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.

Semantic communication is an emerging technology to improve the communication efficiency in future networks. In this paper, we propose a multi-AP multi-user adaptive semantic and bit communication framework for image transmission, where each user can communicate with the access points via either semantic communication (SemCom) or bit communication mode. While the peak mean square error (PMSE) is a key parameter to characterize the difference between the original and corresponding recovered images, this metric has no closed form. We propose a data regression approach to approximate the PMSE. Then, the cost functions for the two types of communication modes are designed, where the delay and energy consumption for image transmission and the PMSE for the recovered image are considered simultaneously. Moreover, the computation delay and energy consumption for semantic feature extraction and recovery are also integrated into the SemCom cost function design. Then, an overall user cost minimization problem is formulated to jointly optimize the communication mode decision, user association, channel selection, power control, and computation resource allocation. To solve the formulated problem, we propose an improved particle swarm optimization based semi-cooperative matching (IPSO-SCM) algorithm, where a semi-cooperative matching (SCM) game is established to determine the communication mode decision, user association, and channel selection and the improved particle swarm optimization algorithm is designed to jointly optimize the power control and computation resource allocation in each step of the constructed SCM game. We further prove the effectiveness, convergence, stability, and extensibility of the proposed IPSO-SCM algorithm. Simulation results are provided to demonstrate the superiority of the proposed scheme.

In this paper, we propose an energy-efficient resource allocation scheme for the multi-user multi-channel adaptive semantic-bit communication in emergency networks with the text transmission emergency task. Specifically, we first analyze the semantic energy efficiency for both semantic and bit communication modes. Then, we formulate a network-level semantic energy efficiency maximization problem by jointly optimizing the communication mode decision, channel selection, semantic coding length, power control, and computation resource allocation. To solve the formulated problem, we propose a fractional programming based hierarchical coalitional game (FP-HCG) algorithm. To be specific, we first use the Dinkelbach method to transform the fractional objective function into a parametric form such that the optimal solution of the original problem can be obtained by updating the parameter through iterations. In each iteration, we solve the transformed problem by formulating a two-layer coalitional game, where the coalitional game is adopted to determine the channel selection in the upper layer, and the genetic algorithm is adopted to determine the communication mode decision and resource allocation of each coalition in the lower layer. Simulation results demonstrate that the proposed scheme can efficiently improve the network energy efficiency for emergency communications.

Over-the-air computation (AirComp) is a promising technique that addresses big data collection and fast wireless data aggregation. However, in a network where wireless communication and AirComp coexist, mutual interference becomes a critical challenge. In this paper, we propose to employ an unmanned aerial vehicle (UAV) to enable integrated communication and AirComp, where we capitalize on UAV mobility with alleviated interference for performance enhancement. Particularly, we aim to maximize the sum of user transmission rate with the guaranteed AirComp accuracy requirement, where we jointly optimize the transmission strategy, signal normalizing factor, scheduling strategy, and UAV trajectory. We decouple the formulated problem into two layers where the outer layer is for UAV trajectory and scheduling, and the inner layer is for transmission and computation. Then, we solve the inner layer problem through alternating optimization, and the outer layer is solved through soft actor–critic-based deep reinforcement learning. Simulation results show the convergence of the proposed learning process and also demonstrate the performance superiority of our proposal as compared with the baselines in various situations.

The multi-access edge computing (MEC)-enabled low Earth orbit (LEO) satellite network is a promising approach to meet the growing ubiquitous diverse computation demands around the world. In this paper, a joint task offloading and resource allocation strategy is proposed for hybrid MEC-enabled LEO satellite networks, where two types of MEC tasks, namely delay-sensitive edgy-cloud task and data-and computation-intensive cloudy-edge task, are considered simultaneously. Specifically, we first design the cost functions for the two types of tasks, which take the delay-sensitive feature of edgy-cloud task and data-and computation-intensive characteristics of cloudy-edge task into consideration. Then, an overall terminal cost minimization problem is formulated for task offloading and resource allocation under the communication and computation capability constraints and the service delay requirements. In practice, terminals usually only care about their own costs, but satellites pursue the overall cost minimization of all the served terminals. Thus, considering the individual and collective rationality simultaneously, a two-level hierarchical game is constructed to solve the formulated problem. In the upper level, a hedonic coalition formation game is established, which enables each terminal to make the coalition selection and task offloading decision based on the designed coalition switch rule. In the lower level, the joint channel and power allocation in each coalition is first formulated as a noncooperative game to represent the individual rationality of each terminal. Then, each satellite performs the optimal computation resource allocation to maximize the coalition value with collective rationality. We prove that the Nash equilibrium (NE) for the noncooperative game exists and the coalition partition converges to a Nash stable state. Simulation results are provided to demonstrate the superiority of the proposed strategy.

Millimeter-wave (mmWave) grant-free (GF) access is a promising approach for massive machine-type communication (mMTC) networks to improve the access efficiency and alleviate the shortage of spectrum resources. Due to the lack of authentication, mmWave GF-mMTC networks are vulnerable to the pilot contamination attack (PCA), which can cause severe performance degradation of the channel estimation (CE) and user activity identification (UAI). However, the existing PCA resistance schemes for mmWave GF-mMTC networks perform the CE, UAI, and PCA detection through two separated phases, which will limit the system performance. To solve the problem, we establish a three-dimensional (3-D) transmission model with time-correlated two-dimensional sparsity for mmWave GF-mMTC networks under PCA, where the user activity sparsity, the virtual angular channel sparsity, and the temporal correlation of legitimate user (LU) status are jointly considered. Based on the established transmission model, we develop a 3-D compressive sensing based joint CE, UAI, and PCA detection (3D-CS-JCUPD) scheme. In this scheme, a parallel expectation-maximization vector approximate message passing with multiple measurement vector (Parallel EM-VAMP-MMV) algorithm is proposed to estimate the channel virtual representation (CVR) and the LU status is identified with the aid of different temporal correlation features between LUs and attackers. Moreover, we also develop a location information aided joint CE, UAI, and PCA detection (LIA-JCUPD) scheme to address the situation when attackers and LUs exhibit similar temporal correlations, where the BS utilizes the recorded LU location information to distinguish the LU status. Simulation results show that the developed schemes can achieve substantial performance gains over several reference schemes.

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.

Prompt-based pre-trained language models (PLMs) paradigm has succeeded substantially in few-shot natural language processing (NLP) tasks. However, prior discrete prompt optimization methods require expert knowledge to design the base prompt set and identify high-quality prompts, which is costly, inefficient, and subjective. Meanwhile, existing continuous prompt optimization methods improve the performance by learning the ideal prompts through the gradient information of PLMs, whose high computational cost, and low readability and generalizability are often concerning. To address the research gap, we propose a Dialogue-comprised Policy-gradient-based Discrete Prompt Optimization (DP_2O) method. We first design a multi-round dialogue alignment strategy for readability prompt set generation based on GPT-4. Furthermore, we propose an efficient prompt screening metric to identify high-quality prompts with linear complexity. Finally, we construct a reinforcement learning (RL) framework based on policy gradients to match the prompts to inputs optimally. By training a policy network with only 0.62M parameters on the tasks in the few-shot setting, DP_2O outperforms the state-of-the-art (SOTA) method by 1.52% in accuracy on average on four open-source datasets. Moreover, subsequent experiments also demonstrate that DP_2O has good universality, robustness and generalization ability.

Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Yichen Wang, Shangbin Feng, Abe Hou, Xiao Pu, Chao Shen, Xiaoming Liu, Yulia Tsvetkov, Tianxing He. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

In the upcoming sixth generation (6G) era, dynamic routing relying on link state information update is crucial for global data service in large-scale low-earth orbit (LEO) satellite networks. However, the existing dynamic routing methods use a static link state update scheme where all satellites distribute their link state information with the same fixed period, while the link state of the satellites are different and vary dynamically. This makes it difficult to achieve the balance among various network performance metrics such as link state update accuracy, signaling overhead, network throughput, and energy efficiency. To solve this issue, we propose an adaptive link state update scheme for the LEO satellite network, where each satellite can dynamically adjust its own link state distribution interval according to the observation on the inter satellite links (ISLs). Based on the proposed scheme, we define the information deviation to characterize the accuracy of the link state update and derive the signaling overhead of link state distribution. To improve further the network performance, a multi-objective optimization problem (MOP) is formulated to minimize the information deviation and the signaling overhead simultaneously. By applying the weighted sum method, we convert the formulated MOP into a single-objective optimization problem (SOP). Then, we adopt the distributed reinforcement learning approach and develop the deep Q-network (DQN) algorithm for each satellite to learn its optimal link state distribution decision strategy based on local information. Simulation results demonstrate the superiority of the proposed scheme.

Abe Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, Yulia Tsvetkov. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Recent advances in prompt optimization have notably enhanced the performance of pre-trained language models (PLMs) on downstream tasks. However, the potential of optimized prompts on domain generalization has been under-explored. To explore the nature of prompt generalization on unknown domains, we conduct pilot experiments and find that (i) Prompts gaining more attention weight from PLMs’ deep layers are more generalizable and (ii) Prompts with more stable attention distributions in PLMs’ deep layers are more generalizable. Thus, we offer a fresh objective towards domain-generalizable prompts optimization named ''Concentration'', which represents the ''lookback'' attention from the current decoding token to the prompt tokens, to increase the attention strength on prompts and reduce the fluctuation of attention distribution. We adapt this new objective to popular soft prompt and hard prompt optimization methods, respectively. Extensive experiments demonstrate that our idea improves comparison prompt optimization methods by 1.42% for soft prompt generalization and 2.16% for hard prompt generalization in accuracy on the multi-source domain generalization setting, while maintaining satisfying in-domain performance. The promising results validate the effectiveness of our proposed prompt optimization objective and provide key insights into domain-generalizable prompts.

Downlink channel state information (CSI) feedback typically results in an unacceptable overhead in frequencydivision-duplex (FDD) massive multiple-input multiple-output (MIMO) systems. To deal with this challenge, several deep learning (DL) based CSI compression and recovery approaches have been developed, which follow an auto-encoder architecture and aim at minimizing CSI reconstruction error. Different from the mainstream methodology mentioned above, in this letter, we follow a goal-oriented design philosophy. That is, instead of minimizing the reconstruction error, we train a deep neural network (NN) to compress the CSI such that the precoder using the compressed CSI as input can optimize the downlink transmission performance, i.e., minimize the bit error rate (BER) at the UEs. A two-stage training method is developed to train the NN. Experimental results demonstrate that the proposed scheme outperforms the existing solutions in terms of signal-tointerference-plus-noise ratio (SINR) and BER at terminal users

In this paper, we propose an adaptive semantic and bit communication framework for image transmission, where each user is allowed to select either semantic communication (SemCom) or bit communication (BitCom) mode. As no closed-form expression for the peak mean square error (PMSE) that is a key parameter to characterize the difference between original and corresponding recovered images exists, we propose a data regression approach to approximate the PMSE by the Gompertz function. Due to the different characteristics of the two types of communication modes, the cost functions are designed for both SemCom and BitCom modes, which take the delay, energy consumption, and PMSE into consideration. Specifically, for the SemCom mode, we analyze the required computation resources for semantic feature extraction and recovery and the corresponding computation delay and energy consumption are integrated into the SemCom cost function design. Then, an overall user cost minimization problem is formulated to jointly optimize the communication mode decision, channel selection, and computation resource allocation. To solve the formulated problem, we propose a particle swarm optimization based many-to-one matching game with one-side cooperation (PSO-MMOC) algorithm, where a many-to-one matching game with one-side cooperation is established to determine the communication mode and channel selection and the particle swarm optimization algorithm is adopted to allocate the computation resource in each step of the matching game. We further prove the convergence and stability of the proposed PSO-MMOC algorithm. Simulation results are provided to demonstrate the superiority of the proposed scheme.

Beam Hopping (BH) is a promising approach to support the dynamically varied and non-uniformly distributed ground traffic demands with limited satellite beam resources. However, almost all the existing BH schemes only focus on the overall cell-level traffic demands without considering the transmission demand of each user, which may degrade the system performance. To address this issue, in this paper, a user-level dynamic BH scheme for low Earth orbit satellite networks is proposed, where the user-level real-time traffic demands are integrated into the BH pattern design. Specifically, by considering the user-level transmission demands in the multi-satellite and multi-cell scenario, we formulate an optimization problem that aims to maximize the overall long-term throughput of the network by jointly optimizing the BH pattern and access control (AC) strategy. To solve the formulated problem, we first establish a user-oriented Markov decision process framework, based on which the original long-term optimization problem can be converted to a short-term sum value maximization problem. Then, a deep reinforcement learning assisted enhanced genetic algorithm is proposed to solve the converted short-term optimization problem, where the deep reinforcement learning is adopted to estimate the long-term state-action values and the enhanced genetic algorithm is used to determine the BH pattern and AC strategy with a low complexity according to the estimated state-action values. Simulation results show that the proposed scheme can achieve better performance over existing methods.

To integrate efficiently the emerging massive machine-type communications (mMTC) into the fifth generation (5G) and beyond 5G (B5G) cellular networks, we design a two-hop data aggregation and forwarding mechanism for the machine-type communications (MTC) to coexist with the traditional human-to-human (H2H) communications. Specifically, by sharing the channel resources originally allocated to H2H user equipments (HUEs), the activated MTC devices (MTCDs) first send small-sized data packets to the associated MTC gateways (MTCGs). Then, the MTCGs aggregate the received data packets and forward them to the base station (BS). The data transmission of MTCGs and HUEs are scheduled by the BS such that there is a competition relationship between the forwarding MTCGs and HUEs. To limit the influence of mMTC on the H2H service, we set different scheduling weights for the forwarding MTCGs and HUEs to control the transmission opportunities of the two types of services. Based on the stochastic geometry (SG) theory, we develop an analytical framework to characterize signal-to-interference ratio (SIR) during the intra-group aggregation and MTCG forwarding phase. Using this SIR analytical framework, we derive the data aggregation success probability of each MTCD and the average throughput of each forwarding MTCG. We also conduct simulations to evaluate the performance of the mMTC and H2H service under varying parameter settings, which indicates the trade-off between the system settings of the data aggregation phase and MTCG forwarding phase.

Due to the lack of authentication, millimeter-wave (mmWave) grant-free massive machine-type communication (GFmMTC) networks are vulnerable to the pilot contamination attack (PCA), which will cause serious performance degradation of channel estimation (CE) and active user detection (AUD). However, the existing works towards the PCA detection in the mmWave GFmMTC networks perform the CE, AUD, and PCA detection through two separated phases, which will limit the system performance. To solve the problem, in this paper, we establish a three-dimensional transmission model with time-correlated two-dimensional sparsity for both legitimate users (LUs) and attackers in mmWave GFmMTC networks, where the LU and attacker activity sparsity, the virtual angular channel sparsity, and the temporal correlation of L U activity are jointly considered. Based on the established transmission model, a three-dimensional multiple measurement vector-compressive sensing (MMV-CS) based joint CE and AUD scheme against PCA is proposed. Specifically, we first formulate the joint CE and AUD under PCA as a three-dimensional MMV-CS problem. Then, by utilizing the sparsity of user activity and angular virtual channel, we develop a parallel expectation-maximization vector approximate message passing with MMV (Parallel EM- VAMP-MMV) algorithm to efficiently solve the formulated problem. Simulation results show that the proposed scheme can achieve a substantial performance gain over comparison methods.

The fifth-generation (5G) and beyond networks are expected to accommodate both the original human-to-human (H2H) communication and the emerging massive machine-type communication (mMTC). To enable a harmonious coexistence between the two different types of services, we propose a resource-efficient mMTC/H2H coexistence scheme by jointly considering the random access (RA) and data transmission, where the entire uplink resources are divided for the proposed RA and data transmission procedures. Based on the proposed scheme, we derive the average achievable throughput of the bursty mMTC service and develop a time-nonhomogeneous Markov chain model to characterize the joint state transition of H2H user equipments (HUEs). To tackle the cumbersome Markov model, we approximately decompose the constructed time-nonhomogeneous Markov model into multiple independent Markov chains, where each decomposed Markov chain characterizes one single HUE’s state transition. Then, the decomposed Markov model is transformed into a semi-Markov process and the corresponding steady-state condition is obtained based on the queueing network analysis for H2H service. By approximating the evolution of number of HUEs in different states as M/M/1 queues, we derive the stationary probabilities for the embedded Markov chain of the semi-Markov process and obtain the data transmission success probability of each HUE. Based on the abovementioned analytical framework, we formulate a constrained nonlinear integer programming (NLIP) problem to maximize the mMTC throughput under the constraints of H2H quality-of-service (QoS) stabilization and resource allocation. By adopting the modified particle swarm optimization (PSO) algorithm, we solve the formulated problem and obtain the efficient resource allocation strategy for the mMTC/H2H coexistence. Simulation results demonstrate that the developed analytical framework and modified PSO algorithm achieve close to the optimal mMTC/H2H coexisting performance and can be adapted to various network settings.

With the rapidly increasing number of satellites, the handover scheme design is critically important for the low Earth orbit (LEO) satellite networks, especially for the mega-constellations that include massive number of LEO satellites. However, the existing handover schemes for LEO satellite networks are designed based on the static propagation conditions, which cannot satisfy the dynamic feature of communication environment caused by the mobility of LEO satellites and users. To address this issue, a centralized adaptive intelligent handover scheme for mega-constellations is proposed, where the dynamics of the propagation conditions and limited LEO satellite capacity are taken into considerations. Specifically, we first use a three-state Markov model to characterize the dynamically varying propagation conditions between satellites and users. Then, the Loo model is employed to describe the dynamic land mobile satellite channels. By considering the user transmission rate requirement and the load-balancing demand of satellites, we design the user utility function and formulate an optimization problem that aims to maximize the overall long-term utility of the network. To reduce the handover decision-making complexity, a multi-agent successive hysteretic deep Q-learning algorithm is developed and it can efficiently solve the formulated problem by reducing the state and action space. To reduce the signaling overhead and the computation complexity of the proposed centralized handover scheme brought to the control center, a distributed intelligent handover scheme is further developed, where each user is enabled to independently make the handover decision only based on the local information. Simulation results show that both the proposed centralized and distributed approaches can efficiently improve the network performance over the existing schemes.

Machine-Generated Text (MGT) detection, a task that discriminates MGT from Human-Written Text (HWT), plays a crucial role in preventing misuse of text generative models, which excel in mimicking human writing style recently.The latest proposed detectors usually take coarse text sequences as input and finetune pre-trained models with standard crossentropy loss.However, these methods fail to consider the linguistic structure of texts.Moreover, they lack the ability to handle the lowresource problem, which could often happen in practice considering the enormous amount of textual data online.In this paper, we present a coherence-based contrastive learning model named COCO to detect the possible MGT under the low-resource scenario.To exploit the linguistic feature, we encode coherence information in the form of graph into the text representation.To tackle the challenges of low data resources, we employ a contrastive learning framework and propose an improved contrastive loss for preventing performance degradation brought by simple samples.The experiment results on two public datasets and two self-constructed datasets prove our approach outperforms the state-of-the-art methods significantly.Also, we surprisingly find that MGTs originated from up-to-date language models could be easier to detect than these from previous models, in our experiments.And we propose some preliminary explanations for this counter-intuitive phenomena.All the codes and datasets are open-sourced.1

This paper considers the problem of covert communication threatened by jamming attacks. The transmitter (T) randomly selects a portion of the available channels to transmit uplink signals. The receiver (R) uses maximal-ratio combining to receive the uplink signals. Warden (W) detects the uplink signals in each channel by solving ternary hypothesis testing and jams the channel according to the detection results. In order to jam the activated channel accurately, W formulates his utility function (UF) as maximize the detection probability. His UF subject to a false alarm probability. In order to confuse W, the cooperative jammer (J) sends artificial noise in randomly selected part of all available channels except the uplink activation channel. Minimizing the uplink signal detection probability and maximizing the detection error probabilities are UF of A and J, respectively, both are subject to SNR. We formulate this problem as a two-layer game model. In addition, we analyze the proposed game and obtain the solution of tripartite strategies. Simulation results indicate that the proposed scheme achieves better transmission performance and lower detection probability.

To satisfy the highly heterogeneous requirements of Internet of Things applications for the sixth-generation (6G) networks, the machine-type communication (MTC) aims to support multiple types of services having diverse traffic demands, which will cause significant challenges in the grant-free based channel estimation (CE) and multi-user detection (MUD) scheme design for massive MTC (mMTC) networks. To address this challenge, we develop a multi-state Markov chain based transmission model to characterize the diverse time-varying traffic demands for MTC users, where the temporal correlation of user activity and the data length diversity are jointly exploited. Based on the developed transmission model, a multi-service oriented joint CE-MUD scheme is proposed to realize the efficient CE, user activity identification and data detection. Specifically, we first construct the joint block sparse structure for the transmitted pilot and data signals to fully explore the structured sparsity of the pilot and data symbols. Then, we convert the joint CE-MUD into a maximum a posteriori probability (MAP) problem such that the block sparsity of the transmitted signals and the diverse traffic demands provided by the established transmission model can be efficiently exploited. Moreover, we further develop an adjustable prior probability aided Bayesian sparsity adaptive matching pursuit (APP-BSAMP) algorithm to efficiently solve the formulated MAP problem. In the proposed algorithm, we first adjust the prior user activation probabilities through the approximate message passing (AMP) based detector to reduce the impact of the misestimation of user transmission status. Then, we jointly reconstruct the transmitted pilot and data signals under the Bayesian pursuit framework, where the active user set is obtained by maximizing the posterior probabilities. Simulation results show that the proposed scheme can achieve a substantial performance gain over existing methods.

With the rapid increasing number of the deployed satellites, the handover strategy becomes more challenging for large-scale low-earth orbit (LEO) constellations. In this paper, a distributed satellite handover scheme for large-scale LEO constellations is proposed, which not only takes the handover delay, handover failure, quality-of-service (QoS) requirements of users, and inter-satellite traffic balancing into consideration, but also enables each user to dynamically perform the handover process only with the local information. Specifically, we adopt a shadowed Rice model to characterize the user-satellite channel, which is determined by the elevation angle between the user and satellite. Then, the user utility function is designed, where the user transmission rate requirement and the number of available channels of visible satellites are jointly considered. An overall long-term utility maximization problem is further formulated. By exploiting the independence feature of different satellites and the fact that each user only has finite number of visible satellites, a low-complexity successive deep Q-learning algorithm is developed, which can significantly reduce the dimensions of state spaces and efficiently solve the formulated problem in a distributed manner. Simulation results show that the proposed scheme can achieve better performance over existing methods.

Existing studies on pilot contamination attacks often assume that the enemy has not made any strategic corrections to the detection plan. In this paper, we assume that the intelligent malicious user can obtain legitimate users’ pilot sequence and power in a timely and accurate manner. Based on the acquired pilot information, the attacker adjusts the pilot contamination attack strategy during the reverse training phase and the jamming power during the data transmission phase to improve his eavesdropping performance and reduce the downlink transmission rate of legitimate users. By modeling the defender-attacker interaction as a Stackelberg game, Bob as the leader chooses his pilot training power, while a full-duplex eavesdropper as the follower determines the pilot contamination power according to the observed Bob’s ongoing training signals transmission. In addition, we analyze Bob’s pilot transmission power and secrecy rate under complete and incomplete information conditions. Simulation results show that the proposed scheme can defend against an intelligent active eavesdropper with a higher secrecy rate and utility.

The diverse time-varying transmission demands cause significant challenges in the grant-free based multi-user detection (MUD) scheme design for massive machine-type communications (mMTC) networks. In this paper, we develop a multistate Markov model to characterize the diverse time-varying traffic demands, where the temporal correlation of the user activity and the data length diversity are considered simultaneously. Based on the developed Markov model, a diverse traffic demands oriented MUD scheme is proposed to realize the efficient joint user activity and data detection. Specifically, we first construct the block sparse structure for the transmitted signal to fully exploit the structured sparsity of the data matrix. Then, we convert the MUD into a maximum a posteriori probability (MAP) problem such that the block sparsity of the transmitted signal and the temporal correlation and data length diversity provided by the established Markov model can be efficiently exploited. Moreover, we further develop an intra-block pruning aided Bayesian block orthogonal matching pursuit (IBPA-BBOMP) algorithm such that the formulated MAP problem is efficiently solved. Simulation results show that the proposed scheme can achieve a substantial performance gain over existing methods.

Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes.

As device-to-device (D2D) caching technology allows a number of devices to cache some particular contents, requesters can obtain these contents directly from these neighbor devices rather than the base station (BS) and thus the burden can be efficiently reduced. However, the required time consumptions for caching contents, which may significantly affect the network delay performance, are ignored in the existing schemes. Consequently, a delay-driven caching and sharing strategy is proposed in this paper. Specifically, in the proposed strategy, each D2D device can obtain contents from BS and play as the cache device (CD). Moreover, the consumed time for CDs is integrated into the strategy design. Then, three kinds of delay, which are the delay for CDs to cache contents and the delay for requesters to get the required files from BS and CDs, respectively, are considered simultaneously. We formulate an optimization problem, which aims at minimizing the overall average network delay subject to the successful transmission probability as well as the content cache and request constraints. To solve the formulated complex non-convex problem, the original problem is divided into three subproblems and efficiently solved in an iterative manner. Moreover, as the convergence for solving the three subproblems are proved, the convergence of the developed iterative algorithm can be guaranteed. Simulation results demonstrate that the proposed strategy can efficiently reduce the overall average network delay as compared to the existing schemes.

To accommodate massive machine-type communication (mMTC) in the networks originally designed for human-to-human (H2H) communication, we investigate the resource allocation for the mMTC/H2H coexisting network where the conventional random access (RA) and data transmission procedures are tailored for mMTC. The resource allocation strategy jointly consider the resource allocation of physical random access channel (PRACH) and physical uplink shared channel (PUSCH), aiming to support more MTC users while protecting the quality-of-service (QoS) of traditional H2H communication. A Markov chain is utilized to explicitly model the RA and data transmissions of H2H, and H2H's success probability of data transmission is derived under the analysis of stationary distribution. Then, we formulate a nonlinear integer programming (NLIP) problem which aims to maximize MTC throughput while guaranteeing H2H's success probability of data transmission. By solving the optimization problem with a modified particle swarm optimization method, we obtain the resource allocation strategy that achieves a balance between PRACH and PUSCH in terms of resource efficiency. Simulation results demonstrate the superiority of our proposed resource allocation strategy over traditional LTE strategy in the scenario of mMTC/H2H coexistence.

Machine-type communications (MTC) technology, which enables direct communications among devices, plays an important role in realizing Internet-of-Things. However, a large number of MTC devices can cause severe collisions. As a result, the network throughput is decreased and the access delay is increased. To address this issue, a throughput-oriented non-orthogonal random access (NORA) scheme is proposed for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PAs), multiple MTC devices (MTCDs) choosing the same PA can be distinguished and regarded as a non-orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel for transmissions by multiplexing in the power domain. The Sukhatme's classic theory and the characteristic function approach are adopted to formulate an optimization problem. The aim is to maximize the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. Based on the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. The derived solution can be used to adjust the access class barring factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity suboptimal solution is also developed, which can achieve near-PSO performance under high data rate requirement. Simulation results show that the proposed scheme can efficiently improve the network performance and comparison is made with the existing schemes.

In this paper, we propose a power back-off based non-orthogonal random access (NORA) scheme for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PA), multiple machine-type communication devices (MTCD) choosing the same PA can be distinguished and regarded as a non- orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel (PUSCH) for transmissions by multiplexing in power domain. Then, we adopt the Sukhatme's classic theory and characteristic function to formulate the optimization problem that aims at maximizing the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. By using the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. We further adjust the access class barring (ACB) factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity solution is also developed, which can achieve near PSO-based performance under high data rate requirement. Simulation results show that our proposed scheme can efficiently improve the network performance as compared with the existing schemes.

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.

In this paper, we propose the optimal resource allocation strategy for uplink ultra-reliable low-latency communication (URLLC) networks. Specifically, by employing the quality-of-service (QoS)-aware packet scheduling mechanism which is built upon the theory of maximum achievable rate under finite blocklength regime, the QoS requirement is described for uplink URLLC transmissions. Then, we formulate the non-convex optimization problem which aims at minimizing the total transmit power consumption of the URLLC network while meeting the QoS requirement as well as the channel assignment and transmit power constraints. By adopting the bipartite weighted graph theory, we design the ORA-KMM algorithm to obtain the optimal joint power and channel allocation strategy. Moreover, two low-complexity suboptimal algorithms, namely BCCG and SSG-LDF, are developed and the impact of spatial diversity on transmission reliability and total transmit power consumption are also analyzed. Simulation results show that our proposed optimal resource allocation strategy can achieve better performance as compared to the developed suboptimal schemes.

Due to the spectrum-sharing feature of cognitive radio networks (CRNs) and the broadcasting nature of wireless channels, providing quality-of-service (QoS) provisioning for primary users (PUs) and protecting information security for secondary users (SUs) are two crucial and fundamental issues for CRNs. Consequently, in this paper, we establish a unified QoS and security provisioning framework for wiretap CRNs. Specifically, different from the widely used deterministic QoS provisioning method and information-theoretical security protection approach, our established framework, which is built on the theory of statistical queueing analysis, can quantitatively characterize the PU's QoS and the SU's security requirements. By adopting the theories of effective capacity and effective bandwidth, we further convert the QoS and security requirements to the equivalent PU's effective capacity and SU's effective bandwidth constraints. Following our developed framework, we formulate the nonconvex optimization problem, which aims at maximizing the average throughput of SU subject to PU's QoS requirement, SU's security constraint, as well as SU's average and peak transmit power limitations. Then, we adopt the techniques of convex hull and probabilistic transmission to convert the original nonconvex problem to the equivalent convex problem and obtain the optimal power allocation scheme through the Lagrangian method. Moreover, we also develop a fixed power allocation scheme which is suboptimal but has low complexity. The simulation results are also provided, which demonstrate the impact of the PU's QoS and the SU's security requirements on SU's throughput as well as the advantage of our proposed optimal power allocation scheme over the fixed power allocation scheme and the conventional security-based water-filling policy.

In this paper, we propose a QoS-driven subchannel and power allocation scheme for security-aware D2D underlaying cellular networks. Specifically, we aim at maximizing the delay QoS constrained average sum throughput of D2D users while meeting cellular users' QoS requirements, D2D users' information security demands, as well as the subchannel and power allocation constraints. To solve our formulated integer-mixed nonconvex problem, we develop a two-step subchannel and power allocation scheme. In particular, by introducing power-splitting variables, the subchannel allocation and power allocation can be decoupled and independently optimized, where the subchannel allocation scheme is based on the classic water-falling algorithm and the power allocation problem is solved via the convex approximation method. Simulation results show that our proposed scheme outperforms the existing method.

In this paper, we establish a unified Quality-of-Service (QoS) and security provisioning framework for wiretap cognitive radio networks (CRN) by employing the theories of statistical queueing analysis, effective bandwidth, and effective capacity, which can quantitatively characterize the QoS and security requirements. Based on our developed framework, we formulate the nonconvex optimization problem that aims at maximizing the average throughput of secondary user (SU) subject to PU's QoS requirement, CRN's security constraint, as well as SU's average and peak transmit power limitations. By using the techniques of convex hull and probabilistic transmission, we convert the original nonconvex problem to the equivalent convex problem and then obtain the optimal power allocation via Lagrangian method. Simulation results demonstrate the impact of PU's QoS and CRN's security requirements on SU's throughput as well as the advantage of our proposed scheme over the fixed power allocation and the conventional security-based water-filling policy.

A pilot spoofer can paralyze the channel estimation in multi-user orthogonal frequency-division multiplexing (OFDM) systems by using the same publicly known pilot tones as legitimate nodes. This causes the problem of pilot authentication (PA). To solve this, we propose, for a two-user multi-antenna OFDM system, a code-frequency block group (CFBG) coding-based PA mechanism. Here multi-user pilot information, after being randomized independently to avoid being spoofed, is converted into activation patterns of subcarrier-block groups on code-frequency domain. Those patterns, though overlapped and interfered mutually in the wireless transmission environment, are qualified to be separated and identified as the original pilots with high accuracy, by exploiting CFBG coding theory and channel characteristic. Particularly, we develop the CFBG code through two steps, i.e., 1) devising an ordered signal detection technique to recognize the number of signals coexisting on each subcarrier block, and encoding each subcarrier block with the detected number and 2) constructing a zero-false-drop code and block detection-based code via k-dimensional Latin hypercubes and integrating those two codes into the CFBG code. This code can bring a desirable pilot separation error probability, inversely proportional to the number of occupied subcarriers and antennas with a power of k. To apply the code to PA, a scheme of pilot conveying, separation, and identification is proposed. Based on this novel PA, a joint channel estimation and identification mechanism is proposed to achieve high-precision channel recovery and simultaneously enhance PA without occupying extra resources. Simulation results verify the effectiveness of our proposed mechanism.

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.

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.

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.

Security is an issue of paramount importance, yet is it a significant challenge for wireless communications, which becomes more intricate when facing a full duplex (FD) active eavesdropper capable of performing eavesdropping and jamming simultaneously. In this paper, we investigate the physical layer security issue in the presence of an FD active eavesdropper, who launches jamming attacks to further improve the eavesdropping. The jamming, however, also results in self-interference at the eavesdropper itself. This security problem is formulated within a hierarchical game framework where the eavesdropper acts as the leader and the legitimate user is the follower. In particular, we first investigate the follower's secrecy rate maximization problem and derive the optimal legitimate transmission strategy. Then, the leader's wiretap rate maximization is expressed as a mathematical program with equilibrium constraints (MPEC). Leveraging the concavity of the follower's problem, we transform the MPEC problem into a single-level optimization and obtain the jamming power allocation strategy by applying the primal-dual interior-point method. Moreover, we analyze the situations where only partial channel state information is available at the legitimate user and the corresponding impacts on the game. Finally, we present extensive simulation results to validate our theoretical analysis.

Due to the dynamic arrival of data packets and time-varying channel states, secure transmission opportunities will be wasted when there is no confidential message to transmit, and data packet transmission can be delayed while waiting for the next available secure transmission opportunity. In order to seize every precious secure transmission opportunity and reduce the data packet waiting time, we propose a secure transmission protocol in which the under-utilized secure transmission opportunities can be exploited to transmit key packets, and these key packets will encrypt the confidential messages in the subsequent transmissions. Following the above-mentioned principle, we first apply this protocol to a single-input single-output network, and optimally allocate the secure transmission opportunities for the key and data transmissions, such that the secrecy rate is maximized under the constraints of the minimum energy efficiency and queue stability requirements. In addition, the packet delay for the proposed protocol is investigated using a generating function approach and a closed-form expression of the packet delay is derived. Then, we extend our work to the multiple-input single-output network and utilize the key queue as well as the artificial noise to protect the confidential messages. Similarly, we also optimally allocate the transmission opportunities for the key and data transmissions to maximize the secrecy rate and study the data packet delay performance. Since all secrecy transmission opportunities are utilized in the proposed protocol, the full secrecy rate is achieved. Numerical results are demonstrated to verify the performance superiority of the proposed protocols when compared with the other key encrypted schemes in terms of the data packet delay and the secrecy rate.

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.

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.

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.

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.

Device-to-device (D2D) communication is proposed as a vital technique to enhance system capacity in cellular networks, which requires efficient interference modeling and managements to improve spectral efficiency. In practice, even when two wireless connections share the same resources, the interference between them may not always exist if there is no conflict at packet-level transmissions. To explore the real effect of interference, in this paper, we establish a cross-layer model for D2D communications underlaying cellular network and derive the closed-form stable throughput region. Then, we formulate an optimization problem to obtain the maximal achievable packet rate for cellular link, which determines whether the D2D pair can share the same resources with a specific cellular link. By dividing the original optimization problem into several simplified subproblems, the optimal solution can be calculated with low complexity. Subsequently, our model is extended to a generalized scenario where multiple D2D pairs share the same resources with one cellular link. Due to the complexity of obtaining closed-form expressions on stable throughput regions, we propose an algorithm to determine whether the transmissions of the cellular link and the multiple D2D pairs can satisfy the QoS requirements simultaneously. Furthermore, a low-complexity dynamic admission control strategy is introduced to deal with the admission process for new D2D requests. As a consequence, the cellular spectrum can allow access of many more D2D pairs than what the conventional model can. The significant improvements are verified by numeral simulations.

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 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.

In this paper, an amplify-and-forward two-way relay system with unknown carrier frequency offsets (CFOs) is considered. A double differential transmission scheme is proposed to achieve successful two-way relaying transmission without any CFOs information. The average symbol error rate (SER) performance of the proposed scheme is analyzed and a closed-form upper bound of the average SER is derived. Simulation results are provided to validate the proposed scheme.

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.

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.

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.

Aiming at reducing the outage probability of primary users (PU) and obtaining spectrum resources for secondary users (SU), in this paper, we proposed a joint subcarrier and power allocation scheme (JSPA) for reciprocally-benefited spectrum sharing with SUs cooperating with PUs. In JSPA scheme, SUs adopt the decode-and-forward (DF) relaying protocol to help PUs in a two-stage way. Meanwhile, a fraction of unallocated licensed spectrum is allocated for the secondary transmission in every stage. However, if the two-stage cooperation still cannot satisfy PUs' outage quality-of-service (QoS) requirement, SUs then switch to the access mode to entirely capture the licensed spectrum. Our proposal is to maximize the average transmission rate of SUs through joint allocation of subcarriers and power. The closed-form expressions about the outage probability and average transmission rate of both PUs and SUs are derived. Simulation results show that compared with the conventional cognitive cooperation schemes, the average transmission rate of SUs improves.

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.

In order to achieve better statistical Quality-of-Service (QoS) provisioning for cognitive radio networks (CRN), in this paper, we develop a hybrid underlay/overlay transmission mode for CRNs. Specifically, by applying the theory of effective capacity and taking PN's activity statistics into consideration, we first analyze the maximum achievable throughput of the CRN under two dominant transmission modes, namely underlay and overlay, respectively, and provide efficient algorithms to derive optimal transmission strategies for the two modes. Following the analyses, we then propose a hybrid underlay/overlay transmission mode, through which the cognitive users' QoS requirements can be better guaranteed and network throughput can be further improved. Moreover, we analyze the optimal transmission strategies for both underlay and overlay modes under two limiting cases. Analyses indicate that 1) for the loose QoS requirement, optimal transmission strategies for both underlay and overlay modes become the water-filling algorithm; and 2) for the stringent QoS requirement, the cognitive user will transmit with constant rate. Furthermore, the impact of imperfect channel estimations on our proposed transmission mode is discussed. Simulation results are provided to demonstrate the impacts of delay QoS requirements and PN's activity statistics on maximizing the delay-constrained throughput for both underlay and overlay modes and verify the effectiveness of our proposed transmission mode. Moreover, for the overlay mode, we observe that 1) a unique optimal sensing time exists under the given QoS constraint; and 2) the optimal sensing time surprisingly increases as the QoS constraint gets more stringent.

In this letter, we propose a power allocation scheme for statistical quality-of-service (QoS) provisioning in multi-relay decode-and-forward (DF) cognitive networks (CN). By considering the direct link between the source and destination, the CN first chooses the transmission mode (direct transmission or relay transmission) based on the channel state information. Then, according to the determined transmission mode, efficient power allocation will be performed under the given QoS requirement, the average transmit and interference power constraints as well as the peak interference constraint. Our proposed power allocation scheme indicates that, in order to achieve the maximum throughput, at most two relays can be involved for the transmission. Simulation results show that our proposed scheme outperforms the max-min criterion and equal power allocation policy.

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.

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.

SUMMARY In this paper, we propose a cross‐layer based power allocation scheme with statistical delay QoS guarantees for the cognitive (secondary) amplify‐and‐forward relay link, which coexists with one primary link by sharing particular portion of the spectrum. Specifically, our derived power allocation scheme aims at maximizing the effective capacity of the cognitive relay link, which can be seen as the maximum arrival rate supported by the system under given QoS constraints. In our work, not only the average total transmit power and average interference power constraints are considered, but also the impact of the interference from the primary link to the cognitive relay link is taken into consideration. Simulation results show that the effective capacity of the cognitive relay link varies with the statistical QoS constraints. In particular, the stringent QoS constraint will cause low effective capacity. Moreover, we observe that the average total transmit power and average interference power are two important parameters, which will obviously impact the performance of the cognitive relay link. In addition, we find that the transmission of the primary link will significantly affect the performance of the cognitive relay link, such that a larger transmit power of the primary link will cause the performance degradation of the cognitive relay link. Copyright © 2011 John Wiley & Sons, Ltd.

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.

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.