and reliability of wireless communications. This paper addresses this issue by investigating anti-jamming communications empowered by an active reconfigurable intelligent surface. The strategic interaction between the legitimate system and the adaptive jammer is modeled as a Stackelberg game, where the legitimate user, acting as the leader, proactively designs its strategy while anticipating the jammer’s optimal response. We prove the existence of the Stackelberg equilibrium and derive it using a backward induction method. Particularly, the jammer’s optimal strategy is embedded into the leader’s problem, resulting in a bi-level optimization that jointly considers legitimate transmit power, transmit/receive beamformers, and active reflection. We tackle this complex, non-convex problem by using a block coordinate descent framework, wherein subproblems are iteratively solved via convex relaxation and successive convex approximation techniques. Simulation results demonstrate the significant superiority of the proposed active RIS-assisted scheme in enhancing legitimate transmissions and degrading jamming effects compared to baseline schemes across various scenarios. These findings highlight the effectiveness of combining active RIS technology with a strategic game-theoretic framework for anti-jamming communications.

Satellite communications are envisioned as a key enabler for ubiquitous coverage in future 6G networks, yet the broadcast nature renders them vulnerable to eavesdropping, especially given the long-distance transmissions and associated high uncertainties. In this paper, we propose the physical layer security enhancement for multi-beam satellite communications with the assistance of an aerial reconfigurable intelligent surface (ARIS). Considering the high dynamics and uncertainties of channels, we characterize the channel distribution with moment-based ambiguity sets. Accordingly, a distributionally robust secrecy rate optimization is formulated through joint design of transmit and reflection beamforming. We then introduce a conditional value-at-risk-based reformulation to convert the probabilistic constraints into deterministic forms. An alternating optimization framework is subsequently employed to iteratively update the transmit and reflective beamforming vectors until convergence. Simulation results demonstrate that the proposed distributionally robust scheme significantly enhances secrecy performance, and maintains reliable performance across various channel error distributions.

The fundamental capacity limits of pinching-antenna systems (PASS)-based multi-user communication network are investigated. Two practical pinching-antenna configuration strategies are considered, namelymultiple-time discrete activationandone-off continuous sliding. For each strategy, the capacity and rate regions are characterized under the non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes, respectively. 1) For NOMA, the capacity region achieved by the discrete activation is first characterized. In particular, the ideal case with the infinite number of activation times is considered, where the optimal PA activation and resource allocation scheme is derived. It is shown that different user groups or decoding orders are served via time-sharing. Inspired by this result, an inner bound of capacity region is obtained for the practical case with a finite number of activation times. Then, for the continuous sliding case, the inner bound of capacity region is characterized by alternately optimizing the resource allocation and PAs’ continuous positions. 2) For OMA, the rate region is first obtained for the discrete activation case. It is unveiled that multiple users are successively served. Then, by alternately optimizing the resource allocation and PAs’ continuous positions, the inner bound of rate region is obtained for the continuous sliding case. Numerical results demonstrate that i) PASS can significantly improve the capacity performance compared with the conventional fixed-antenna systems; ii) the capacity gain can be further enhanced by using the proposed PA activation and sliding and resource allocation schemes; and iii) the continuous sliding has the potential to outperform the discrete activation in NOMA, whereas the latter performs better in OMA.

The proliferation of unmanned aerial vehicles (UAVs) in low-altitude airspace necessitates sophisticated resource management supporting both cooperative communications and unauthorized intrusion detection. This paper investigates joint optimization of cell association and power allocation in integrated sensing and communication (ISAC)-enabled low-altitude networks. We propose a novel dual-function framework where ground base stations simultaneously provide communication services to authorized UAVs and localize non-cooperative UAVs for collision avoidance. We establish a channel model capturing the relationship between communication rate and sensing accuracy, formulating an optimization problem that maximizes the weighted sum of system average sum rate and localization quality of service (QoS). The problem jointly optimizes cell association, communication power allocation, and sensing power allocation under UAV localization QoS and cooperative sum rate constraints. To solve the resulting mixed-integer non-convex problem, we propose a joint optimization algorithm based on optimal transport theory (J2OT) that directly handles discrete variables without relaxation, avoiding accuracy losses of conventional approximation methods. J2OT decomposes the problem using optimal transport-based cell association optimization (OTC) and power allocation optimization (OTP). Simulation results demonstrate J2OT’s superiority, achieving 1.5 bits/s/Hz improvement in system objective and 7.5% reduction in localization Cramér-Rao bound compared to Weighted Voronoi and Iterative Water-filling baseline methods.

Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoWblockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.

Malicious jamming presents a pervasive threat to the secure communications, where the challenge becomes increasingly severe due to the growing capability of the jammer allowing the adaptation to legitimate transmissions. This paper investigates the jamming mitigation by leveraging an active reconfigurable intelligent surface (ARIS), where the channel uncertainties are particularly addressed for robust anti-jamming design. Towards this issue, we adopt the Stackelberg game formulation to model the strategic interaction between the legitimate side and the adversary, acting as the leader and follower, respectively. We prove the existence of the game equilibrium and adopt the backward induction method for equilibrium analysis. We first derive the optimal jamming policy as the follower’s best response, which is then incorporated into the legitimate-side optimization for robust anti-jamming design. We address the uncertainty issue and reformulate the legitimate-side problem by exploiting the error bounds to combat the worst-case jamming attacks. The problem is decomposed within a block successive upper bound minimization (BSUM) framework to tackle the power allocation, transceiving beamforming, and active reflection, respectively, which are iterated towards the robust jamming mitigation scheme. Simulation results are provided to demonstrate the effectiveness of the proposed scheme in protecting the legitimate transmissions under uncertainties, and the superior performance in terms of jamming mitigation as compared with the baselines.

Satellite communication is envisioned as a key enabler of future 6G networks, yet its wide coverage with high link attenuation poses significant challenges for physical layer security. In this paper, we investigate secure multi-beam, multi-group satellite communications assisted by aerial reconfigurable intelligent surfaces (ARISs). To maximize the sum of achievable multicast rates among the groups while constraining wiretap rates, we formulate a joint optimization problem involving transmission and reflection beamforming, ARIS-group association, and ARIS deployment. Due to the mixed-integral and non-convex nature of the formulated problem, we propose to decompose the problem and employ the block coordinate descent framework that iteratively solves the subproblems. Simulation results demonstrate that the proposed ARIS-assisted multi-beam satellite system provides a notable improvement in secure communication performance under various network scenarios, offering useful insights into the deployment and optimization of intelligent surfaces in future secure satellite networks.

Deploying integrated sensing and communication (ISAC) technology in future Internet of Drones (IoD) networks is a significant trend. In this paper, we investigate the exploration of ISAC signals within IoD networks, enabling Unmanned Aerial Vehicles (UAVs) to engage in vehicle trajectory tracking and communication. Taking into account real-world road constraints and scenarios of non-continuous communication, we propose a grid model and leverage the Hidden Markov Model (HMM) for tracking vehicle trajectories, while conducting channel estimation at the vehicle using ISAC signals. Simulation results demonstrate a notable performance enhancement of the proposed scheme compared to baseline method relying on direct rounding decisions, particularly in low SNR conditions.

In this paper, we propose a dynamic grid sparse Bayesian inference (DGSBI) method for near-field channel estimation in extremely large-scale multiple-input multiple-output (XL-MIMO) systems. To address the off-grid problem of scatterers in the polar-domain channel, we represent the polardomain dictionary dynamically using angle and distance offsets and provide a sparse representation of the channel within this dynamic dictionary. By updating the grid through angle and distance offset variables, the proposed method significantly improves signal recovery performance in the sparse polar domain. The dictionary update process is modeled as a linearquadratic (LQ) optimization problem, and we derive an analytical solution for the update. Simulation results demonstrate that our method outperforms benchmark approaches in terms of estimation accuracy.

Due to their flexibility and dynamic coverage capabilities, Unmanned Aerial Vehicles (UAVs) have emerged as vital platforms for emergency communication in disaster-stricken areas. However, the complex channel conditions in high-speed mobile scenarios significantly impact the reliability and efficiency of traditional communication systems. This paper presents an intelligent emergency communication framework that integrates Orthogonal Time Frequency Space (OTFS) modulation, semantic communication, and a diffusion-based denoising module to address these challenges. OTFS ensures robust communication under dynamic channel conditions due to its superior anti-fading characteristics and adaptability to rapidly changing environments. Semantic communication further enhances transmission efficiency by focusing on key information extraction and reducing data redundancy. Moreover, a diffusion-based channel denoising module is proposed to leverage the gradual noise reduction process and statistical noise modeling, optimizing the accuracy of semantic information recovery. Experimental results demonstrate that the proposed solution significantly improves link stability and transmission performance in high-mobility UAV scenarios, achieving at least a 3dB SNR gain over existing methods.

In frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) communication systems, the base station (BS) requires channel state information (CSI) reported from user equipment (UE) for downlink precoding, which brings in significant feedback overhead. In this paper, we propose a position-based precoding method, where the precoder at the BS is directly derived from the location information of UE, without relying on channel measurement and CSI feedback. To achieve this, we devise a novel neural network (NN) structure called P2P-Net (Position-to-Precoder Net), which includes a position encoding module, an adaptive combination weight, and a refining module based on self-attention mechanism. With deep learning techniques, P2P-Net is able to learn the information about scatterers in the signal propagation environment, thereby realizing the mapping from position to precoder. Simulation results demonstrate the superiority of the proposed positionbased precoding method compared with existing feedback-based solutions in terms of spectral efficiency and communication overhead.

This paper propose a graph neural network (GNN) framework to achieve physical layer security. We consider the secure communication between a multi-antenna base station and multiple users, in the presence of multiple eavesdroppers, where the GNN-based beamforming is conducted for secure transmissions. Particularly, we reinterpret the networks roles as graph elements and track the inter-user interference through the graph structure, and thus the secrecy rate maximization is obtained through neural network training. Numerical results indicates that the proposed GNN approach approximate the secrecy performance as compared with the conventional optimization techniques, while obtaining the solution in a more efficient manner, with the ability to adapt and scale in dynamic wireless networks.

Non-terrestrial network (NTN)-based integrated communication and computation empowers various emerging applications with global coverage. Yet this vision is severely challenged by the energy issue given the limited energy supply of NTN nodes and the energy-consuming nature of communication and computation. In this paper, we investigate the energy-efficient integrated communication and computation for the ground node data through a NTN, incorporating an unmanned aerial vehicle (UAV) and a satellite. We jointly consider ground data offloading to the UAV, edge processing on the UAV, and the forwarding of results from UAV to satellite, where we particularly address the uncertainties of the UAV-satellite links due to the large distance and high dynamics therein. Accordingly, we propose to minimize the weighted energy consumption due to data offloading, UAV computation, UAV transmission, and UAV propulsion, in the presence of angular uncertainties under Gaussian distribution within the UAV-satellite channels. The formulated problem with probabilistic constraints due to uncertainties is converted into a deterministic form by exploiting the Bernstein-type inequality, which is then solved using a block coordinate descent framework with algorithm design. Simulation results are provided to demonstrate the performance superiority of our proposal in terms of energy sustainability, along with the robustness against uncertain non-terrestrial environments.

Users’ connection is pivotal for advancing meta-computing in Industrial Internet of Things (IIoT), where efficient data transmission can help overcome the barriers of computing resources distributed among billions of devices from diverse IIoT networks. Cooperative device-to-device (D2D) transmission, which is underlaid with cellular networks, plays an important role in enhancing the users’ connections for IIoT. However, since serving as a content provider in cooperative transmission needs consuming resources, IoT devices are typically willing to participate in cooperative transmission only when the content requesters are within the same IIoT networks. To encourage the cooperation across different IIoT networks, we in this article introduce social credit as a universal virtual concurrency to stimulate users’ willingness to assist content requesters via D2D transmission. Specifically, we first establish the relationship between social credit and data transmission, and model the process of credit acquisition and expenditure using a queue, which is initially unstable. Then, the inversely queuing technique is adopted to construct an equivalent stable queue, and a statistical credit guarantee mechanism is proposed to maintain cooperative transmission. Based on this mechanism, we explore the throughput maximization problem for cooperative D2D transmission in IIoT underlaying cellular networks, considering constraints on average and peak transmit power as well as interference to cellular communications, for obtaining the optimal credit-aware power control scheme. When multiple content providers are available, we prioritize the provider located at the shortest distance and examine the corresponding credit-aware power minimization problem, where the optimal power control scheme is obtained. Simulation results demonstrate that the proposal offers more stable credit guarantees than baseline methods, encouraging greater participation in cooperative transmission while achieving higher throughput and reducing transmit power consumption.

This paper proposes a novel algorithm, named a quasi-neural network-based decoder (QNN-decoder), for a single-carrier communication system. The algorithm is designed for an inter-symbol-interference (ISI) channel that a trellis diagram can model. According to the trellis diagram, a quasi-neural network (QNN) is built to acquire the likelihoods of the received samples enabling the subsequent decoding. The QNN-decoder differs from artificial neural network (ANN) based algorithms, such as the online learning trellis diagram (OLTD), as it doesn’t rely on data but instead utilizes the physical system model. This means the QNN-decoder can use a much shorter pilot to train its network via backpropagation than OLTD. Meanwhile, the QNN-decoder doesn’t require explicit channel state information (CSI) or statistics of interference and noise. Instead, it can efficiently suppress non-Gaussian interference by learning the CSI and interference and noise statistics. Simulation results verify the QNN-decoder outperforms the state-of-the-art methods and approaches the performance limits provided by the conventional Viterbi with perfect CSI in Gaussian noise only. The QNN-detector outperforms the conventional Viterbi and OLTD with non-Gaussian interference. A few redundant nodes make the QNN-decoder robust to channel length uncertainty, and it may be easily extended to a multi-antenna system for even greater interference suppression.

In frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) communication systems, the base station (BS) requires channel state information (CSI) reported from user equipment (UE) for downlink precoding, which brings in significant feedback overhead. In this paper, we propose a position-based precoding method with sensing assistance to realize MIMO downlink transmission without CSI feedback from UE. By exploiting the location of UE and the information of the propagation environment provided by wireless sensing techniques, the BS is able to derive the precoder for downlink transmission. To achieve this, we devise a novel neural network (NN) structure called PS-Net (Position-based-precoding with Sensing-assistance Network), which includes an environmental feature extractor, a weight generation module, an adaptive position encoder, and a position-to-precoder mapper. Using the PS-Net, information about the scatters in the propagation environment can be extracted and fused with the UE’s location to realize position-based precoding for time-varying channels. We also propose a dedicated data augmentation method called random phase shifting to enhance the training data diversity, thus improving the generalization ability of PS-Net. Simulation results demonstrate the superiority of the proposed PS-Net compared with the existing feedback-based solutions and other position-based approaches in terms of spectral efficiency and communication overhead.

While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rather challenging. The conventional approaches extended from optimization perspectives are usually quite involved, especially when jointly considering factors in different scales such as deployment and transmission in UAV-related scenarios. In this paper, we address the UAV-enabled multi-user secure communications by proposing a deep graph reinforcement learning framework. Specifically, we reinterpret the security beamforming as a graph neural network (GNN) learning task, where mutual interference among users is managed through the message-passing mechanism. Then, the UAV deployment is obtained through soft actor-critic reinforcement learning, where the GNN-based security beamforming is exploited to guide the deployment strategy update. Simulation results demonstrate that the proposed approach achieves near-optimal security performance and significantly enhances the efficiency of strategy determination. Moreover, the deep graph reinforcement learning framework offers a scalable solution, adaptable to various network scenarios and configurations, establishing a robust basis for information security in UAV-enabled communications.

Over-the-air computation (AirComp) has emerged as a promising technology that enables simultaneous transmission and computation through wireless channels. In this paper, we investigate the networked AirComp in multiple clusters allowing diversified data computation, which is yet challenged by the transceiver coordination and interference management therein. Particularly, we aim to maximize the multi-cluster weighted-sum AirComp rate, where the transmission scalar as well as receive beamforming are jointly investigated while addressing the interference issue. From an optimization perspective, we decompose the formulated problem and adopt the alternating optimization technique with an iterative process to approximate the solution. Then, we reinterpret the iterations through the principle of algorithm unfolding, where the channel condition and mutual interference in the AirComp network constitute an underlying graph. Accordingly, the proposed unfolding architecture learns the weights parameterized by graph neural networks, which is trained through stochastic gradient descent approach. Simulation results show that our proposals outperform the conventional schemes, and the proposed unfolded graph learning substantially alleviates the interference and achieves superior computation performance, with strong and efficient adaptation to the dynamic and scalable networks.

Age of information (AoI) is an effective measure to quantify the information freshness in wireless status update systems. It has been further validated that the peak AoI has the potential to capture the core characteristics of the aging process, and thus the average peak AoI is widely used to evaluate the long-term performance of information freshness. However, the average peak AoI is a risk-insensitive metric and therefore may not be well suited for evaluating critical status update services. Motivated by this concern, and following the spirit of entropic value-at-risk (EVaR) in the field of risk analysis, in this paper we present a concept, termed Statistical AoI, for providing a unified framework to guarantee various requirements of risk-sensitive status-update services with the demand on the violation probability of the peak age. In particular, as the constraint on the violation probability of the peak age varies from loose to strict, the statistical AoI evolves from the average peak AoI to the maximum peak AoI. We then investigate the statistical AoI minimization problem for status updates over wireless fading channels. It is interesting to note that the corresponding optimal sampling scheme varies from step to constant functions of the channel power gain with the peak age violation probability from one to zero. We also address the maximum statistical AoI minimization problem for multi-source status updates with time division multiple access (TDMA), where longer transmission time can improve reliability but may also cause the larger age. By solving this problem, we derive the optimal transmission time allocation scheme. Numerical results show that our proposals can better satisfy the diverse requirements of various risk-sensitive status update services, and demonstrate the great potential of improving information freshness compared to baseline approaches.

Reconfigurable intelligent surface (RIS) technologies have drawn significant attention in wireless communications networks. However, the accurate channel estimation of separated reflecting channels in RIS-assisted multi-input multi-output (MIMO) systems is very important, though crucially challenging due to the complex structure of channels and the large number of RIS reflective elements with passive reflective nature. In this paper, we propose a Three-point Cooperative Sensing based Separated Reflecting Channel Estimation (3PC-SRCE) scheme, which is based on the Pilot Reconstruction based Separated Reflecting Channel Estimation (PR-SRCE) scheme and introduces a multi-antenna auxiliary to assist the channel estimation process. The proposed 3PC-SRCE scheme is a three-stage channel estimation scheme, which overcomes the limitation of ambiguities in the existing channel estimation schemes for separated channels. It is able to obtain the full channel state information (CSI) with the assistance of the auxiliary, including the direction and amplitude information of each channel. Simulation results demonstrate the effectiveness of the proposed PR-SRCE scheme and 3PC-SRCE scheme in estimating the CSI of separated reflecting channels.

Status update applications are very common in the internet of drones (IoD), where some of status update information are privacy-sensitive. How these information can be efficiently and securely transmitted remains as an open challenging issue. Motivated by this concern, we in this paper consider using the transmit antenna selection (TAS) technique to guarantee secure status updates over IoD downlink networks, in which only the drones corresponding to the highest channel gain on each antenna can become the candidate receivers, and then the base station (BS) selects one antenna to transmit the associated status update information with wiretap coding to the corresponding candidate. The mobility of drones causes the channel gain is time-varying, and thus one natural question arises, i.e., what is the optimal antenna selection scheme in terms of the long-term network-wide freshness performance. To answer this question, the weighted sum of average age-of-information (AoI) minimization problem is formulated, and we propose a deep Q-learning-based antenna selection approach to solve it. It is worth mentioning that, in light of the try-error mechanism of Q-learning, no prior knowledge of the wireless environments is required for our proposal in contrast to the traditional schemes. Finally, the numerical results verify that our proposal can further reduce the weighted sum of AoI as compared with the state-of-the-art max-weight scheme.

This paper proposes a Quasi-neural network-based detection algorithm, namely QNN-detector, for a single-carrier communication system in an inter-symbol interference (ISI) channel, which can be modeled by a trellis diagram. According to the trellis diagram, a quasi-neural network (QNN) is built to acquire the normalized likelihoods to enable the subsequent detection or decoding. The QNN can accommodate non-Gaussian interferences through ingeniously designing its hidden layers. Unlike the artificial neural network (ANN) based algorithms, which are data-driven, the QNN-detector relies on the physical system model and only needs a short pilot sequence for training. Moreover, it requires neither explicit channel state information (CSI) nor statistics of interference and noise. Simulation results illustrate that in a channel under white Gaussian noise, the QNN-detector significantly outperforms the ANN-based algorithms in that its network training requires a far shorter pilot sequence, and can approach the performance limits provided by the Viterbi detector with perfect CSI. The simulations also show that in the presence of non-Gaussian interferences, the QNN-detector can learn the distribution of the interferences and therefore suppress them effectively, while the conventional Viterbi detector fails to.

With the emergence of the sixth generation (6G) era, ubiquitous sensing and machine intelligence have emerged as the primary motivation for the development of large-scale Internet of Things (IoT) systems. However, the limited communication capabilities of IoT devices are often constrained by costs and further inhibit their full potential. Nowadays sensor technologies are rapidly evolving, offering improved accuracy and update rates for environmental perception. Considering this, the challenge faced by 6G IoT lies in the increasing data payload from sensing and the constrained communication capacity. To this end, lite communication solutions for IoT are investigated in this paper. By integrating task-oriented communication concepts with sensing objectives, we extract and represent semantic information from sensor nodes. Subsequently, this information is transmitted to edge nodes via wireless transmission for analysis and processing of sensory data. This approach effectively reduces redundant data transmission, thus achieving lite communication in IoT. Based on these principles, a lite edge task-oriented communication system architecture tailored for IoT applications is proposed. Specifically, we implement and evaluate the IoT communication module and the edge task-oriented module, demonstrating that the proposed system reduces resource consumption by 99 % and significantly alleviates the transmission payload of IoT. Through leveraging low-speed IoT transmission protocols and high-precision sensors, our system effectively supports a wide range of sensing tasks.

Metaverse is envisioned to shape a virtual digital world accommodating people to live, work, and interact with each other, which requires massive information exchange for frequent updates of panoramic information in the digital world and imposes unprecedented pressure on future networks, so it is desirable to only sample the information updating process covering objects of user’s main attention. Yet under the always-limited wireless capacity compared to the persistently-growing information load, a critically important but unanswered question remains, i.e., how often shall we sample information updating process and deliver it to users, while keeping information at users’ side as fresh as possible. To answer this question, we investigate the statistical age-of-information (AoI) minimization problems to catch varying wireless channels and attention of users. Unlike conventional average or maximum AoI optimization technologies, we concentrate on statistical feature of AoI to more accurately characterize the capability of supporting metaverse applications. The formulated problems are solved by fractional programming. Specifically, using the Dinkelbach’s and quadratic transforms, we derive the adaptive sampling and transmission schemes for cases with single and multiple users, respectively. The interaction among multiple users is also considered. Analyses reveal that the optimized sampling rate shall decrease as the information updating process covers more varying objects or the channel gets poorer. Moreover, when the AoI requirement becomes extremely stringent, the sampling rate approaches a constant. Numerical results validate that our proposals can achieve lower statistical AoI than baseline schemes, thus offering better experiences for metaverse users.

Due to the openness of wireless medium and the public structure of pilot signals, wireless sensing procedure is vulnerable to eavesdropping, which causes privacy concerns. In this paper, a novel physical layer obfuscation solution termed as multi-antenna signal masking is proposed to realize privacy-preserving sensing. The privacy protection is realized via controlling the phase difference between the sensing signals of different antennas. Considering the fact that the channel state information (CSI) variation caused by changes of the physical environment typically slowly varies with time, the phase difference is designed as a slowly-varying function with temporal-correlation such that the real variation pattern in CSI is masked and the eavesdropper is thus unable to perform sensing based on the measured CSI. Furthermore, we also devise a round-trip transmission method to avoid secret information exchange between legitimate users, hence realizing privacy-protection without additional overhead. Simulation results demonstrate the superiority of the proposed method in terms of sensing accuracy and privacy-protection capability compared with existing works.

This paper considers a wireless federated learning (FL) system, where the parameters of neural networks (NNs) from distributed users are transmitted to the base station (BS) periodically via wireless links for global aggregation. Due to random fading, users experiencing deteriorated channel conditions are unable to upload their NN parameters successfully, which lowers the convergence rate and degrades the accuracy of the NN model. In order to mitigate the influence of channel fading and accelerate convergence, we propose UC-FL, a user cooperation framework for wireless FL. Unlike the traditional FL paradigm where only “vertical” connections (i.e., users-to-BS) are supported, in the UC-FL framework, “horizontal” connections (i.e., users-to-users) are also introduced to enable user cooperation. In this manner, users with good channel conditions help those experiencing deep fading channels to upload their NN parameters, which provides more opportunities for distributed users to participate in global aggregation. Moreover, a novel global aggregation weight design is proposed by taking into account the channel conditions, to further improve the performance. Simulation results demonstrate the superiority of the proposed UC-FL compared with the classic FedAvg counterpart in terms of model accuracy and convergence rate.

Polar codes can be utilized to enhance physical layer security (PLS) over wiretap channels. Most of the polar-coding based PLS schemes require the channel state information (CSI) of the main channel and that of the eavesdropper’s channel. However, the CSI of the eavesdropper’s channel is generally unavailable in practice. In this paper, we propose a differentially pre-coded polar coding (DPPC) scheme for systems where the CSI of the eavesdropper’s channel is unavailable. The main idea of DPPC is to add a differential pre-coding operation prior to polar coding. In classical polar coding scheme, a legitimate transmitter (Alice) transmits K information bits to a legitimate receiver (Bob), where K is the number of good sub-channels of the main channel. For Bob, all bits transmitted on good sub-channels can be decoded correctly. On the other hand, some of the K information bits lie in the bad sub-channels of the eavesdropper (Eve) and thus cannot be decoded correctly by Eve, given the assumption that the eavesdropper’s channel is degraded compared to the main channel. The pre-coding operation introduced in the DPPC scheme XORs a "good" information bit (i.e., the bit that will be decoded correctly by Eve) with a "bad" information bit (i.e., the bit that will not be decoded correctly by Eve), yielding an error propagation at Eve after the inverse operation of the pre-coding. This results in a high BER at Eve. Meanwhile, the error performance of Bob remains unchanged because the pre-coding operation is applied to the K information bits, all of which will be received correctly with polar coding. Simulation results demonstrate that the proposed scheme can achieve transmission reliability and security simultaneously. Moreover, our scheme outperforms the existing candidates in terms of transmission reliability in the high-SNR regime.

Physical layer encryption is an emerging security paradigm which supplements the higher-layer encryption solutions. To realize physical layer encryption, channel measurement is typically utilized as a randomness source to generate secret keys. Due to non-perfect reciprocity of the channels and the inevitable channel estimation errors resulting from noises and interferences, the channel measurements at the legitimate transceivers are not the same, yielding a mismatch in the generated keys. To address this issue, a distributed physical layer key generation scheme based on deep learning is proposed in this paper. A channel state information (CSI) learning neural network (CLNet) based on the autoencoder is deployed at both the transmitter and the receiver side to refine the initial channel estimation results. The CLNet takes the least square (LS) channel estimations as input and outputs the channel estimations more similar to the real channel. To train this CLNet, a channel equalization module together with a pre-trained maximum-likelihood (ML) detection network is deployed, which is used to make a decision on the transmitted pilot signal. Since the pilot is known as a priori, the cross entropy loss between the known pilot and the recovered one can be calculated, which implicitly indicates the accuracy of the refined CSI. Further, the weighted sum of the aforementioned cross entropy loss and the mean square loss between the original and the refined CSI estimates is utilized for training, which not only improves the CSI estimation quality but also avoids the over-fitting effect. Extensive simulation results show that compared with benchmark schemes, the proposed scheme improves the key agreement rate and the key generation rate at the legitimate users. Moreover, our method ensures a higher key disagreement rate between the legitimate user and the eavesdropper as well, especially in the low signal-to-noise ratio (SNR) regime.

Inspired by Dirty Paper Coding (DPC), in this paper we propose a design approach of transmit beamforming for the integrated sensing and communications (ISAC) system that offers simultaneous sensing services via radar and information delivery to users. Specifically, we shape the channel matrix into an equivalent lower triangular matrix. Under this strategy, the communicating users are able to eliminate the multi-user interference (MUI) by using successive interference cancellation (SIC). The corresponding transmit beamforming optimization problem subject to radar constraints is formulated and then solved by a manifold optimization approach. Compared to the conventional design, the idea behind our proposal is to reduce partial burden of beamforming towards interference suppression while taking advantage of low-complexity detection at the receiver to achieve better performances. The simulation results show that our proposed scheme has lower BER than the conventional scheme at low-SNR regime. As for sensing function, the beam pattern of proposed scheme has only a small performance loss compared with the desired beam pattern.

Physical layer security (PLS) of wireless communications is becoming increasingly significant with the advent of 6G. The reconciliation of channel-based secret key generation (SKG) methods is necessary to avoid inconsistencies after key generation, resulting in increased communication overhead, leakage of key information, and reduced security performance. This paper proposes a fuzzy secret key generation (FSKG) method that does not require the legitimate user to perform a consistency check after fuzzy extraction of the phase, which is used directly to encrypt the constellation rotation of the key. The proposed scheme allows users to code-decode the key bits to reduce the transmission error probability and improve the key consistency, and the simulation results prove that the proposed FSKG scheme outperforms traditional SKG schemes like CQA and CQG. The simulation results also indicate that the best secure key rate can be achieved by applying PSK modulation.

Reconfigurable intelligent surface (RIS) technologies can effectively extend the coverage of MIMO systems with improved signal-to-interference-and-noise ratio (SINR). However, the channel estimation of RIS-assisted MIMO systems is very challenging, though crucially important. First, the passive reflective nature of RIS makes it hard to obtain channel state information (CSI) of the transmitter-to-RIS and RIS-to-receiver links, respectively. Second, limited by the number of transmit antennas, reflective elements typically need to be divided into groups, and RIS needs to turn on/off element groups successively to obtain the full CSI, causing extra overhead and difficulty in control. To tackle these problems, we first propose a Two-fold-SVD based Separated Reflecting Channel Estimation (2-SVD-SRCE) scheme, which based on pilot blocks can effectively extract transmitter-to-RIS and RIS-to-receiver links’ CSI, respectively. Then, we propose a novel pilot reconstruction design at the transmitter and develop a Pilot Reconstruction based Separated Reflecting Channel Estimation (PR-SRCE) scheme, which can overcome the limitation that the number of reflective elements in each group cannot exceed transmit antennas. Analyses and simulations show that our proposed scheme can significantly simplify the RIS control procedures, while achieving good performances in both complexity and channel estimating accuracy.

As drones become increasingly prevalent in human life, they also raise security concerns such as unauthorized access and control, as well as collisions and interference with manned aircraft. Therefore, ensuring the ability to accurately detect and identify between different drones holds significant implications for coverage extension. Assisted by machine learning, radio frequency (RF) detection can recognize the type and flight mode of drones based on the sampled drone signals. In this paper, we first utilize Short-Time Fourier Transform (STFT) to extract two-dimensional features from the raw signals, which contain both time-domain and frequency-domain information. Then, we employ a Convolutional Neural Network (CNN) built with ResNet structure to achieve multi-class classifications. Our experimental results show that the proposed ResNet-STFT can achieve higher accuracy and faster convergence on the extended dataset. Additionally, it exhibits balanced performance compared to other baselines on the raw dataset.

Orthogonal Time-Frequency-Space (OTFS) modulation has demonstrated superior performances to extend coverage for high-mobility communicatoins. In this paper, we propose a novel OTFS scheme featuring interference self-cancellation achievable with nearly linear complexity. We first apply the repetitive precoding in delay-Doppler domain, and performing negative padding in the time-frequency domain. Additionally, we formulate equivalent coefficients for the TF domain channel to obtain combined gain with point division equalization. Through numerical simulation, compared with the unprocessed OTFS system, the proposed scheme can achieve a significant performance improvement under TF domain point equalization. Finally, through the analysis of computational complexity, our proposed scheme can realize the transceivers design and equalization with approximately linear complexity.

Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this article, we consider the anti-eavesdropping communication established through an RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst case security provisioning as compared with the baselines.

Grant-Free (GF) access has been recognized as a promising candidate for Ultra-Reliable and Low-Latency Communications (URLLC). However, even with GF access, URLLC still may not effectively gain high reliability and millimeter-level latency, simultaneously. This is because the network load is typically time-varying and not known to the base station (BS), and thus, the resource allocated for GF access cannot well adapt to variations of the network load, resulting in low resource utilization efficiency under light network load and leading to severe collisions under heavy network load. To tackle this problem, we propose a multi-tier-driven computing framework and the associated algorithms for URLLC to support users with different QoS requirements. Especially, we concentrate on$K $- repetition GF access in light of its simplicity and well-balanced performance for practical systems. In particular, our framework consists of three tiers of computation, namely network-load learning, network-load prediction, and adaptive resource allocation. In the first tier, the BS can learn the network-load information from the states (success, collision, and idle) of random-access resources in terms of resource blocks (RB) and time slots. In the second tier, the network-load variation is effectively predicted based on estimation results from the first tier. Finally, in the third tier, by deriving and weighing the failure probabilities of different groups of users, their QoS requirements, and the predicted network loads, the BS is able to dynamically allocate sufficient resources accommodating the varying network loads. Simulation results show that our proposed approach can estimate the network load more accurately compared with the baseline schemes. Moreover, with the assistance of network-load prediction, our adaptive resource allocation offers an effective way to enhance the QoS for different URLLC services, simultaneously.

The fifth generation network offers a powerful infrastructure for Industrial internet of things (IoT) in that its support on massive machine-type communications (mMTC). Recently, grant-free access has been recognized as the promising new access approach for mMTC; however, it also introduces the potential risk. To timely discover intrusion, in this article, we propose a learning network by extracting traffic load information from the states (success, collision, and idle) of access resources observed at media access control and physical layers. In particular, our learning network consists of three concatenated function modules, i.e., traffic load estimation, traffic load prediction, and intrusion identification. Moreover, our proposed learning network is able to identify two types of intrusion, i.e., false data dissemination and congestion attack. Simulation results indicate that the proposed scheme can effectively capture the number of active devices, provide reasonable prediction by using history records, and eventually, achieve more accurate detection compared with baseline approaches.

With the ubiquitous connectivity in internet of vehicles, secure transmission is of vital importance for intelligent vehicle-to-everything (V2X) communications. However, in order to effectively guarantee the secrecy performance, once the short-term statistics of channel quality are low, which frequently happens because of the high mobility of vehicles, the throughput performance can be weakened by using the existing either cryptography techniques or physical-layer security (PLS) technique. To improve the throughput of secure data transmission, we propose the concept of statistical security, which utilizes the time-sensitive characteristics of information, such as the position of mobile vehicles, in V2X communications. Specifically, the time-sensitive information has its own outdated rate. As long as the information leakage rate is less than the information outdated rate, the eavesdropper still cannot obtain any useful information. Then, we use a queue system to characterize the eavesdropping process, where the queue-length violation probability indicates the level of secure transmission. Under this statistical security model, we investigate the throughput maximization problem and give the optimal power allocation scheme for the legitimate vehicular users. Furthermore, since the service’s security QoS requirements and wireless environments are both varying in V2X communications, we propose a unified cross-layer policy-adaption approach to guarantee the varying security QoS requirements meanwhile improving the throughput. Simulation results verify that our proposed approach significantly outperforms the existing baseline schemes.

In this paper, we studies secure wireless transmission using polar codes which based on self-coupling encryption for relay-wiretap channel. The coding scheme proposed in this paper divide the confidential message into two parts, one part used to generate key through a specific extension method, and then use key to perform coupling encryption processing on another part of the confidential message to obtain the ciphertext. The ciphertext is transmitted in the split-channels which are good for relay node, legitimate receiver and eavesdropper at the same time. Legitimate receiver can restore key with the assistance of relay node, and then uses the joint successive cancellation decoding algorithm to restore confidential message. Even if eavesdropper can correctly decode the ciphertext, he still cannot restore the confidential message due to the lack of key. Simulation results show that compared with the previous work, our coding scheme can increase the average code rate to some extent on the premise of ensuring the reliability and security of transmission.

This paper studies machine learning-assisted optimal receivers for a communication system with memory, which can be modelled by a trellis diagram. The prerequisite of the optimal receiver is to obtain the likelihoods of the received samples under different state transitions. We propose to learn the trellis diagram real-time using an artificial neural network (ANN) trained by a pilot sequence. This approach, termed as the online learning trellis diagram (OLTD), requires neither the channel state information (CSI) nor statistics of the noise, and can be incorporated into the classic Viterbi and the BCJR algorithm. In a channel with non-Gaussian noise, the OLTD method can significantly outperform the model-based methods that use Gaussian assumption. It requires much less training overhead than the state-of-the-art ANN-assisted methods. As an illustrative example, the OLTD-based BCJR is applied to a Bluetooth low energy (BLE) receiver trained only by a 256-sample pilot sequence. Moreover, the OLTD-based BCJR can accommodate for turbo equalization. As an interesting by-product, we propose an enhancement to the BLE standard by introducing a bit interleaver to its physical layer; the resultant improvement of the receiver sensitivity can make it a better fit for some Internet of Things (IoT) communications.

Massive multiple-input-multiple-output (MIMO) systems provide high spatial resolution of the antenna array and the angle reciprocity of the massive MIMO channel holds in both time division duplex (TDD) and frequency division duplex (FDD) systems. In this paper, we propose a new secure strategy towards transmissions in massive MIMO systems. Each coherent time is divided into two stages. The angle signature of the uplink (UL) channel is estimated in the first stage and the downlink (DL) angle signature can be obtained using angle reciprocity. In the second stage, the angle signature of the DL channel is adjusted by spatial rotation according to the data to be transformed. Using the independent distribution of angle signatures of different channels, security performance can be enhanced.

In this paper, we propose an edge caching scheme in vehicular networks, which allows multiple roadside units (RSUs) to collaboratively cache part of content in a distributed way and delivery coded packets by using fountain codes. The overlapping placement algorithm is proposed to increase the correlation of data packets cached in RSUs. When distributing content, the unequal probability selection algorithm of generating fountain codes is proposed to decrease the decoding overhead. The simulation results prove the scheme above that the transmission efficiency and the successful content acquisition rate can be improved than the traditional coded caching when the caching memory is limited.

In Ultra-Reliable and Low-Latency Communications (URLLC), K-repetition Grant-Free (GF) access can effectively lower the latency by avoiding the complicated hand-shake procedures. However, it can hardly achieve both high reliability and millimeter-level latency simultaneously if collisions across users frequently happen. In fact, the collision level mainly replies on whether there are sufficient resources for URLLC compared with the network-load, i.e., the number of active users, which, however, is typically not known by the base station (BS). To solve this problem, we propose the effective network-load estimation schemes for URLLC. In particular, based on access states (success, collision, or empty) of resource blocks across consecutive access slots in a subframe, we derive the multi-slot maximum-likelihood (ML) and single-slot least-squares (LS) estimation schemes. Benefitted from the obtained estimation, we further design the adaptive resource allocation scheme for URLLC. Simulation results corroborate that by exploiting the correlation across multiple access slots, our proposal can achieve more accurate estimation than the baseline scheme. Correspondingly, our adaptive resource allocation schedule offers a way to significantly enhance the delay QoS while assuming reliability at only small cost of a slight increment of total access resources.

As the theory that passive RFID tags placed closely can communicate with each other was verified, tag-to-tag communications and networking have become a popular topic. We need to achieve communication between long-distance tags at most time, so this paper studies a multi-hop tag-to-tag network based on turbo backscattering operation (TBO). End-to-end bit error rate is as the selection standard through the design of the data frame structure of reader and passive tags. The reader chooses the best transmission route and then brodcasts the routing information including tags' IDs. The tags on the selective path are then activated and uses cooperative network coding to transmit information. Moreover,$\mu \text{code}$encoding algorithm is used to reduce the interferences between the reflected signals. According to the requirements of different application scenarios, we analyze and adjust our proposed routing. The simulation results show that proposed routing scheme reduces bit error rate (BER) and transmission delay, in addition, improves throughput and channel utilization.

With the advent of 5G, cyber-physical systems (CPSs) employed in the vertical industries and critical infrastructures will depend on the cellular network more than ever; making their attack surface wider. Hence, guarding the network against cyberattacks is critical not only for its primary subscribers but to prevent it from being exploited as a proxy to attack CPSs. In this article, we propose a consolidated framework, by utilizing deep convolutional neural networks (CNNs) and real network data, to provide early detection for distributed denial-of-service (DDoS) attacks orchestrated by a botnet that controls malicious devices. These puppet devices individually perform silent call, signaling, SMS spamming, or a blend of these attacks targeting call, Internet, SMS, or a blend of these services, respectively, to cause a collective DDoS attack in a cell that can disrupt CPSs' operations. Our results demonstrate that our framework can achieve higher than $91\%$ normal and underattack cell detection accuracy.

Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. State-of-the-art literature applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, in this article we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that preprocesses raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of Things environment.

The relay networks face with such a circumstance that an eavesdropper overhears every hop of the relay networks. To solve the issue, we propose a full-duplex friendly-jamming relaying (FDFJR) scheme to secure the information transmissions for a two hop relay network, where a malicious eavesdropper attempts to overhear the private messages transferred by the transmitter and the relay. In the scheme, we take advantage of the capacity of simultaneous reception and transmission of fullduplex (FD) relay. Specifically, in the first hop, the full-duplex jamming relay (FDJR) receives the private information from the transmitter while transmitting the jamming signals to the malicious eavesdropper. And in the second hop, FDJR continues transferring the jamming signals to the eavesdropper, rather than merely amplifying and forwarding the information signals previously received. Furthermore, the ergodic secrecy capacity (ESC) of the networks is characterized and its asymptotical expression is derived. To maximize ESC, we formulate a novel power scheduling problem satisfying several key power and rate requirements. By solving the problem, an exact analytic solution regarding power allocation factor is acquired. Finally, numerical results are provided to validate the accuracy of analytical results and the superiority of our proposed scheme.

This paper investigates the physical layer security of non-orthogonal multiple access (NOMA) based cooperative device-to-device (D2D) communications in cellular networks. In the networks, D2D transmitter acts as a relay of cellular networks while transmitting its own information over the spectrum of cellular networks with NOMA technique. The information transmitted by D2D transmitter is overheard by an external eavesdropper. To prevent eavesdropping, a joint design of jamming and beamforming is performed from an aspect of the power control. Particularly, the full-duplex (FD) receiver of cellular networks emits the jamming signals to deteriorate the eavesdropper's channel while receiving the confidential signals from D2D transmitter. Beamforming is designed to protect the legitimate receivers against the jamming signals. Furthermore, the secrecy outage probability of the system (SOPS) is characterized and its closed-form expression is derived. Finally, numerical results are employed to validate the accuracy of the analytical results and the superiority of the proposed scheme in terms of security performance.

In this paper, we investigate the power-efficient resource allocation strategy with Quality-of Service (QoS) provisioning in uplink ultra- reliable and low-latency communication (URLLC) networks. By adopting the finite-blocklength information theory, the QoS requirement is described for uplink URLLC transmissions. Then, we formulate an optimization problem concerning joint bandwidth assignment, subchannel allocation and transmit power control, which aims at minimizing the required total transmit power consumption with QoS provisioning. To solve this non-convex optimization problem, we design a traffic-aware resource allocation scheme, including the adaptive bandwidth assignment strategy based on the traffic load information and the joint subchannel allocation and transmit power control strategy based on nearest-neighbor searching. What's more, the impact of spatial diversity on the QoS provisioning and the power consumption are also analyzed. Simulation results demonstrate that our proposed resource allocation scheme can achieve better performance as compared to the conventional schemes.

This paper proposes a proactive cache-replacing scheme in mobile-edge-computing (MEC) -enabled mobile wireless networks, which targets at decreasing the caching costs when offering convenient data access for mobile users. The main features of our scheme, termed Cost-Aware Variable-Size Caching (CAVSC), include three folds: 1) joint consideration of content sizes and user-access frequencies in characterizing the content popularity; 2) adaptive content clustering to ease the popularity prediction; 3) the size-aware cache-replacing strategy. In particular, our CAVSC scheme regularly performs adaptive clustering, which dynamically categories the data contents into classes according to user-access frequencies. Every time upon the receipt of a new user's request, our CAVSC scheme predicts the popularity as the weighted product of the access frequency and size of the corresponding data contents, where the weights reflect the accuracy of our preceding prediction. Then, our scheme conducts cache replacement by jointly considering the updated popularity as well as sizes of contents. Simulations evaluations show that our CAVSC scheme outperforms the baseline schemes in terms of caching cost.

5G is envisioned to have an artificial intelligence (AI)-empowerment to efficiently plan, manage and optimize the extremely complex network by leveraging colossal amount of data (big data) generated at different levels of the network architecture. Cell outages and congestion pose serious threat to the network management. Sleeping cell is a special case of cell outage in which the cell provides inferior services to its users. This peculiar behavior of the cell is particularly challenging to detect as it disguises itself from the network monitoring entity. Inadequate accuracy and high false alarms are two major constraints of state-of-the-art approaches for the anomaly-sleeping cell and surge in user traffic activity that may lead to congestion-detection in cellular networks. This implies squandering of scarce resources which ultimately results in increased operational expenditure (OPEX) while disrupting network's quality of service (QoS) and user's quality of experience (QoE). Inspired from the prominent success of deep learning (DL) technology in machine learning domain, this is the first study that applies DL for the detection of abovementioned anomalies. We utilized, and did a comprehensive study of, L-layer deep feedforward neural network fueled by real call detail record (CDR) dataset (big data) and achieved 94.6% accuracy with 1.7% false positive rate (FPR), that are remarkable improvements and overcome the limitations of the previous studies. The preliminary results elucidate the feasibility and preeminence of our proposed anomaly detection framework.

In order to secure the primary privacy information and provide quality-of-service provisioning for the secondary system, we propose a secondary encryption secure transmission scheme. In the proposed scheme, the primary system utilizes the secure secondary messages to encrypt the primary confidential messages and the secondary system can acquire some spectrum opportunities. Specifically, when the primary system is secure, the primary information can be directly transmitted; when the primary system is insecure while the secondary messages can be securely transmitted, the primary system utilizes the secure secondary messages to encrypt the primary information; otherwise, the spectrum will be utilized for secondary transmission. For the proposed scheme, we investigate the performances of the primary ergodic secrecy rate and the average secondary throughput. Numerical results have demonstrated that the secondary encryption secure transmission scheme can secure the primary privacy messages and improve the secondary transmission throughput.

Physical layer security is a promising technology for secure communication by using wiretap encoding or physical-layer secret key generation. Most of the existing research works study these two techniques separately, which however cannot fully utilize the radio resource. In this paper, we combine the two techniques by using a channel-aware threshold-based approach. Specifically, when the estimated channel gain is larger than the threshold, the wiretap encoding is adopted; otherwise, the secret key generation is performed. For given constraints on security and reliability, we respectively derive the effective secrecy rate of the two techniques and then obtain the secrecy throughput of the proposed scheme through simulations. Numerical results show that the proposed scheme can effectively combine the two techniques and enhance the average secrecy throughput.

The Internet of Things (IoT) is expected to accommodate every object which exists in this world or likely to exist in the near future. The enormous scale of the objects is challenged by big security concerns, especially for common information dissemination via multicast services, where the reliability assurance for multiple multicast users at the cost of increasing redundancy and/or retransmissions also benefits eavesdroppers in successfully decoding the overheard signals. The objective of this work is to address the security challenge present in IoT multicast applications. Specifically, with the presence of the eavesdropper, an adaptive fountain code design is proposed in this paper to enhance the security for multicast in IoT. The main novel features of the proposed scheme include two folds: (i) dynamical encoding scheme which can effectively decrease intercept probability at the eavesdropper; (ii) increasing the transmission efficiency compared with the conventional nondynamical design. The analysis and simulation results show that the proposed scheme can effectively enhance information security while achieving higher transmission efficiency with a little accredited complexity, thus facilitating the secured wireless multicast transmissions over IoT.

The security of the interference alignment (IA)- based networks is of uttermost importance for the application of interference alignment in multi-user networks. Several recent works have utilize the physical layer security schemes including artificial noise (AN) and friendly jamming etc. to reduce the eavesdropping capabilities of an outside eavesdropper. In this paper, we propose a novel AN- based anti-eavesdropping scheme in IA-based networks where the AN and the interferences are aligned into two different subspaces at the desired receiver. This results in more confusion to the eavesdropper and an enhancement of the desired signal simultaneously. The simulations testify this observation.

Non-orthogonal multiple access (NOMA) is important in 5G, and the users served in NOMA are often paired to avoid the excessive interference. However, for a two-user network, if the channel condition of one user is serious, this network may require the other user to relay this user's signals. In this case, the demands of these users are possibly different. Specifically, the relay user may want the node to increase the spectral efficiency for compensating the cost of relay. On the other hand, because privacy information may be contained in signals, the indirect communication user may primarily focus on his or her information security. Therefore, we propose a novel physical layer scheme to satisfy these demands. Different from the existing relay schemes in NOMA, our scheme has the following characteristics: (i) through power allocation, the relay user can extract his or her signals with spectral efficiency improvement; (ii) through a signal-level method, the relay user can forward the indirect communication user's signals, but he or she is difficult to learn the privacy information in these signals; (iii) through a mechanism, the indirect communication user can attain his or her privacy information. Our scheme is able to support the relay user's demand, and this eavesdropping suppression in our scheme doe not depend on the complicated encryption techniques and positive secrecy rate.

Non-orthogonal multiple access (NOMA) is an important multiple access mode in 5G. Nevertheless, eavesdropping may appear between the users in NOMA. In this case, physical layer security schemes can be introduced to combat eavesdropping. Different from the existing schemes, eavesdropping suppression in NOMA should be based on several rules: (i) successive interference cancellation (SIC) should be normally operated; (ii) each user can not attain others' privacy information; (iii) each adopted scheme had better not to depend on the spatial disparity between channels (since the channels may have the strong spatial similarity). Therefore, we propose a novel scheme to adapt to these rules. In our scheme, the original signals of users are separately transformed into the transmitted signals by a well-designed angle conversion method, and the principles of these variations for diverse users are different. Furthermore, an auxiliary mechanism with full-duplex technology is devised to guarantee the users to safely learn the principles, respectively. Through this scheme, each user can deduce other users' transmitted signals for SIC, while the original signals are difficult to be determined from transmitted signals. Hence, our scheme can effectively improve security.

Pilot spoofing attack is a serious threat to timedivision duplex (TDD) orthogonal frequency division multiplexing (TDD-OFDM) system. By employing identical pilot tones as a legitimate receiver, an adversary can contaminate the uplink channel estimation between a transceiver pair. To solve this problem, we in this paper propose an independent component analysis (ICA) based channel estimation and identification mechanism with a subcarrier-block discriminating coding (SBDC) technique (ICA-SBDC). Firstly, a receiver randomizes the values of its pilot tones to avoid contamination, which however incurs pilot jamming attack. A minor-component-based detector (MCD) is devised to detect the attack efficiently. Secondly, the transmitter exploits the fourth-order statistical information of received signals to extract a linear-mixing channel. We can prove that given previously used legitimate pilots, both legitimate and attack sub-channels can be recovered from the obtained channel. Finally, the receiver maps its utilized pilots into various uplink transmission strategies on subcarrier-blocks which can be ultimately identified by the transmitter in a jamming environment. The mapping therein is formulated via a public-known codebook with discriminating algebraic property and the identification is achieved by decoding the codebook according to the results of MCD-based detection for each subcarrier-block. Simulation results verify the effectiveness of our proposed mechanism.

Device-to-device (D2D) relay network is regarded as a promising technology to meet the drastically increasing demands on local-based communication services. The relay devices on users with social behaviors will inevitably cause the negative effects on the stability of D2D communications. To improve the stability of the communication over mobile relays, we exploit users' social information in terms of contact duration to characterize the social stabilities of potential relays. Furthermore, with optimal stopping theory, we propose a joint social-physical relay re-selection scheme. This scheme takes into account the mobility of the currently selected relay as well as the social stability and physical conditions of potential relays. This can avoid the interruption of relayed communication and achieve the long-term increase of the relayed data traffic. Our scheme is shown to exhibit the stage-dependent policy structure that is adaptive for different mobility and social stability. This structure indicates that the relay re-selection scheme can achieve the tradeoff between the cost of relay probing and the amount of relayed data traffic. We conduct extensive simulations to demonstrate the superiority of our proposed scheme compared with other baseline schemes. The impact of social stability and mobility on the performance are revealed by our simulation results.

In this paper, we propose a cooperative secure transmission for two-hop relay network with limited feedback. In the proposed scheme, the channel state information associated with cooperative users is quantized and limited bits are feedback for cooperative relay and jammer selection. By considering the quantization error brought by limited feedback, we investigate the performances of transmission outage probability and secrecy outage probability, and their closed-form expressions are derived. On this basis, we optimal design the target transmission rate and secrecy rate so that the average secrecy rate is maximized under the constraints of maximum permitted transmission outage probability and secrecy outage probability requirements. Simulation results are presented to verify the analytical results for the proposed scheme and prove its secrecy performance improvement in terms of the secrecy performance with light overhead.

Eavesdropping in wireless communication environment should be suppressed. However, most existing schemes ordinarily focus on secrecy rate enhancement, which may not be achieved with the non-Gaussian signals. Therefore, we consider this security problem from the actual signal point of view. On the basis of this premise, a novel scheme is proposed. In our scheme, each original signal in one constellation is converted as a transmitted signal in another constellation, and the principle of this variation can be safely told to the user without being learned by others. With this conversion, the eavesdropper is difficult to restore the original signals. Performance analysis and simulation results illustrate that the proposed scheme is efficient for physical layer security improvement.

Conventional methods of codebook design in limited-feedback multi-antenna systems aim to quantize the single-user channel but without considering secrecy requirements. Thus, the information leakage is inevitably aggravated due to the eavesdropping behaviors in limited-feedback multiple-input single-output single-antenna- eavesdropper (MISOSE) systems. To reduce the information leakage without any extra cost in antenna resources and feedback overheads, the statistical distribution of the channel matrix of both the legitimate receiver and the eavesdropper needs to be jointly exploited. Accordingly, this paper studies the novel codebook design method by further utilizing the statistical relationship between channel direction vectors and codeword vectors. Particularly, we formulate a codeword update mechanism on the weighted Voronoi diagram (WVD) where weighted codeword vectors are iteratively updated for improving the non-zero secrecy rates. Ultimately, an implementing algorithm is devised to determine those codewords with both of the secrecy-rate gains and beamforming gains. Simulation results further validate the superiority of our proposed method over conventional single-user-oriented codebooks in the respect of both average secrecy rates and average rates.

D2D relaying strategy is conceived as an attractive enhancement technique as the complement for D2D communication which can extend the communication range and further improve the system performance. Considering users' selfishness, we establish a new D2D cooperate relaying paradigm by leveraging two social factor: social tie and reputation. Tie strength with the source decides how much power can be provided by relay users. The cooperation probability modeled by the combination of reputation and social tie reflects the user's forwarding willingness. Then we propose two social- aware relay schemes considering the impact of social tie and reputation. Simulations results show that there is a trade-off between the cooperation probability and transmission rate of the potential relay user, the proposed schemes can greatly improve the performance in different cases.

Relay transmission can expand the coverage area and improve the communication reliability. However, it may face higher eavesdropping risk due to the additional relay-destination retransmission, which provides eavesdroppers with a second chance to intercept the confidential message. To guarantee the communication secrecy, specially designed transmission strategies are needed. In this paper, we concentrate on the secrecy assurance of a relay network, which is surrounded by colluding eavesdroppers with unknown locations. Specifically, we aim to maximize the secrecy rate by jointly optimizing the power allocation and relay placement. First, we derive the exact expression of the secrecy outage probability. After imposing a constraint on the outage probability, we formulate a secrecy-rate- maximization problem, which is difficult to solve. By using an upper bound, we transform the original problem into a new one, the solution to which is also feasible for the original problem. We then obtain the optimal power allocation between the source and the relay and find the relay's best location to maximize the secrecy rate. It is noted that the derived optimal power allocation is independent of the instantaneous channel state information (CSI), which avoids the frequent change of transmit power and lowers the system complexity. Finally, numerical results are presented to validate our analyses.

We propose a novel physical layer scheme to suppress eavesdropping. Different from the existing schemes which are based on secrecy rate enhancement with Gaussian signal, our scheme is executed from the actual signal point of view. In our scheme, we set several structures of constellations for each modulation mode, and then different structures are utilized for different signals' modulations. In this case, on one hand, even though the eavesdropper knows this modulation mode, he#x002F;she is difficult to demodulate each signal. On the other hand, the information related to this constellation selection is safely delivered to the authorized user by a well-designed mechanism. Moreover, an auxiliary artificial interference method is introduced for further confusing the eavesdropper. In a word, our scheme is feasible for physical layer security improvement.

Cyber-physical system (CPS), regarded as the next generation of engineered system, has the capability to interact with the real physical world. Applications of CPS span various fields such as medical monitoring, traffic control, and smart grid. With such widespread applications, privacy assurance is becoming more and more important since what the CPS connects are people and the real world. Any leakage of private information will cause serious consequences. In this paper, we focus on enhancing the secrecy of wireless communications in CPS by use of physical layer security techniques. Specifically, we study an amplify and forward (AF) relay network where all devices are equipped with a single antenna. We propose a privacy-enhanced waveform design approach aided by artificial noise (AN) to enhance the communication secrecy in a wireless environment with multipath receptions. First, we consider the case with perfect eavesdropper's channel state information (CSI). We optimize the AF coefficient for forwarding the informationbearing signal and the AN covariance to maximize the achievable secrecy rate. The optimal solution is obtained by solving a series of semidefinite programs. Then, a more practical scenario with imperfect eavesdropper's CSI is studied. We develop a robust waveform design method and obtain the lower bound of the achievable secrecy rate. Numerical results are presented to show the effectiveness of our proposed algorithms.

Ultra-dense networks are envisioned as essential to embrace the skyrocketed traffic for the next-generation wireless networks. In this paper, we consider the uplink transmissions in ultra-dense networks, where the increased interference along with the increased density significantly challenges the efficient utilization of network resources as well as the provisioning of users' quality of services (QoS). Targeting these issues, we consider the QoS in terms of target signal-to-interference-plus-noise ratio (SINR) and power consumption simultaneously for each user within a multi-objective optimization model. We then investigate the interactions among users by leveraging the non-cooperative game-theoretical framework. By characterizing the properties of Nash equilibrium, we develop the target-SINR oriented resource allocation (TORA) algorithm, which features distributed implementation. Moreover, we obtain the condition to guarantee the convergence of our proposed TORA algorithm and demonstrate that it adapts to different interfering scenarios. Furthermore, considering the heterogeneous service requirements in real practice, we also design the target-SINR constrained resource allocation (TCRA) algorithm, such that TORA and TCRA are able to cope with voice and data services, respectively. Also provided are the simulation results, which demonstrate that, compared with the counterparts, our proposals more effectively guarantee the target-SINR for users with efficient power utilization.

This paper studies the privacy-preserving for downlink transmission in multiuser relay networks, where a source communicates with multiple users via a relay employing the amplify-and-forward protocol. Within any scheduling unit, only one user (desired user) is chosen for data reception, and the other users (undesired users) are viewed as potential eavesdroppers due to the broadcast nature of wireless medium. To prevent information leakage, we propose a physical-layer cooperative privacy preserving scheme, whose key idea is to schedule a cooperating user in addition to the desired user to deliver artificial noise (AN). By exploiting the characteristics of channels, the cooperating user carefully designs the AN transmitted during two time slots such that the AN can be canceled out at the desired user, but cannot be removed at the undesired users. As a result, the end-to-end signal-to-noise-ratio of any undesired user is heavily degraded, while that of the desired user is not seriously affected, thus preserving the data confidentiality of the desired user. To maximize the instantaneous secrecy rate, an opportunistic user selection criterion is developed. The lower bound of the ergodic secrecy rate (ESR) as well as the approximate upper bound of the secrecy outage probability is derived. The asymptotic performance of ESR is also analyzed via extreme value theory. Furthermore, to motivate users with heterogeneous channel conditions to participate in cooperation and guarantee the fairness among users, a user-grouping-based selection method is proposed. To evaluate the performance of this method, a novel concept called system fairness factor is introduced and studied. Theoretical analysis and simulation results show that, thanks to the proposed cooperative privacy preserving mechanism, the system ESR grows with the increasing number of users, and much higher secrecy rate and lower secrecy outage probability can be achieved compared with the existing schemes in the literature.

In this paper, the critical effect of noisy feedback in improving the physical layer security is investigated. Unlike previous works, where the eavesdropper's channel state information (either the instantaneous channel state information or the channel distribution information) is assumed known, a feedback and jamming scheme without any eavesdropper's channel state information is studied, which allows both the source and legal destination to transmit private message and degrade the eavesdropper alternatively in different phase of the transmission. More specifically, the situation in which no power constraint is considered first, and it shows using channel inversion a positive secrecy rate can always be achieved by mixing proper amount of artificial noise in the private signal, and the secrecy rate grows linearly with the power of private message in dB. Then we consider the practical situation with power constraint. Interestingly, it shows that a positive secrecy rate can also be obtained by using truncated channel inversion and selecting proper cutoff value and jamming power. Finally, the numerical results verify our analysis.

This paper studies privacy-preserving for downlink transmission in multiuser relay systems, where a source communicates with multiple users via a relay employing the amplify-and-forward (AF) protocol. At any scheduling unit, only one user (desired user) is selected to receive the source information, and the other users (undesired users) are viewed as potential eavesdroppers due to the broadcast nature of wireless medium. A cooperative physical-layer scheme is proposed to prevent information leakage. The key idea of this scheme is to schedule a cooperating user in addition to the desired user to deliver the artificial noise (AN). By exploiting the characteristics of channels, the cooperating user carefully designs the AN transmitted during two time slots such that the AN can be cancelled out at the desired user, but can not be removed at the undesired users. As a result, the detection performance of the desired user is free of interference, while that of undesired users is heavily degraded, thereby preserving the data confidentiality of the desired user. To maximize the secrecy rate of the system, a user scheduling policy is developed. Further, the lower bound of the ergodic secrecy rate (ESR) is derived, and its asymptotic behavior is analyzed via extreme value theory (EVT). Theoretical analysis and simulation results show that, thanks to the proposed cooperative AN injection mechanism, the system ESR grows with the increasing number of users, and much higher secrecy rate can be achieved compared to the existing schemes in literature.

Noise aggregation is an efficient way of aggregating the inherent noises introduced during wireless transmissions over multiple channels to degrade the eavesdropper's channel quality. While the performance of channel aggregation has not been thoroughly studied over wireless fading channels, we in this paper concentrating on analyses of its achievable average secrecy rate with emphasis on binary symmetric channel (BSC), whose cross-over probability is a time-varying process. The advantage of noise aggregation over traditional transmission in fading environments is demonstrated by our analyses and simulations. Simulation results show that the noise aggregation scheme can achieve a remarkable increase in terms of average secrecy rate even if the eavesdropper has better average channel quality than the legitimate receiver.

Pilot spoofing attack can deteriorate the transmission performance of the legitimate system and facilitate eavesdropping concurrently. Some detection methods of pilot attack have been proposed, while we consider the problem of channel estimation under pilot spoofing attack. In this paper, a hybrid channel estimation strategy using random Binary Phase Shift Keying (BPSK) is proposed. Specifically, a random BPSK sequence is transmitted by the legal user once the attack is detected. In this manner, the base station detects the sequence and re-estimates the legal channel with both contaminated pilot and assistant sequence. We evaluate the estimation performance in three cases according to different responses of the malicious user. Simulation results demonstrate that the proposed strategy can effectively estimate the legitimate channel against pilot attack.

The development of wireless technologies makes telemedicine more and more popular. However, due to the broadcast and openness nature of radio propagation, it makes the image transmitted over wireless channel much easier to be eavesdropped. Moreover, delay requirements is another concern in most application scenarios. To deal with the security issue, a secure scheme for wireless image transmissions based on fountain coding and adaptive resource allocation is proposed in this paper. For fountain-coded transmissions, the transmitter must firstly encode the source image packets into fountain packets using fountain codes before sending these fountain packets over the channels. At the receiving end, receiver can recover the original image if and only if a sufficient number of fountain packets have been correctly received and then a feedback signal will be sent to transmitter to end transmission. Therefore, the transmission secrecy will be achieved if the legitimate receiver can get enough fountain packets precede eavesdropper. To achieve this, adaptive resource allocation relative to the legitimate channel is used to ensure a higher packet reception rate at the legitimate receiver for the independent channel fading characteristic of legitimate receiver and eavesdropper. A medical image usually consists of region of interest (ROI) and background (BG). With respect to BG, ROI tends to contain important diagnostic information and need a higher reliability requirement. Source image packets can be divided into ROI source packets and BG source packets accordingly. In this condition, superposition coding, which can make transmitter deliver two packets simultaneously, is employed to reduce the transmission time delay. The simulation results show that the proposed scheme can achieve considerable gains in terms of image transmission security and the delay requirement.

This paper studies the secrecy outage probability of hybrid relay selection scheme which switches between full-duplex and half-duplex mode. First, motivated by the fact that either the full-duplex or half duplex has its own disadvantages, i.e., the full- duplex suffers from inherently self-interference even after cancellation by advanced technology while half-duplex cannot receive and transmit data simultaneously, we study the hybrid relay scheme. Then, inspired by the superiority of relay selection scheme, we propose the optimal full-duplex relay selection scheme and optimal hybrid relay selection scheme where the eavesdropper adopts joint decoding. Finally, the simulation results are presented, which show proposed scheme significantly outperform the traditional half-duplex relay selection scheme.

Security and energy efficiency are two critical metrics in many multiuser networks (e.g. M2M networks, sensor networks and ad hoc networks). In this paper, we try to maximize secure energy efficiency (SEE) by allocating power for those User Equipments (UEs) meeting the security transmission requirements, where SEE is defined as the ratio of the total secrecy throughput to the total transmission power in the whole network. Concretely, we propose an efficient algorithm which uses the parametric programming and Difference of Convex (DC) function to solve the optimization problem (i.e. maximize secure energy efficiency). Finally, we compare the secure energy efficiency of our scheme with that of fixed power allocation schemes and show the results through simulations at different system conditions.

In this paper, we investigate a new cooperative paradigm to provide information security for the primary system in cognitive two-way networks where the two-way secondary system can access the licensed spectrum to support the secondary quality of service (QoS) requirement as long as the secondary system provisions secure cooperation for the primary system against the malicious eavesdropper. To do so, the secondary system adopts the physical-layer method of cooperative jamming and relaying to protect the primary confidential message in two stages and acquire some spectrum opportunities for the two-way transmission in both stages. In addition, we try to allocate the power for transmitting jamming signal, secondary messages, and relaying messages in such a way that the secrecy capacity of the primary system is maximized subject to the minimum secondary transmission rate requirements. Furthermore, a sequential parametric convex approximation (SPCA) based iterative algorithm is proposed to solve this non-convex problem. Our proposed cooperative transmission scheme is reciprocally- benefited for both systems as the secondary system can access the licensed spectrum in both two slots and the primary confidential message can be protected from eavesdropping. In addition, we analyze the secrecy capacities for asymptotic scenarios. Simulation results demonstrate the performance superiority of our proposed scheme over conventional cooperative secure communication scheme in terms of the primary secrecy capacity.

Aiming at providing secure provisioning for the primary system, in this paper, we propose an energy harvesting based cooperative communication (EHCC) scheme which will protect the primary confidential message from eavesdropping under the constraint of the secondary quality-of-service (QoS) requirement. To do so, the secondary receiver (SR) firstly transmits jamming signal to protect the primary transmission and then, the secondary transmit (ST) harvests part of the received signals and forwards the remaining signals to the primary receiver (PR) concurrently with the secondary transmission. In our proposed scheme, radio- frequency energy harvesting will improve ST's maximum transmit power and the jamming interference at SR can be directly cancelled as SR has transmitted it. Then, we try to allocate the transmit power and design energy harvesting parameters in such a way that the secrecy capacity of the primary system is maximized under the constraint of the secondary QoS requirement. In addition, an iterative algorithm is proposed to solve this non-convex problem. Moreover, we also analyze the primary secrecy capacities and allocate the resource for the asymptotic scenarios. Simulation results demonstrate the performance superiority of our proposed scheme over the conventional cooperative secure communication scheme in terms of the primary secrecy capacity.

In the underlay cognitive radio network, the secondary users are allowed to transmit concurrently with the primary users , which shows great potential to relieve the spectrum scarcity problem. However, it raises significant security issues at the physical layer due to the spectrum sharing approach. Different from most present research, in this paper we study the secure communication problem between secondary users where the primary receiver is a curious passive adversary, i.e., an eavesdropper. Specifically, we consider the worst but common case where the primary receiver can decode the messages from its primary transmitter successfully before trying to decode secondary user's confidential messages. To protect these messages from being eavesdropped, we employ a fullduplex secondary receiver who can broadcast jamming signals to degrade eavesdropper's channel quality while guaranteeing the quality of service (QoS) for primary transmission. Following this framework, we formulate a secrecy capacity maximization problem and obtain the optimal power allocation scheme to allocate power between the secondary transmitter and the fullduplex secondary receiver. Simulations are presented to show the superiority of the optimal scheme over baseline algorithms.

Guaranteeing the secure delivery is a critical yet challenging issue in wireless transmission. In this paper, a secure delivery scheme that utilizing the dynamic fountain code design is proposed. Using fountain-coded transmission, the transmitter continuously sends fountain packets until the legitimate receiver successfully recovers the original data from a sufficient number of fountain packets. Secure delivery can be guaranteed if the eavesdropper overhears inadequate fountain packets to recover the original data. Inspired by this insight, we propose a fountain-encoded scheme in transmitter which adopts the feedback from the legitimate receiver as the encoding motivation. By dynamically adjusting the fountain-encoded mechanism based on the message fed back from legitimate user, the proposed scheme is beneficial to enhance the decoding rate of legitimate receiver. Further, we analyse the performance for the intercept probability as well as the transmission efficiency of the transmitter. Simulation results confirm our analytical results and demonstrate that, compared with the counterparts, our proposed scheme more effectively guarantees secure wireless delivery with lower intercept probability and higher transmission efficiency.

We propose a novel physical layer scheme for improving multiple users' information security e.g., vehicle devises and mobile terminals in the next-generation communication systems. Owing to the limitation of service node's antennas, not all users can be concurrently served. Therefore, the node only provides services for some chosen users in a certain time. On the basis of this premise, in our scheme, the access status of each user is involved. With the information of access status, the node devises transmitters to increase the served users' transmission rates and decrease the possibility of each idle user's signal interception. Furthermore, the node executes active jamming according to the realistic situation of this multi-user network for confusing the idle users/potential eavesdroppers. In addition, with the gradually mature applications of full-duplex techniques for the terminals, each user can also implement active jamming in the full-duplex mode to interfere with the potential eavesdroppers. It is seen that our scheme is linear without iteration, and performance analysis and simulation results illustrate that it is feasible for multi-user security enhancement. Copyright © 2016 John Wiley & Sons, Ltd.

Cooperative relaying communications is an efficient paradigm for end-to-end data delivery in industrial wireless sensor networks. However, due to the broadcast nature of radio propagation, it is challenging to guarantee the secrecy of cooperative transmissions under eavesdropping attacks. To deal with this issue, a fountain-coding aided relaying scheme is proposed in this paper, for which all the source packets are first encoded with fountain codes (FCs) and then transmitted over the channels. Based on the basic characteristic of FC transmissions, a sufficient number of coded packets have to be successfully received to recover the original data. Therefore, transmission secrecy is guaranteed if the legitimate receiver can accumulate the required number of FC packets before the eavesdropper does. To satisfy this condition, a cooperative jamming method is utilized to worsen the received signal quality at the eavesdropper. By applying the constellation rotation approach, the information-bearing signal and the jamming signal are designed carefully to reduce the negative effect of the jamming procedure on the legitimate receiver. To evaluate how the scheme behaves in wireless fading channels, the authors propose a novel performance metric, i.e., the quality-of-service violating probability (QVP), and derive its closed-form expression. Compared to the commonly used metrics in physical-layer security such as secrecy outage probability, QVP can give a more comprehensive performance evaluation for the system, including the delay, the reliability, and the security level as well. Finally, the theoretical analysis is validated by simulation results.

Traditional multi-channel cooperative spectrum sensing (CSS) scheme schedules a group of cognitive users (CU) to sense a particular channel in any given sensing slot, which means that the same sensing sequence pattern is shared by all CUs in the group. Although the sensing accuracy can be improved, the energy consumption will correspondingly increase. In order to reduce the energy consumption without loss of the sensing accuracy, we in this letter propose a cyclic-shifting based sequential CSS strategy for multi-channel cognitive networks (CN). Specifically, instead of employing the common shared sensing sequence pattern, our proposed strategy assigns a unique cyclic-shifting based sensing sequence for each CU, such that different channels will be sensed simultaneously in any given sensing slot. Moreover, if the decision for a particular channel can be made by current sensing information, the channel will not be sensed in the following sensing slots. Theoretical analysis shows that our proposed strategy can efficiently reduce the number of both sensing slots and reporting slots consumed for each channel and achieve the same probabilities of detection and false-alarm as the traditional CSS scheme. This implies that the energy efficiency of the system can be improved while maintaining the sensing accuracy undegraded. Simulation results are also provided to demonstrate the superiority of our proposed strategy as compared to the existing scheme.

Reactive jamming, which performs jamming attacks on condition of detecting the legitimate transmissions, is widely considered as one of the most serious security challenges in wireless communications. In this paper, we tackle the reactive jamming issue from a novel yet realistic perspective -- the jammer may not always be able to accurately detect the legitimate transmissions, which in turn, can be exploited by the legitimate user to enhance security. In accordance with the detection- then-jamming characteristic of reactive jamming, we formulate the transmitting-jamming problem within a Stackelberg game framework, where the legitimate user takes action first, followed by the reactive jammer. To optimize its own utility, the legitimate user needs to determine the transmission strategy by elaborately achieving the tradeoff between the signal-to- interference-plus-noise ratio (SINR) and the probability to be accurately detected and thus jammed by its adversary. The investigation on Stackelberg equilibrium provides the solution to the game model. Furthermore, we consider the more practical situation that the legitimate user has only incomplete knowledge regarding its adversary and analyze the corresponding impact on the game and equilibrium. Simulation results demonstrate significant performance superiority in terms of secure legitimate transmissions compared with the classical approach.

In this paper, we propose a joint amplify-and-forward (AF)-based channel estimation and user selection (J-ACES) scheme for multi-user MIMO (MU-MIMO) systems. Firstly, without channel state information (CSI) quantization, the transmitter estimates the downlink CSI for each receiver by adopting AF-based CSI feedback mechanism, in which the receiver amplifies and feeds back its own received downlink pilot symbols to the transmitter. An analytic user normalized mean square error (UNMSE) characterization of the uplink channel estimation and a lower bound of UNMSE for the downlink channel estimation are respectively derived and verified by Monte Carlo simulations. We show how the performance of UNMSE is influenced by the uplink and downlink pilot signal to noise ratio (SNR). Secondly, we obtain a corresponding suboptimal beamformer and estimated Signal to Interference Plus Noise Ratio (SINR) for each receiver to minimize the influence of estimation error on the subsequent user selection based on semi-orthogonal user selection (SUS). Finally, a mechanism that each selected receiver is informed of all the selected receiver beamformers to adopt the minimum mean square error (MMSE) detector is performed for further reducing the error caused by the uncertainty of the user selection. Simulation results show that the proposed scheme can improve the system spectral efficiency and has robust spectral efficiency gain even under the condition that the uplink pilot SNR is 20dB lower than the downlink pilot SNR, as compared to Quasi-MMSE Weight (QMW) scheme.

In light of the drastically-increasing demands on ubiquitous information acquisition and exchange, battery-powered devices are playing a more critical role in wireless networking, which inspires and expedites the development of energy-harvesting techniques. The sustainability requirements for such devices include two mutual-impacting dimensions: energy supply avoiding battery outage and stable transmissions avoiding buffer overflow. We in this paper propose a power-and-rate adaption scheme subject to the constraints on buffer-overflow probability and battery-outage probability in energy-harvesting enabled networks. The two probability constraints address the statistical assurance for the two-dimensional sustainability, which is suitable to dealing with the highly unpredictable energy-harvesting and channel fading statuses. Specifically, by applying the asymptotic queuing analyses to data-transmission and energy-harvesting processes, we can characterize both of the battery-outage and buffer-overflow probabilities via the effective-capacity/-bandwidth theories. Then, we formulate the effective-capacity optimization problem, which maximizes the sustainable throughput while complying with the statistical buffer-overflow and battery-outage constraints, and solve for the optimal power-adaptation scheme. We also conducted abundant simulations to evaluate our scheme's performances, demonstrate the superiority of our proposed scheme over existing baseline schemes, as well as study the impact of sustainability requirements on the performances.

In recent years, many approaches have been introduced in next generation (5G) wireless cellular networks in response to the demands for higher data rates and broader coverage. In this paper, a novel downlink three-dimensional (3D) beamforming scheme is proposed for the 5G multiuser multiple-input multiple-output (MU-MIMO) system. This scheme separates beams in the so-called elevation domain via base station (BS) antenna tilt assignment, with the objective of reducing inter-user interference. The key to this scheme is controlling the vertical radiation pattern of BS antennas, which is realized by more efficient use of a two-dimensional (2D) planar antenna array. Moreover, we give the optimal solution of 3D beamforming to maximize the users average data rate, including adjustments of the antenna array and the corresponding multiuser selection algorithm. This can be used as a systematic framework for any given 3D scenario to mitigate inter-user interference. Our simulation results demonstrate the performance benefits in terms of transmission rate in comparison with traditional schemes.

Supporting massive access of machine-type devices in a short period is a critical challenge in machine-to-machine (M2M) communications. We in this paper propose a traffic-aware Access Class Barring (ACB) scheme to improve the scalability of M2M networks. Unlike traditional ACB scheme, our proposed scheme aim at dynamically regulating the parameter of access probability, called barring factor, based on network load, thus accommodating much more M2M devices as well as lowering the access delay. To achieve this goal, we first develop a Markov-Chain based traffic-load estimation scheme according to the collision status. Then, we propose a spectrum of functions to control the barring factor varying with the estimated traffic load. Also provided is a set of simulations results, demonstrating that our proposed traffic-aware scheme significantly outperforms the traditional ACB scheme in terms of not only access success probability, but also average access delay.

In heterogeneous networks, users are usually confronted with multiple covering base stations (BSs) that differ in the respects of transmit power, bandwidth resources, and so forth, which makes the user association problem more challenging. In this paper, we consider this problem by emphasizing the long-term effect of the user association policy against the dynamic wireless environment for each individual user. In particular, we exploit the stochastic game model to characterize users' non-cooperative behaviors that they compete for the limited resources at BSs for better services, where the reward function for users is defined as their infinite-horizon discounted sum rate. Such a formulation has the advantage to track the users' performance in the long run with respect to the channel state variations. The Nash equilibrium of the game is obtained from users' best-reply playing, which is formulated as a Markov decision process with the value iteration algorithm providing the solution. Furthermore, we specially analyze the two-BS scenario and derive the threshold-based results for the association policy. The simulation results demonstrate that, compared with the counterparts, our proposal achieves higher system sum rate with relatively lower frequency of handovers, and improves the fairness in terms of transmission rate among users.

In this paper, we investigate the optimal power allocation strategy for underlay-based cognitive radio networks (CRN) with statistical quality-of-service (QoS) protection of primary users (PU). Instead of utilizing commonly used average/peak interference power constraints to protect PU's transmission, our proposed power allocation strategy will satisfy PU's statistical QoS requirement characterized by the queue-length bound violation probability. By applying the effective capacity theory, we convert PU's queue-length bound violation probability constraint to the equivalent maximum sustainable traffic load requirement. Then, we formulate the optimization problem aiming at maximizing the average transmission rate of secondary user (SU) while meeting PU's statistical QoS requirement as well as SU's average and peak transmit power constraints. Unfortunately, such a problem is non-convex. By employing the theories of convex hull and probabilistic transmission, we successfully convert the original non-convex problem to an equivalent strictly convex problem and obtain the optimal power allocation strategy of SU through Lagrangian approach. Simulation results are also provided to demonstrate the impact of PU's statistical QoS requirement on the SU's maximum achievable transmission rate as well as the superiority of our proposed optimal strategy as compared to the fixed power allocation scheme.

Recently, amorphous cells emerge as a promising paradigm in cellular networks to support bursty traffic caused by the user mobility, where dense small cells can cooperate on data delivery to mobile users thus implementing coverage without definite cell shape. In this paper, we propose a novel small cell deployment scheme, aiming at optimizing the network throughput over varying distributions of users' locations. Unlike the traditional research simply assuming one specific distribution of users' locations, our work could deal with the more realistic scenario, where the statistical information of users' locations might change across different time periods. In order to tackle this issue, we formulate the throughput maximization problem over multiple user-location distributions. Then, by applying the simulated annealing algorithm, we develop an iterative location deployment updating algorithm to solve for the efficient deployment as well as cooperation and resource allocation among small cells. Simulation results show that our proposed strategy can effectively improve the system average throughput, cell-edge user throughput, and user fairness as compared to the traditional approach.

This paper investigates the problem of secure communications of the two-hop primary system with the cooperative jamming of the untrusted secondary system. The secondary system is untrusted for the primary system and willing to eavesdrop on the primary signal. In addition, the secondary system is also willing to provide friendly jamming to increase the secure rate of the primary system in reward for being allowed to share the licensed spectrum. Specifically, the cooperative communication is implemented into two slots which correspond to the transmission of the first and second hops of the primary system, respectively. In each slot, part of the slot is allocated for the primary information transmission. Simultaneously, a secondary user (SU) is selected to broadcast jamming signal to protect the secure communication of the primary users (PU) against the other untrusted SUs. Then, the remaining time of the slot is allocated for the secondary transmission. To maximize the transmission rate of the secondary system under the constraint of the target secure rate requirement of the primary system, we optimally select two jamming SUs and determine the time parameters in each slot. SUs' average transmit rate and the lower bound on PUs' secure outage probability are derived. Simulation results demonstrate the performance superiority of our developed strategy over conventional secure communication schemes in terms of PUs' secure outage probability and SUs' average transmission rate.

Inter-user interferences (IEIs) and intra-user interferences (IAIs) may degrade the quality-of- service in JT (joint transmission)-CoMP (coordinated multi-point) systems. Interference alignment (IA) with prefect channel state information (CSI) can efficiently mitigate these interferences. However, in realistic systems, the effect of IA is obviously decreased via channel uncertainty with channel estimation and quantization errors. In this case, we propose a novelly robust precoder-and-receiver design scheme with IA (NRIA) for interference suppression and channel uncertainty restraint. In the NRIA, we first utilize precoding method to suppress the IEIs to each user generated by the users' actually acquired channels. Second, the receiver of each user is devised to alleviate the impact of IAI and channel uncertainty. Unlike the existing MU-MIMO schemes which introduce the statistical or bounded information of the above errors, the NRIA is a local scheme for JT and contains the instantaneous information of channel uncertainty. The instantaneous information can be abstracted through installing and sending special transmit signals from base stations to users without exorbitant overhead. The NRIA doesn't include any iteration, moreover, it can suppress interference and improve robustness compared with just using statistical or bounded information of channel uncertainty.

Due to the highly-stochastic nature of wireless channels, how to provide efficient delay quality-of-service (QoS) provisioning for primary users (PU) while optimizing the performance of secondary users (SU) is a critically important task for cognitive radio networks (CRN). To address the above issue, we investigate the optimal power allocation strategy for underlay-based CRN with PU's statistical delay QoS protection. Instead of utilizing the widely-used interference power constraint to protect PU's transmission, we aim at satisfying PU's statistical delay QoS requirement characterized by the queue-length bound violation probability. By applying the theory of effective capacity, we further convert PU's queue-length bound violation probability constraint to the equivalent maximum sustainable traffic load requirement. Then, we formulate the optimization problem to maximize SU's average throughput while meeting PU's statistical delay QoS requirement as well as SU's average and peak transmit power constraints, which can be proved as a nonconvex problem. By employing the theories of convex hull and probabilistic transmission, we convert the original nonconvex problem to the equivalent strictly convex problem and then obtain the optimal power allocation strategy, which adapts to both PU's delay QoS requirements and channel conditions. Moreover, we also develop for comparison a fixed power allocation scheme that only adjusts with PU's delay QoS requirements. Simulation results are provided which demonstrate that both the optimal and fixed schemes can flexibly allocate the upperbounded transmit power budget according to PU's delay QoS requirements, but the proposed optimal power allocation strategy can also efficiently exploit the time-varying nature of wireless channels and thus significantly outperforms the fixed power allocation scheme.

In this paper, we develop a macro-femto cooperation strategy for uplink transmissions of multi-channel two-tier networks, which aims at alleviating the co-channel interference and optimizing the energy efficiency of macro-users (MUEs) and femto-users (FUEs) simultaneously. Specifically, the features of our work include three folds. First, our proposed strategy allows the MUE to select a femto-access point (FAP) to perform hybrid access, which efficiently eliminates the cross-tier interference. Second, by adopting the coalitional game in partition form, the users with strong mutual interference form a coalition to share the channel in a time-division multiplexing manner such that the intra-coalition interference can be avoided. The corresponding time-division policy is obtained by employing the Nash bargaining solution. Third, the inter-coalition resource competition problem is solved within a non-cooperative energy efficiency game framework and the transmit power for each user is derived through Nash equilibrium. Theoretical analysis shows that our proposed strategy can efficiently improve the energy efficiency of FUEs. Also provided are simulation results which demonstrate the performance superiority of our developed strategy over the non-cooperative scheme in terms of user's energy efficiency and data transmission rate.

3GPP LTE-Advanced (LTE-A) cellular networks support relay transmissions to improve the cell-edge users throughput as well as the system capacity, so that mobile communications services including multimedia transmissions, data download, real-time online gaming, etc., can be served with better quality-of-services (QoS). In LTE-A networks, Type I relay is widely used, where the relay station (RS) and evolved Node B (eNB) independently schedule and allocate resource for their served user equipments (UE). Existing research often assumes that the backhaul link (connecting eNB and relay) and the access link (connecting relay and UE) employ the same transmission rate, in order to avoid traffic congestions at the RS without queuing buffer. However, this makes the total relay throughput subject to the worse channel quality between the backhaul link and access link, thus severely degrading the system capacity as the wireless channels vary with time. To overcome this problem, this paper enables buffering function at RSs and proposes a buffering-aided three-step resource allocation scheme, which can efficiently make use of the time-varying channel qualities for data delivery. Furthermore, optimizations for the long-term fairness and overall network throughput are jointly designed. Also conducted is a set of system-level simulations to evaluate the performances of the proposed scheme. Simulation results show that the proposed scheme can not only improve the average network throughput compared with existing baseline schemes, but also achieve better long-term fairness over RS-served UEs and eNB-served UEs.

Ubiquitous information exchange has motivated wide research interests on device-to-device (D2D) networks, where device nodes can communicate to each other reusing the cellular network's spectrum in an underlay fashion. We in this paper propose the peer-to-peer (P2P) share enabled routing schemes over multi-hop interference-constrained D2D networks, where multiple D2D subscribers attempt to download the common data from multiple distributed D2D servers. We aim at maximizing the average download rate over subscribers while keeping the interferences to cellular network's spectrum under a tolerable level. We focus on the scenario with two subscribers, two file servers, and two cellular users to avoid massive P2P share drastically increasing the interference temperature and crashing the network. Specifically, we develop a Routing scheme with Direct P2P-Share (R-DPS), which allows subscribers distribute their received data to each other. Moreover, we propose a Routing scheme with Coverage-based P2P-Share (R-CPS). The R-CPS scheme makes use of the broadcast nature of wireless channel to assure that each route can cover all subscribers, unlike passing through them in R-DPS scheme. The R-DPS and R-CPS schemes have the potential to enhance the data download rate compared with the approach without P2P support.

Relay selection is a crucial problem for LTE-advanced (LTE-A) networks in order to improve the performance of users in the cell-edge or the hot-spot areas. Relay selection needs to not only consider the distance, the channel quality or the differentiated throughput require of the network, but also take system traffic load into account. It is worth noting that the unbalanced traffic load often degrades throughput performance of the user under the backhaul resource constraint. To address this problem, we in this paper propose a scheme for joint relay selection and long-term resource allocation in interference-coordination enabled LTE-A networks. We show that the traffic load can be better balanced via controlling the global differentiated-fairness. Simulation results demonstrate that compared with conventional relay selection schemes, our proposed scheme can enhance the performance in terms of the global differentiated-fairness as well as throughput for hot-spot or cell-edge areas.

Power control strategy that guarantees users' quality-of-service (QoS) in a power-efficient manner is a critical yet challenging issue in distributed wireless networks. In this paper, we investigate the problem by considering the energy consumption and QoS provisioning simultaneously, where the QoS requirement is specified by the target signal-to- interference-plus-noise ratio (SINR). The problem is represented as multi-objective optimization at each individual user. Then, we cast the formulation within a non-cooperative game framework where the weighted sum of the original objectives is the payoff function. Following our analyses on the properties of Nash equilibrium, we propose the target-SINR oriented power control (TOPC) strategy, which has the advantage of distributed implementation. Further, we reveal the condition for TOPC to converge and illustrate its performance in the extreme cases. Simulation results confirm our analytical results and demonstrate that, compared with the counterparts, our proposal more effectively guarantees users' QoS with efficient power utilization.

The coexistence of a macrocell and a number of femtocells often leads to a two-tier heterogeneous network, where the co-tier interference (CotIN) and cross-tier interference (CrotIN) both degrade users' quality of service. In order to mitigate the two types of interferences, we propose a precoding scheme for the cooperative femtocells, called CotIN elimination and CrotIN suppression (CECRS) precoding. In this scheme, we first eliminate the CotINs of each user by applying the QR decomposition to channel matrix. Then, the CrotINs of MUs and FUs are suppressed by the macrocell base station (MBS) and femtocell access points (FAPs), respectively. The CECRS scheme doesn't require much information exchange between the MBS and FAPs, and thus significantly reduce the difficulty of its implementation. Simulation results show that the CECRS effectively improves the users' transmission rates.

In cognitive Ad Hoc networks (CAHN), because the contentions and mutual interferences among secondary nodes are inevitable as well as secondary nodes usually have limited power budget, spectrum efficiency and energy efficiency are critically important to the CAHN, especially for the medium access control (MAC) protocol design. Aiming at improving both spectrum and energy efficiencies, we in this paper propose a diversity technology called Channel-Aggregation Diversity (CAD), through which each node can utilize multiple channels simultaneously and efficiently allocate the upper-bounded power resource with only one data radio. Based on the proposed CAD technology, we further develop a CAD-based MAC (CAD-MAC) protocol, which enables the secondary nodes to sufficiently use available channel resources under the upper-bounded power and transmit multiple data packets in one transmission process subject to the transmission-time fairness constraint. In order to improve the performance of CAHNs, we propose two joint power-channel allocation schemes. In the first scheme, we aim at maximizing the data transmission rate. By converting the joint power-channel allocation to the Multiple-Choice Knapsack Problem, we derive the optimal allocation policy through dynamic programming. In the second scheme, our objective is to optimize the energy efficiency and we obtain the corresponding allocation policy through fractional programming. Simulation results show that our proposed CAD-MAC protocol can efficiently increase the spectrum and energy efficiencies as well as the throughput of the CAHN compared with existing protocols. Moreover, the energy efficiency of the CAHN can be further improved by adopting the energy efficiency optimization based resource allocation scheme.

Dynamic spectrum auction has been widely recognized as a promising solution to spectrum allocation in cognitive radio networks. However, the performance of dynamic spectrum auction is significantly affected by the uncertainty of the available time of spectrum opportunities. To combat this uncertainty, we propose risk-reduced auction (RRA) mechanism in this paper. Specifically, we study the scenario where a cognitive base station (CBS) auctions spectrum opportunities to secondary users (SUs). Modeling the traffic pattern of primary users (PUs) as an alternating renewal process, we derive the optimal auction time to maximize the auctioneer's utility to combat the time-duration uncertainty. In the meantime, a collision probability constraint is imposed to protect the PU's priority on spectrum utilization. We show analytically that both the auctioneer and the SUs are truthful and there is a weakly dominant equilibrium in the RRA algorithm. Moreover, the SUs bidding risk can be effectively reduced. Also conducted is a set of simulation evaluations which demonstrate the improvement of both auctioneers and SUs' utilities achieved by using our proposed RRA algorithm.

Device-to-Device (D2D) communications underlaying cellular networks have been considered as an efficient way to improve the system capacity of the cellular networks in an underlaying paradigm. However, increasing interferences between regular cellular communications and D2D communications often prevent the overall system throughput from being further enhanced. To address the corresponding interference management issue, we in this paper study the intelligent resource allocation for D2D pairs. Unlike the conventional resource allocation scheme where each D2D pair shares only one cellular user's entire resources upon approval, our approach allows D2D pairs to share only part of each cellular user's resources, such that the interference on regular cellular communications can be suppressed. In the mean time, D2D pairs can share multiple cellular users' resources to meet their own quality of services (QoS) requirement. Following the aforementioned principles, we propose a Partial Time-frequency Resource Allocation (PRA) framework for D2D communications. Under this framework, we design a category of sharing functions to map the channel status between D2D transmitters and cellular receivers to the resource proportion shareable for D2D users, through which a suboptimal yet simple and effective solution is derived, termed PRA scheme. Simulation results show that while meeting the D2D users' QoS requirement, our proposed PRA scheme can improve the overall system capacity and stability as compared to the conventional approaches.

Cognitive Radio has been widely regarded as an effective way to improve the spectrum utilization. In Cognitive Radio Networks (CRN), the heterogeneity of spectrum availability often leads to link interruptions among Secondary Users (SUs). By deploying relay nodes, the link interruption problem can be effectively mitigated since relay nodes bring more freedoms for the system optimization. By jointly exploiting the multi-dimensional optimization flexibilities, including transmission mode selection, relay selection, and channel allocation, we in this paper propose a framework towards minimizing the outage percentage of SUs in cognitive relay networks. In particular, we formulate a combinatorial optimization problem to achieve this goal, which is then transformed into a max-matching problem in the Graph Theory. Using the Hungary algorithm, the optimal solution to this problem is obtained with a polynomial computational complexity. Simulation results show that by combining advantages of multiple transmission modes, including direct transmission, amplify-and-forward, and decode-and-forward, our proposed joint transmission mode selection and resource allocation strategy can significantly reduce the occurrences of link interruptions in cognitive radio networks.

The conventional depth-first sphere decoder (DF-SD) and breadth-first K-Best algorithm for MIMO detection are subject to unstable output throughput and error-rate degradation, respectively, over ill- conditioned channels. These problems become more serious in high-order MIMO systems with more antennas and larger constellation size. In this paper, we propose a condition-number driven decoder for high-order MIMO systems to overcome these problems. Specifically, we first apply K-Best algorithm with Winner-Path Enumeration (WPE) method on high layers of the detection tree to guarantee the throughput stability and apply DF-SD on remaining layers to lower the bit-error-rate (BER). As the throughput fluctuation or, equivalently, complexity fluctuation of DF-SD often happens with a large condition number, we adaptively adjust the number of layers applying K-Best WPE by comparing the condition number with a predefined threshold, such that the complexity can be further decreased. Through theoretical analyses and simulation verifications, we derive a criterion of determining the number of layers adopting K-Best algorithm. Performance analyses and simulation evaluations show that our proposed scheme has much lower average complexity than K-Best WPE, while achieving near-optimal BER performance and stable complexity / throughput.

The K-best detection algorithm with its diverse variations, which belong to a typical breadth-first type of quasi maximum-likelihood (ML) detection algorithms, have been close to implementation in realistic communications systems for its excellent bit error rate (BER) performance with reasonable computational complexity. However, when high-order modulation schemes are employed, the complexity for MIMO detection remains drastically high. In this paper, we propose an adaptive low-complexity constellation-reduction aided K-best detection algorithm for MIMO systems using 64QAM/256QAM modulation schemes. Our proposed algorithm can adaptively reduce the size of candidate constellation set in each detection layer, which differs from the conventional schemes using a fixed strategy, such that the complexity can be further decreased. Moreover, our algorithm adopts real-valued constellation set for constellation-reduction rather than the traditionally-used complex-valued set, achieving better tradeoff between the computational complexity and detection performance. Simulation results show that compared with existing algorithms, our proposed approaches have better BER performance as well as reducing the complexity.

SUMMARY Although the wireless relay transmission has been recognized as one of the effective approaches in extending the coverage area and improving the network capacity, the delay control over the two‐hop wireless channels toward end users’ QoS satisfaction is still challenging in green wireless network designs. In this paper, we propose the QoS‐driven delay control schemes over two‐hop wireless relay links to statistically upper bound the total queuing delay for the decode‐and‐forward relay transmissions. Specifically, we first conduct asymptotic queuing‐delay analyses for the relay transmissions. Then, we show that an efficient approach for statistical delay guarantees is to make the exponentially decaying speed of the complementary cumulative distribution functions of the queueing delay at both hops identical. We further derive that this target needs to be attained through asymmetric resource allocations over the two hops. Motivated by this fact, we formulate two optimization problems aiming at satisfying the specified delay‐bound violation probability for the relay transmission, while minimizing the power consumption toward green wireless networking. One framework is for the frequency‐division duplex relay mode and the other is for time‐division duplex relay mode. In the frequency‐division duplex relay mode, the source and relay nodes use a different transmit power. For the time‐division duplex relay mode, the source and relay nodes use time‐slots with different length but with the same instantaneous power. We solve for the optimal solutions for both of the aforementioned frameworks. Also conducted is a set of simulation results to show the impact of the QoS requirements, traffic load, and position of the relay node on the resource allocation over wireless relay links. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY Dynamic spectrum auction (DSA) has been considered as one of potential spectrum allocation approaches in cognitive femtocell networks. As a modified version of traditional spectrum auction, DSA should not only increase auction revenue but also improve spectrum utilization on finer time granularity. We propose a DSA algorithm based on a double optimization framework (DOF), which focuses on the optimization of auction revenue and spectrum utilization. The optimization processing consists of two stages. Firstly, a proper auction period is selected to balance the expected spectrum utilization and auction revenue. Then, the cognitive femtocell base station adjusts its reserve price with the repetition of auction to leverage over instant revenue and spectrum utilization. At the same time, the bidders can adjust their bidding price to improve utilities. Performance analysis shows that the DOF‐based DSA algorithm has low complexity and can resist collusion, so it can be carried out frequently with small overhead. On the other hand, it is better than the greedy algorithm and Vickrey–Clarke–Groves auction on revenue. Simulation results show that the DOF‐based DSA algorithm can keep a fine spectrum utilization and bring the cognitive femtocell base station more revenue in both single‐unit award spectrum auction and multi‐unit awards spectrum auction. Copyright © 2012 John Wiley & Sons, Ltd.

Recent measurements show that there are abundant spectrum opportunities across the licensed cellular bands, over which the relay stations (RSs) are widely employed for both coverage extension and throughput improvement. However, current efforts in cognitive radios focus on exploiting spectrum holes over the licensed direct transmission links. To exploit new spectrum opportunities over the licensed relay assisted transmission links, we propose a novel two-phase joint sensing and transmission scheme (TP-JSTS) for the dual-hop Amplify-and-Forward (AF) relay assisted primary user (PU) transmission in cognitive radio networks. The TP-JSTS takes into account the relay behaviors of the PU system. In the first phase, the secondary user (SU) senses the signal transmitted by the PU base station (BS). In the second phase, the SU detects the signals sent by the PU-Relays. To protect the PU from harmful interference, the SU transmits within the current band in the rest of each phase only when the PU is detected to be absent. We investigate the sensing performance, throughput performance and delay performance of our proposed TP-JSTS when all the PU-Relays adopt the AF relay protocol. We obtain the optimal sensing-time allocation strategy that maximizes the achievable throughput of the SU and the optimal sensing-time allocation strategy that minimizes the average transmission delay of the SU. Simulation results show that there exists an optimal pair of sensing-time durations that maximizes the achievable throughput for the SU, while there exists another optimal pair of sensing-time durations that minimizes the average transmission delay for the SU.

Dynamic spectrum access (DSA) strategy selection and the associated resource allocation are critically important issues for cognitive networks, because they need to not only satisfy the interference constraint caused to the primary users (PU), but also meet the delay quality-of-service (QoS) requirements for the secondary users (SU). In this paper, we develop the optimal resource allocation schemes for the underlay and overlay DSA strategies, respectively, in delay-QoS constrained cognitive networks. Specifically, for the underlay strategy, we find that 1) when the maximum average interference power is less than the maximum average transmit power, the cognitive network will gradually converge to an interference-power constrained system as the QoS constraint becomes more stringent; 2) when the maximum average interference power is larger than the maximum average transmit power, the cognitive network reduces to a transmit-power constrained system not varying with the QoS requirement. For the overlay strategy, we observe that 1) a unique optimal sensing time exists under the given QoS constraint; 2) the optimal sensing time increases as the QoS constraint gets more stringent. Following these results, we further propose a selection criterion across underlay and overlay DSA strategies. By applying this criterion, the SU can determine whether to use underlay or overlay for DSA under the given QoS constraint and the PUs' spectrum-occupancy probability.

Resource allocation is a critically important issue for cognitive networks, because it needs to not only meet the quality-of-service (QoS) requirements for the secondary users (SU), but also protect the QoS of primary users (PU) from degradation. In this paper, we develop the optimal power allocation scheme for the cognitive network, which can satisfy the QoS requirements of SUs and PUs simultaneously. Specifically, on the one hand, as the deterministic delay QoS provisioning is usually unrealistic for practical wireless networks, we use the theory of effective capacity for SU's statistical delay QoS provisioning. On the other hand, in order to meet the PU's QoS demand, we impose the PU's outage probability constraint on the cognitive network instead of the traditional average and/or peak interference power constraints. Following the above concept, we derive the optimal power allocation scheme for the cognitive network under the SU's average and peak transmit power constraints and the PU's outage probability constraint. We find from simulation results that 1) the effective capacity of the cognitive network decreases as the statistical delay QoS requirement becomes stringent; 2) the performance of the cognitive network can be improved if the PU can tolerate higher outage probability; and 3) under the given average transmit power and PU's outage probability constraints, the cognitive network can achieve better performance while increasing the maximum peak transmit power, but the obtained performance gain for the stringent QoS requirement is more obvious than that for the loose QoS requirement.

Dynamic spectrum auction has been considered as one of potential approaches on spectrum allocation in cognitive radio networks. As an modified version of traditional static or quasi-static spectrum auction, dynamic spectrum auction should not only increase the auction revenue of the owner of spectrum, but also improve spectrum utilization on fine time granularity. We propose a dynamic spectrum auction algorithm with time optimization (DSA-TO) in 802.22 networks. First, the effects of auction period on auction revenue and spectrum utilization are argued and optimized. Then we give a complete spectrum auction procedure where an adaptive reservation price is used to balance the revenue and utilization of spectrum auction. Performance analyses show that the DSA-TO algorithm can resist collusion effectively and has low complexity as well as good revenue and utilization. Simulation results show that the DSA-TO algorithm is reasonable in the optimization of auction period and can keep a fine spectrum utilization and bring more revenue for both single unit spectrum auction and multi-unit spectrum auction.

Cognitive radio has been proposed to improve the spectrum utilization by allowing the unlicensed secondary users (SUs) to access the spectrum resources licensed to the primary users (PUs) opportunistically. However, the cognitive radio has rarely been employed to enhance the system performance of the licensed PUs that endure spectrum scarcity. To improve the outage-probability quality-of-service (QoS) of the mobile station (MS) in macrocellular networks, we propose two Cognitive Amplify-and-forward Relay (CAR) schemes in this paper. In our proposed CAR schemes, the MS and relay utilize both the licensed spectrum band (LSB) provided to the macrocellular network and the opportunistic spectrum band (OSB) discovered by the base stations. Simulation results show that compared with the conventional transmission schemes without cognitive relay, our proposals can effectively improve the outage performance of MS in macrocellular networks by exploiting both the space diversity and spectrum diversity.

Deliberate clipping is a simple solution to high Peak-to-Average Power Ratio problem of OFDM signal. In Amplify-and-Forward OFDM relay networks, due to the limited resource, deliberate clipping is also suggested. However, clipping is a nonlinear process and may cause significant performance degradation. Based on Bussgang Linearization Theory, we provide a linear system model for Amplify-and-Forward OFDM relay networks. To achieve spatial diversity, we design a practical nonlinear distortion aware receiver at the destination. Considering a total relay power constraint, we propose an optimal power allocation scheme to maximize the signal-to-noise distortion ratio. Simulation results show that our optimal relay power allocation scheme can improve the system throughput and resist the non-linear distortion. It is also verified that our proposed transmission scheme outperforms other transmission schemes without considering non-linear distortion.

Dynamic spectrum auction is considered as an effective solution to improve spectrum utilization efficiency in dynamic spectrum sharing networks because it can provide an economic incentive to motivate primary users to share their idle spectrum with secondary users. However, the primary users' own quality of services (QoS) cannot be guaranteed in case of demand peak because their spectrum is being used by the winners at auction. To solve this problem, we propose a recall-based dynamic spectrum auction (RBDSA) algorithm with which a primary base station (PBS) can auction its unused channels to some secondary wireless services providers (SWSPs) safely and economically. The PBS' users are granted a higher channel access priority than the SWSPs, then the PBS can recall some channels after auction to satisfy its demand if necessary. To maximize its profit from spectrum auction and self services, the PBS will reduce its excessive or deficient channels reservation, which is enforced with an introduction of punishment item in the PBS' utility. We show analytically that both the PBS and the SWSPs in the RBDSA algorithm are truthful and there is a weakly dominant equilibrium. Moreover, it can be extended to the scenarios with multiple units of channels demand and multiple PBSs. Simulation results show that the RBDSA algorithm can increase the utility of the PBS and improve the channels utilization efficiency while keeping the QoS of the primary users.

Periodic spectrum sensing over the entire primary user (PU) band always interrupts the secondary user (SU) data transmission in the sensing interval, which may degrade the quality of service of the SU. To alleviate this problem, we divide the PU band into two subbands, one for opportunistic SU data transmission, and the other for continuous spectrum sensing. Based on the PU band division, we propose a delay oriented continuous spectrum sensing (DO-CSS) scheme for delay sensitive SU services. In the DO-CSS scheme, the average SU transmission delay is reduced by selecting the proper bandwidth for spectrum sensing within each frame. Since different SUs may have different requirements on their quality of services, we further propose a throughput oriented continuous spectrum sensing (TO-CSS) scheme. In the TO-CSS scheme, the achievable average SU throughput is maximized by choosing the optimal sensing bandwidth within multiple adjacent frames. Both theoretical analyses and simulation results show that compared with the conventional periodical spectrum sensing scheme, the average transmission delay of the SU is reduced without degradation in the maximum achievable throughput by using the proposed DO-CSS scheme, and both the delay performance and achievable SU throughput are further improved by using the proposed TO-CSS scheme.

The time-varying feature of wireless channels usually makes the hard delay bound for data transmissions unrealistic to guarantee. In contrast, the statistically-bounded delay with a small violation probability has been widely used for delay quality-of-service (QoS) characterization and evaluation. While existing research on the statistical-delay control mainly focused on the single-hop links, in this paper we propose the QoS-driven power-allocation scheme over two-hop wireless relay links to statistically upper-bound the end-to-end delay for the decode-and-forward (DF) relay transmissions. Specifically, by applying the effective capacity and effective bandwidth theories, we first analyze the delay-bound violation probability over the two-hop link with independent service process in each hop. Then, we show that an efficient approach for statistical-delay QoS guarantees is to make the delay distributions of both hops identical, which, however, needs to be obtained through asymmetric resource allocations over the two hops. Motivated by this observation, we formulate and solve an optimization problem aiming at minimizing the total power consumptions to satisfy the specified end-to-end delay-bound violation probability over two-hop relay links. Also conducted is a set of numerical results to show the impacts of the QoS requirement, the traffic load, and the position of the relay node on the power allocation under our proposed scheme.

One of the major challenges in the medium access control (MAC) protocol design over cognitive Ad Hoc networks (CAHNs) is how to efficiently utilize multiple opportunistic channels, which vary dynamically and are subject to limited power resources. To overcome this challenge, in this paper we first propose a novel diversity technology called Channel-Aggregation Diversity (CAD), allowing each secondary node to use multiple channels simultaneously with only one data radio per node under the upperbounded power. Using the proposed CAD, we develop a CAD based MAC (CAD-MAC) protocol, which can efficiently utilize available channel resources through joint power-channel allocation while guaranteeing the transmission-time fairness. Particularly, we convert the joint power-channel allocation to the Multiple-Choice Knapsack Problem, such that we can obtain the optimal transmission strategy to maximize the network throughput through dynamic programming. Simulation results show that our proposed CAD-MAC protocol can significantly increase the network throughput as compared to the existing protocols.

We propose the QoS-aware BS-selection and the corresponding resource-allocation schemes for downlink multi-user transmissions over the distributed multiple-input-multiple-output (MIMO) links, where multiple location-independent base-stations (BS), controlled by a central server, cooperatively transmit data to multiple mobile users. Our proposed schemes aim at minimizing the BS usages and reducing the interfering range of the distributed MIMO transmissions, while satisfying diverse statistical delay-QoS requirements for all users, which are characterized by the delay-bound violation probability and the effective capacity technique. Specifically, we propose two BS-usage minimization frameworks to develop the QoS-aware BS-selection schemes and the corresponding wireless resource-allocation algorithms across multiple mobile users. The first framework applies the joint block-diagonalization (BD) and probabilistic transmission (PT) to implement multiple access over multiple mobile users, while the second one employs time-division multiple access (TDMA) approach to control multiple users' links. We then derive the optimal BS-selection schemes for these two frameworks, respectively. In addition, we further discuss the PT-only based BS-selection scheme. Also conducted is a set of simulation evaluations to comparatively study the average BS-usage and interfering range of our proposed schemes and to analyze the impact of QoS constraints on the BS selections for distributed MIMO transmissions.

The distributed multiple-input-multiple-output (MIMO) techniques across multiple cooperative base stations (BS) can significantly enhance the capability of the broadband wireless networks in terms of quality-of-service (QoS) provisioning for wireless data transmissions. However, the computational complexity and the interfering range of the distributed MIMO systems also increase rapidly as the number of cooperative BS's increases. In this paper, we propose the QoS-aware BS-selection schemes for the distributed wireless MIMO links, which aim at minimizing the BS usages and reducing the interfering range, while satisfying diverse statistical delay-QoS constraints characterized by the delay-bound violation probability and the effective capacity technique. In particular, based on the channel state information (CSI) and QoS requirements, a subset of BS with variable cardinality for the distributed MIMO transmission is dynamically selected, where the selections are controlled by a central server. For the single-user scenario, we develop two optimization frameworks, respectively, to derive the efficient BS-selection schemes and the corresponding resource allocation algorithms. One framework uses the incremental BS-selection and time-sharing (IBS-TS) strategies, and the other employs the ordered-gain based BS-selection and probabilistic transmissions (OGBS-PT). The IBS-TS framework can yield better performance, while the scheme developed under the OGBS-PT framework is easier to implement. For the multi-user scenario, we propose the optimization framework applying the priority BS-selection, block-diagonalization precoding, and probabilistic transmission (PBS-BD-PT) techniques. We also propose the optimization framework applying the priority BS-selection, time-division-multiple-access, and probabilistic transmission (PBS-TDMA-PT) techniques. We derive the optimal transmission schemes for all the aforementioned frameworks, respectively. Also conducted is a set of simulation evaluations which compare our proposed schemes with several baseline schemes and show the impact of the delay-QoS requirements, transmit power, and traffic loads on the performances of BS selections for distributed MIMO systems.

We propose the dynamic BS-selection schemes for distributed wireless MIMO links, which aim at minimizing the average base-station (BS) usage while satisfying the specified statistical delay quality-of-service (QoS) constraint characterized by the delay-bound violation probability. In particular, the distributed transmission system consists of a number of location-independent BS's, a central server, and one mobile station (MS). Based on the channel state information (CSI), the MS dynamically selects the subsets of BS's with variable subset cardinalities to construct the distributed MIMO link, and feeds back the selected BS's indices to the central server. The central server controls these selected BS's to cooperatively transmit data to the MS. Under the above framework, we develop the QoS-guaranteed dynamic BS-selection schemes for scenarios with and without feeding back the selected BS subsets' CSI, respectively. Simulation results show that our proposed dynamic schemes significantly decrease the average BS usage and interfering range as compared to baseline schemes which fix the cardinality of the selected BS subsets.

We propose the queue-aware spectrum sensing schemes for interference-constrained opportunistic transmissions of secondary users (SUs) in cognitive radio networks. Specifically, we employ the energy detection for SUs to sense the spectrum-usage status of the primary users (PUs). Unlike the traditional energy detector using a fixed energy threshold to decide the presence of the PUs' signals, we dynamically change the energy threshold based on the queue length at the sender of SUs. Our proposed dynamic-threshold policies aim at effectively satisfying the statistical quality-of-service (QoS) requirements such as the queue-length-bound violation probability and buffer-overflow probability, while upper-bounding the probability of causing interferences to the PUs. We develop the dynamic-threshold policies for the scenarios that the sender of SUs has infinite and finite queue buffer sizes, respectively. Simulations evaluations show that under the specified statistical QoS requirements and interference constraints, our proposed schemes can support higher data traffic loads for SUs than the traditional energy-detection based scheme.

Due to the time-varying wireless channels, deterministic quality of service (QoS) is usually difficult to guarantee for real-time multi-layer video transmissions in wireless networks. Consequently, statistical QoS guarantees have become an important alternative in supporting real-time video transmissions. In this paper, we propose an efficient framework to model the statistical delay QoS guarantees, in terms of QoS exponent, effective bandwidth/capacity, and delay-bound violation probability, for multi-layer video transmissions over wireless fading channels. In particular, a separate queue is maintained for each video layer, and the same delay bound and corresponding violation probability threshold are set up for all layers. Applying the effective bandwidth/capacity analyses on the incoming video stream, we obtain a set of QoS exponents for all video layers to effectively characterize this delay QoS requirement.We then develop a set of optimal adaptive transmission schemes to minimize the resource consumption while satisfying the diverse QoS requirements under various scenarios, including video unicast/multicast with and/or without loss tolerance. Simulation results are also presented to demonstrate the impact of statistical QoS provisionings on resource allocations of our proposed adaptive transmission schemes.

We integrate the effective capacity theory and game-theoretic approach to develop quality-of-service (QoS) driven power-allocation schemes in fading multiple-access channels (MAC). The effective capacity characterizes the capability of the wireless channels to support data transmission subject to the statistical delay QoS constraints, and the game-theoretic approach can efficiently handle the selfish behaviors of game users. In particular, each user allocates power in a selfish and noncooperative yet rational way, and its transmit power will be treated as the background noise to the other peer users. Under the above setup, we formulate the noncooperative power-allocation game, where each game user attempts to maximize its own effective capacity under the average power constraint. We focus on the two-user case and derive the Nash equilibrium and the corresponding power-allocation policy for this game. Furthermore, we show that as the delay QoS constraint becomes extremely loose, our power-allocation game reduces to the existing water-filling game. Also conducted are numerical and simulation analyses which compare the performance between our QoS-driven game based scheme and the water-filling game based scheme.

Effective capacity is a useful technique to characterize the system throughput with statistical delay-constrained quality of service (QoS) guarantees. In this paper, we integrate effective capacity theory into superposition-coding (SPC) based multicast transmission to devise the efficient channel-aware multicasting scheme in wireless networks. Specifically, we propose to optimize the effective capacity for multicast transmissions subject to the specified loss-rate constraint and the statistical delay QoS requirement in terms of the QoS exponent. We use superposition coding in wireless multicast to handle heterogeneous channel fading across multicast receivers, and apply the pre-drop strategy to gain more flexible rate control. Under our proposed framework, we derive the optimal pre-drop strategy and the optimal power/rate allocation for each layer of superposition coding. Simulation analyses present insightful observations on tradeoff between effective capacity and the QoS requirements, and demonstrate the superiority of the SPC-based scheme over the existing time-sharing (TS) based multicast scheme.

Due to the highly-varying wireless channels, deterministic quality of service (QoS) is usually difficult to guarantee for real-time multi-layer video transmissions in wireless networks. Consequently, statistical QoS guarantees have become an important alternative in supporting real-time video transmissions. In this paper, we propose an efficient framework to model the statistical delay QoS guarantees, in terms of QoS exponent, effective bandwidth/capacity, and delay-bound violation probability, for multi-layer video transmission over wireless fading channels. In particular, a separate queue is maintained for each video layer, and the same delay bound and corresponding violation probability threshold are set up for all layers. Applying the effective bandwidth/capacity analyses on the incoming video stream, we obtain a set of QoS exponents for all video layers to effectively characterize this delay QoS requirement. We then develop a set of optimal adaptive transmission schemes to minimize the resource consumption while fulfilling the diverse QoS requirements under various scenarios, including video unicast/multicast with and/or without loss tolerance. Simulation results are also presented to demonstrate the impact of statistical QoS provisionings on resource allocations of our proposed adaptive transmission schemes.

Video multicast over multirate wireless networks imposes great challenges in designing efficient rate adaptation schemes due to the multi-layer hierarchical video coding and heterogeneous fading across multicast receivers. We propose a cross-layer framework for rate selection in video multicast, where different video layers have diverse loss-rate QoS requirements and thus may employ different transmission rates varying with the channel state information (CSI). Specifically, we first propose an orthogonal frequency-division multiple access (OFDMA) - code division multiple access (CDMA) based scheme to avoid feedback collisions while efficiently identifying CSI from all multicast receivers. Second, we develop a queue management strategy such that the transmissions for different video layers can be synchronized. Third, we design the rate adaptation scheme to control receivers' loss rates at each video layer not to exceed the thresholds specified by upper protocol layers. Also conducted are simulation results to show the effectiveness of our designed rate-adaptation scheme.

In mobile multicast over cellular networks, the wireless fading channels for different multicast receivers are usually heterogeneous, from both instantaneous and statistical perspectives. This feature imposes significant challenges in designing efficient multicast schemes to adapt to the channel variations. We investigate the adaptive rate and power control for multicast over generally distributed broadcast fading channels in cellular networks, with the focus on the two-receiver case. Specifically, we aim at optimizing the minimum achievable average good put over all receivers through time-sharing based rate adaptation under the fixed/variable power strategies. By developing an SNR-plane partition based method, we derive the optimal rate adaptation policy for the constant (fixed) power strategy. Moreover, we obtain the optimal power-rate adaptation policy for the variable power strategy by using the Lagrangian duality theory. Simulation results are also presented to show the performance gain obtained from our derived optimal policies.

We propose the adaptive resource allocation schemes for multiuser downlink quality-of-service (QoS) provisionings over broadcast fading channels in time-division (TD) based cellular wireless networks. Specifically, we apply the effective capacity theory to control the service rates with the diverse delay QoS requirements for different mobile users. Subject to the proportional- effective-capacity constraint and the diverse statistical delay-QoS requirements over different downlink users, we formulate the sum effective capacity maximization problem via channel-aware power and time-slot allocation. We decompose the above optimization problem into two sub-problems and then derive the optimal power and time-slot adaptation policy. We also develop a suboptimal scheme called the equal-length TD policy. Simulation results are presented to show the impact of QoS provisionings on the resource allocation across different users and on the network performance.

We investigate cross-layer design based rate control for mobile multicast over broadcast fading channels in cellular networks. Specifically, subject to a statistical loss-rate quality- of-service (QoS) constraint required by upper protocol layers, we aim at maximizing the physical-layer throughput by taking advantage of transmission-rate adaptation based on the channel state information (CSI). Using the concept of convex hull and instantaneous sum goodput - throughput gain (IGTG), we derive the optimal rate adaptation policy for the above maximization problem. Simulation results are also provided to show the significant performance gain achieved by our derived policy over the existing fixed dominating-position (FDP) policy.

We propose a power and constellation-size adaptation scheme for the multicast system employing multilevel quadrature amplitude modulation (MQAM) with discrete constellation sizes, which aims at achieving high multicast goodput over wireless fading channels. Specifically, we formulate the optimization problem to maximize multicast goodput via adaptive transmission-mode selection, including power and constellation-size adaptation, and then solve for the optimal adaptation policy through two steps. We first investigate the goodput variation with the transmission-mode selection and equivalently convert the optimization problem into a concave-like form by introducing the concepts of effective marginal goodput gain (EMGG) and adjacent EMGG. Then, we derive the optimal transmission adaptation policy by using a global adjacent EMGG threshold. Finally, simulation results are provided to show the significant multicast goodput improvement gained by applying the optimal power and constellation-size adaption.

We propose a power adaptation scheme for downlink multiuser communications over parallel fading channels in mobile wireless networks. This model can be applied to the case of time division or frequency division downlink transmission over broadcast channels in cellular networks. Specifically, our proposed scheme aims at optimizing the sum of the weighted effective capacity over all users each with a specified statistical delay quality of services (QoS) constraint, called QoS exponent. Using a two-step method, we derive the optimal power adaptation policy for the above problem. Numerical evaluations are also presented to investigate the impact of the delay QoS on optimizing the weighted sum effective capacity and that on the power allocation across different users.

The paper proposed a power and rate adaptation scheme for multicastings in wireless networks by taking advantage of the broadcast nature of wireless channels. Specifically, the proposed scheme aims at achieving high system goodput by dynamically allocating the transmit power and data transmission rate jointly based on all multicast receiver's channel state information (CSI). Formulating the multicast goodput maximization problem to derive the rate and power adaptation policy, we observe that this goodput maximization is a non-concave optimization problem. By identifying the fact that the optimal power policy has the water-filling form, and introducing the concept of effective marginal goodput gain, the original goodput maximization was converted into an equivalent concave optimization problem, and then obtain its optimal solution. Also presented are the simulation analyses which show the performance improvement gained by our developed optimal power and rate adaptation policy.

We investigate the variations of using the different transmit-diversity techniques for multicast in mobile wireless networks. In particular, we propose and study two different schemes using transmit-diversity for mobile multicast. First, we propose the antenna-beamforming-based mobile multicast scheme which derives the transmit antenna weights by formulating/solving an optimization problem to minimize the average Symbol Error Rate (SER) over all multicast receivers. The simulations results show that our proposed beamforming-based scheme achieves lower SER compared with the existing beamforming-based mobile multicast algorithms which either maximize the instantaneous combined Signal to Noise Ratio (SNR) averaged over all receivers, or maximize the worst-case instantaneous combined SNR among all multicast receivers. Second, we apply the antenna-selection scheme in mobile multicasting to achieve the low average SER. We identify that the antenna-selection scheme is suitable for the scenario where different receivers/receiver-groups in a multicast session, experiencing different channel qualities, need to receive different error-control data. The analyses and numerical results show that the proposed scheme improves the SER performance through selecting antennas with better channel qualities and can flexibly support various antenna configurations.

We investigate adaptive power and rate allocation schemes for multicast over fading channels in wireless networks. Specifically, we take advantage of the superposition coding to achieve high multicast throughput, where the transmitted signal consists of multiple parallel sub-stream signals. Each receiver decodes the sub-stream signals within its decoding capability determined by the received signal to noise ratio (SNR). Under the above architecture, we first formulate the multicast throughput optimization problem to obtain the power allocation policy for each sub-stream signal subject to the total power constraint. Also, we develop a selective retransmission strategy to guarantee all receivers to recover the same data copy without unnecessary data reception. By introducing the concept of compensation ratio, we derive the optimal power allocation policy of the formulated throughput optimization problem for the two-receiver scenario. Numerical results are also presented to show the throughput improvement over the worst-case SNR-dominating (WSD) rate policy, the best-case SNR-dominating (BSD) rate policy, and the constant-rate policy.

To save the precious wireless resources in wireless networks, we investigate cross-layer optimization schemes to reduce wireless resource consumption (WRC) for reliable mobile multicast. We characterize WRC by signal-to-noise ratio (SNR) per information bit and by power-delay product for scenarios without and with delay quality of service (QoS) constraints, respectively. Aiming at decreasing WRC, we develop a cross-layer parameter-selection (CLPS) algorithm, which dynamically adjusts the parameters of the physical layer and the medium access control (MAC) layer according to the variations of multicast group size, transport-layer error-control parameters, and delay QoS constraints. In particular, we first derive the optimal physical-layer parameters, including constellation size and transmit SNR, to minimize WRC for Threshold-0 multicast policy, where the sender transmits data regardless of channel qualities. Then, we develop a suboptimal approach for the selection of MAC-layer multicast threshold to achieve low power-delay product. In addition, we study the effects of pure automatic repeat request (ARQ) and hybrid ARQ-forward error correction (FEC) based error-control schemes on the CLPS algorithm. Also conducted is a set of numerical analyses to show the performance gain achieved through the cross-layer design and the impacts of various system parameters on the WRC optimization.

To save the precious wireless resources in wireless networks, we investigate cross-layer optimization schemes to reduce wireless resource consumption (WRC) for reliable mobile multicast. We characterize WRC by signal-to-noise ratio (SNR) per information bit and by power-delay product for scenarios without and with delay quality of service (QoS) constraints, respectively. Aiming at decreasing WRC, we develop a crosslayer parameter-selection (CLPS) algorithm, which dynamically adjusts the parameters of the physical layer and the medium access control (MAC) layer according to the variations of multicast group size, transport-layer error-control parameters, and delay QoS constraints. In particular, we first derive the optimal physical-layer parameters, including constellation size and transmit SNR, to minimize WRC for Threshold-0 multicast policy, where the sender transmits data regardless of channel qualities. Then, we develop a suboptimal approach for the selection of MAC-layer multicast threshold to achieve low powerdelay product. In addition, we study the effects of pure automatic repeat request (ARQ) and hybrid ARQ - forward error correction (FEC) based error-control schemes on the CLPS algorithm. Also conducted is a set of numerical analyses to show the performance gain achieved through the cross-layer design and the impacts of various system parameters on the WRC optimization.

We investigate the Alamouti scheme with joint antenna selection and power allocation over flat-fading Rayleigh channels. Based on the channel state information (CSI) feedbacks, the transmitter selects the optimal two antennas out of all possible antennas to transmit data using space-time block coding (STBC). Then, the transmitter adaptively allocates transmit power among the selected antennas to minimize the symbol-error rate (SER). We derive the SER as either the closed-form expression or the single-fold finite integral when assuming perfect and delayed CSI feedbacks, respectively. Our results show that when the CSI feedback is perfect, the optimal power allocation is to assign all power to the single optimal antenna, such that the selection-combining (SC)-STBC reduces to the simpler selection-combining (SC) scheme. On the other hand, when taking the CSI feedback delay into account, the SC-STBC scheme with dynamic power allocation ensures the better SER performance than the conventional SC scheme and SC-STBC scheme with equal power (EP) allocation.

We propose an adaptive hybrid ARQ-FEC erasure-correcting scheme for QoS-driven mobile multicast services over wireless networks. The main features of our proposed scheme include: the low-complexity and dynamic adaptation to the variations of packet-loss levels and QoS-requirements. The low complexity is achieved by using the graph-code with linear time-complexity. To support diverse QoS-requirements and improve the error-control efficiency, we develop the two-dimensional adaptive error-control scheme, which dynamically adjusts not only the error-control redundancy, but also the code-mapping structure. By deriving and identifying the closed-form non-linear expressions for the optimal check-node degree and the required error-control redundancy in each adaptation step as the functions of the packet-loss level, we propose the non-uniformed two-dimensional adaptive coding scheme. Using this scheme, we develop an efficient hybrid ARQ-FEC protocol for mobile multicast services with diverse QoS requirements. Also conducted is a set of numerical and simulation results which analyze and compare our proposed adaptive scheme with those using the non-adaptive graph codes, Reed-Solomon erasure codes, and pure ARQ-based approach. The simulation results show that our proposed scheme can efficiently support QoS-driven mobile multicast services and achieve well-balanced error-control redundancy while imposing low error-control complexity and overhead for mobile multicast services over wireless networks.