The rapid development of the Internet of Things (IoT) has highlighted the critical importance of security and privacy in cognitive cities. In this context, radio frequency fingerprinting (RFF) identification has emerged as an excellent authentication scheme that provides intelligent and efficient identification in IoT systems. By leveraging RFF, we can improve the security and privacy of cognitive cities while also enhancing their operational efficiency. The RF nonlinear features are unique and unchanging, operating at the hardware level. This attribute renders them amenable to sufficient learning through convolution neural networks (CNNs), which have demonstrated remarkable identification accuracy. Nonetheless, CNNs suffer from a lack of strong interpretability and necessitate vast quantities of training data. Additionally, the enormous amount of data required for training imposes greater demands on computing resources, which are often inadequate in IoT. Moreover, traditional training schemes employ centralized datasets, which cannot ensure corresponding privacy. More recently, federated learning and fractional wavelet scattering network have been proposed to solve the problems above. To address this issue, we in this paper proposed a deep federated radio fingerprinting based on fractional wavelet scattering network (DFFNet) which can acquire the subtle features from non-stationary signals. The advantage of DFFNet is that the federated learning is applied to achieve privacy preserving during the learning process. Meanwhile, fractional wavelet is suitable for non-stationary signal’s features extraction with high interpretability. The representative experiment results demonstrate that hybrid federated framework DFFNet achieve about 99.1% identification accuracy under practical application.

In emergency rescue scenarios, unmanned aerial vehicle (UAV) communications with reconfigurable intelligent surfaces (RIS) is a way to enhance the communications link. However, the key challenges lie in the acquisition of channel information and the design of beamforming due to the limited energy storage and high density integrated antenna array. To solve this problem, we propose an energy minimization (EM) based beam training scheme to optimize the energy consumption of the communication system. Specifically, we consider the RIS assisted UAV communication system with emergency rescue. Considering the constraints of RIS phase-shift, system communication rate and energy efficiency, we propose an optimization problem to minimize the system transmission power, and obtain the optimal solution. The simulation results show that the proposed optimization algorithm can ensure the minimum communication requirements and reduce the transmission power of the system.

The reduced-capability new radio (NR RedCap) was introduced in 3GPP Rel-17 to cater to the use cases that are not yet best served by current NR specifications, such as smart grid and industrial wireless sensors. For the grant-free access system in smart grid, the resistance to impulse noise is a key issue. By using fractional low-order covariance and constant modulus based tensor decomposition, this paper skillfully enables user identification in this scenario while suppressing the effect of impulse noise. The proposed scheme uses spread spectrum signal as the pilot signal. And the user identity is represented jointly by the spread spectrum sequence and information codes. In this condition, we start by transforming the pilot signals into a tensor. The fractional low-order covariance is then used to suppress the impulse noise, and the constant modulus is used to improve the performance of the algorithm during the iterative process of tensor decomposition. Finally the sensor identity is confirmed by the decomposition result. Simulation results show that the proposed scheme can greatly improve the performance of user identification under impulse noise channel. Specifically, the identification rate of the proposed algorithm valued 99.815% outperformed that of AMP valued 89.1471% when generalized signal-to-noise ratio GSNR = 0 dB. In addition, the proposed scheme can also correctly estimate the channel gain from the sensors to the base station in impulsive noise environment.

For the grant-free random access system in the Internet of Thing (IoT) scenario, the recovery of the pilot sequence and the identification of the IoT device is a crucial issue. Contrapose the problem that the existing grant-free access schemes cannot accurately recover the pilot sequence in the intensive industrial zone with impulse noise, this paper proposes to use spread spectrum signal as pilot signal and proposes an estimation algorithm based on joint k-means and M estimation accordingly. This algorithm dynamically suppresses the influence of noise with adaptive weighted function according to the estimated noise energy in the iterative process. First, the received signal is segmented to obtain samples. Second, the samples are clustered using the K-means algorithm. In the iterative process of the algorithm, cluster centers are used to estimate the energy of signal noise. According to the estimation result of the noise energy, the adaptive weighted function is used to dynamically update the cluster centers and the similarity between samples and cluster centers. Finally, assigning +1 or −1 to the samples according to the clustering results, and then the estimation of pseudo-code sequence (PN sequence) is realized while impulse noise is suppressed. Simulation results show that the proposed algorithm can greatly improve the performance of PN sequence estimation under impulse noise channel. The bit error ratio (BER) of the proposed algorithm valued 0.008 outperformed that of EVD valued 0.3 when the generalized signal-to-noise ratio (GSNR) is −4dB. In particular, the proposed algorithm has better performance when the noise distribution has heavier tails, which is different from traditional algorithms.

With the deployment of 5G and large-scale Internet of Things (IoT), the equipment identification and authentication scheme based on RF fingerprint shows unique advantages in terms of lightweight and uniqueness. However, traditional RF fingerprint identification scheme based on machine learning has the disadvantages of high computational complexity and low accuracy. Meanwhile, this scheme requires large-scale labeled datasets to realize network learning, and due to the nonlinearity of the cascade, we can not well understand the properties and optimal configurations of these networks. To solve above problems, in this paper, we propose an RF fingerprint identification method based on wavelet scattering network in the small-scale dataset. Specifically, in this method, we first design a hybrid network model of wavelet scattering network combined with deep residual network (Resnet18). Then, since one of the main problems of RF fingerprinting is the diversity of signal information at different time scales, we choose to use the construction of scattering network based on wavelet basis to complete the accurate feature decomposition of the nonlinear features of RF fingerprint. These features are stable against deformations and retain high frequency information for identification. Finally, we can use the obtained detailed features to realize the accurate identification of RF radiation source equipments. The experimental results show that our scheme can better suppress the interference of noise in the signal, improve the feature representation ability, and it can obtain higher identification accuracy than other comparison schemes.

Deep learning (DL) based identification systems are deemed as the scalable, accurate and lightweight authentication mechanisms to handle the security provisioning of massive Internet of Things (IoT) systems by leveraging the hardware-level radio frequency fingerprints. However, the conventional DL-based methods perform poor generalization in the practical time-varying signal-to-noise ratio (SNR) scenarios. In this paper, we propose a data augmentation and contrastive learning based radio frequency fingerprinting (DACL-RFF) with the joint optimization of samples agreement and labels agreement. First, we expand the SNR variations of training dataset with data augmentation, and then we propose a novel framework of contrastive learning. Specifically, we employ the original samples as the supervisory information of augmented samples and the label information of original samples is leveraged to guide the training process. Experimental results demonstrate that our proposal can increase the average accuracy by up to 51.74% in comparison with the case of none augmentation as the conventional DL-based methods. Additionally, we show that our framework of contrastive learning yields 5.27% improvement compared to the case of data augmentation with supervised learning.

In this article, we focus on the joint design of channel training and data transmission for secure ultrareliable and low-latency communications (URLLCs) in mission-critical Internet of Things (IoT) scenarios, e.g., intelligent transportation and remote control. Specifically, we consider a multiple-input multiple-output multiantenna eavesdropper (MIMOME) system, and the role of artificial noise (AN) for securing URLLC in this system is studied. In the channel training phase, we use the two-way discriminatory channel estimation (DCE) protocol with AN injection to suppress the channel estimation accuracy at eavesdropper. Meanwhile, the AN-assisted secrecy beamforming scheme is adopted to mask the confidential data signals. Following by the security enhancements above, we provide a quantitative definition of achievable effective secrecy rate (AESR) to measure the performance of our URLLC system with imperfect channel state information (CSI) and short-packet feature. Then, a nonasymptotic closed-form lower bound of AESR is provided to make the numerical calculation tractable, and the asymptotic system performance in the high-SNR regime is also studied to gain a comprehensive insight. Based on the cyclic coordinated search method, we propose an iterative resource allocation algorithm to maximize the AESR of our system, where the blocklength and transmit power assigned to the reverse/forward pilots, confidential data signals, and AN are jointly optimized. In addition, numerical results reveal the AESR performance of our URLLC system for different system parameters, and demonstrate the convergence and superiority of our proposed algorithm.

Symbiotic communications have been deemed as a critical technology for Internet of Things (IoT) communications owing to its high spectrum and energy efficiency. Reconfigurable intelligent surface (RIS), which can tune wireless transmission channels by manipulating incident waves through the corresponding electromagnetic elements, is a promising enabler of various symbiotic communications scenarios in IoT. However, when the full electromagnetic elements of RIS are activated, system capacity will be improved and energy efficiency will be reduced inevitably, also with undesirable power consumption. To address this issue, an intelligent dynamic subarray RIS framework based on deep reinforcement learning (DRL) has been proposed. The key idea is to divide RIS electromagnetic elements into several groups and optimize power amplifier factor, independent phase shifts to improve the system energy efficiency under the premise of user’s basic requirements. In particular, we formulate a hybrid optimization problem of RIS subarray partition and beamforming to maximize system energy efficiency. It can be proved that this hybrid optimization is a mixed nonconvex integer programming problem. To solve this issue, we proposed a comprehensive DRL framework, including two parts, i.e., 1) a Markov decision process (MDP) to model the subarray partition design, amplitude, and phase shifts of RIS and 2) an active RIS subarray optimization scheme based on deep deterministic policy gradient. Numerical results have demonstrated that, compared with the conventional fully-connected RIS, the system energy efficiency can be significantly improved.

Radio frequency fingerprints (RFF) identification is a critical enabling technology to support rapid and scalable device identification in long rang (LoRa) based Internet of Things (IoT). In recent years, the identification precision of RFF has been significantly improved by leveraging artificial intelligence (AI) technologies to deeply exploit RFF features which are hardware-level, unique and resilient. However, traditional AI technologies lack strong interpretability, require massive amounts of training data and occupy huge computing resources. To address above challenges, we in this paper propose a deep fractional scattering network (DFSNet) to extract the RFF features hidden in non-stationary LoRa chirp signal through linear translation-variant multiscale fractional wavelet filters. Due to the fractional-domain deformation stability in DFSNet, the influence of noise on feature extraction can be reduced to the greatest extent by fractional transformation. Firstly, we apply DFSNet to build a hybrid RFF identification interpretability framework where the scattering coefficients of input can be calculated and characterized. Ben-efiting from the application of fractional wavelet transform, we can clearly explain the features represented by each coefficient. Then, the robustness characteristic of the fractional deformation is analyzed. Finally, experiment results show that our proposed hybrid DFSNet can achieve up to about 98.5% recognition accuracy rate with only about 5000 LoRa practical training samples per device.

Fountain code, as a linear random code without a fixed bit rate, can be introduced into multi-path transmission control protocol (TCP) to improve throughput and reduce the bottleneck effect caused by path quality diversity. However, the multiple-hop transmission of data and huge feedback delay of acknowledge character (ACK) will lead to excessive transmission of fountain-coded packets on multiple paths, posing serious privacy concerns. To tackle with this problem, we propose a secure cross-locking enabled multi-route fountain coding scheme in this paper which can utilize the diversity of transmission quality among different paths to improve the security of the multi-route fountain code transmission system. Specifically, the fountain-coded packet data on the superior path of the legal receiver is employed to protect the fountain-coded packets on the inferior path, forming a cross-interlocking structure between multiple paths. This design can reduce the probability that the eavesdropper receives enough fountain-coded packets and decode successfully. Besides, the data received by the legal receiver is used to support implicit transmission of control information, further reducing the interception probability of eavesdropper. The simulation results finally verify the effectiveness of this scheme against eavesdropping attack.

Ambient backscatter communications, a promising technique to realize massive machine type communication (mMTC), has recently attracted great attentions, due to its spectrum-and-energy-efficient characteristics. In ambient backscatter communications, multiple tags will transmit signals asynchronously to the target reader, which however imposes huge challenges to the radio access and signal detection at the reader. To tackle these problems, we propose an independent component analysis (ICA) based blind signal separation, identification and detection scheme. Specifically, each of tag signals is randomly and independently encoded to reduce the collision of the tags. Then a novel ICA algorithm is applied at the reader to separate, identify and detect the signals. The results show that the proposed scheme, compared with existing schemes, provides higher detection accuracy with lower cost even under a large number of tags.

The development of 6G communication is now putting higher performance requirements on the mobile satellite system. How to achieve comprehensive coverage by adding satellites to mobile communication system has become a popular topic on integrated satellite-ground network. However, using traditional methods to analyze coverage performance is limited by the topology of the satellite constellation. In this paper we use the stochastic geometry to model the LEO constellation. The stochastic geometry analysis method weakens the influence of constellation topology and provides a new representation of the coverage performance characteristics. This paper considers the models of beam coverage angle and atmospheric attenuation on coverage performance and proposes a new interference simplification method. It provides a new tool for the future optimization analysis of coverage performance of LEO satellite constellation and gives a new general expression for the coverage probability. The simulation shows that the coverage analysis model established in this paper can clearly represent the characteristics of the variation of the coverage probability with different parameters, which is conforms to the changing trends of the actual satellite constellation. For example, it can obtain the number of satellites with optimal coverage probability at different orbital altitudes.

Beam hopping technology can flexibly allocate system resources to achieve on-demand coverage in satellite communication system. However, most of the current research mainly focused on the improvement of system capacity while ignoring the optimization of user delay and quality of service (QoS). In this paper, we propose a maximal user service weight gain (maxUSWG) resource allocation algorithm in beam hopping satellite communication system. Specifically, the user service weight gain is determined by the current traffic demand and delay sensitivity of the cell. We propose to use the combination weights method for multiple attribute decision making based on maximizing deviations to calculate the weight of traffic demand and delay sensitivity. After the weighted decision attributes are obtained, the user service weight gain can be combined and evaluated. In order to maximize the user service weight gain, we allocate resources to the cell with larger weight gain until the power is exhausted or the number of working beams reaches a certain threshold. The simulation results show that the algorithm maxUSWG can effectively improve the QoS, increase the system throughput and improve the resource utilization.

Lightweight device authentication is a critical aspect in edge computing to guarantee the rightness of edge device identities and services. Radio frequency fingerprinting (RFF) is such an enabling technology able to provide robust and affordable security by employing the unique device and channel features which are usually extracted via machine learning (ML) method. The challenge is how to realize strong interpretability and support sufficient generalization ability under non-stationary channel characteristics. To solve this, we in this paper propose a novel hybrid network named FWSResNet which exploits fractional wavelet scattering transform and residual neural network to deal with the device and channel features subtlety. In particular, the proposed FWSResNet uses the scattering network based on fractional domain wavelet transform to extract the low and high-frequency features of the input signal through multiscale fractional wavelet. We find that this design can be robust to non-stationary signals and can extract the key features of noise signals. We also present a comprehensive theoretical analysis of the performance of FWSResNet under non-stationary signal distortion. Finally, we evaluate the hybrid network under largescale long term evolution (LTE) data in the practical application scenario and show that our proposed FWSResNet can achieve 93% recognition accuracy rate with only 280 training samples per device, and achieve up to 99.5% when training samples increase to 4200 per device.

In this paper, we investigate the issue of secure image transmission over point-to-point flat-fading channels under an eavesdropper. We propose an adaptive noise aggregation (ANA) scheme to worsen the eavesdropper’s reception by exploiting the inherent noise of wireless channels. Particularly, data packets to be sent are equally separated into multiple groups which are then correlated with each other for transmission in different time slots. An ANA scheme is proposed to encode even and odd packets in such a way that the correlation between data packets can be adaptively adjusted according to the average signal-to-noise ratio (SNR) fed back by the legal receiver. We prove that this scheme can significantly improve the receiving signal quality of legitimate receiver under low SNR, while paralyzing the decoding process of eavesdropper. Finally, simulation results show the superiority of our scheme over existing noise aggregation schemes.

With the ever-increasing growth of wireless communication technologies and the proliferation of the Internet of Things (IoT), intelligent authentication systems to distinguish legitimate devices are of vital importance. These years, deep learning based authentication algorithms have achieved considerable precision by leveraging radio frequency (RF) fingerprints. However, these methods depending on massive labeled data are difficult to apply on large-scale devices identification. In this paper, we propose a novel method using semi-supervised deep learning employing RF fingerprinting with meta pseudo time-frequency labels to improve identification performance in small-scale labeled datasets. We demonstrate how the scale of datasets and the proportion of labeled data influence the accuracy of identification by analyzing a dataset of 40 GB real Long-Term-Evolution (LTE) mobile phone’s raw signals. Experimental results show that compared with the non-convergence of traditional supervised learning with 100 labeled data, our method can achieve the authentication accuracy of 99.86% with the same labeled data. Moreover, when using the same scale of training datasets and labeled half, our approach could obtain authentication accuracy higher than traditional supervised learning. And even 1% labeled data of 900 training data, this method can still obtain the accuracy of 91.25%.

Supporting the ultra-reliable and low-latency communications (URLLC) in 5G wireless networks is a prerequisite for the deployment of emerging mission-critical IoT and tactile Internet applications. However, the stringent latency and relia-bility constraints of URLLC can hardly be satisfied in the grant-based access protocol, and the grant-free transmission manner integrated with short frame structure is regarded as a potential solution for supporting the uplink transmission of URLLC. In this paper, we aim to investigate the reliability performance of URLLC with proactive grant-free uplink transmission manner. Specifically, we first model the grant-free transmission behaviour of each device as a discrete-time Markov process, and then derive the mathematical expression of overall packet loss probability to characterize the reliability performance. Through numerical results, we obtain some constructive conclusions that offers some insights for the grant-free uplink transmission design of URLLC.

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

Nowadays, a large number of intelligent devices and smart sensors are being connected by various device identification and/or authentication protocols to satisfy various requirements of 5G services. However, how to identify devices by hardware-level radio frequency (RF) fingerprints of real mobile phones has been rarely researched. In this paper, we propose a novel deep learning (DL) based RF fingerprinting ResNet (RFFResNet) to identify different real mobile phones precisely by employing RF fingerprints hidden in wireless signals. Specifically, we quantitatively show how identification accuracy is influenced by channel conditions, noises, the scale of training data and network parameters. We also evaluate the proposed RFFResNet by using a dataset of 220GB long term evolution (LTE) simulation raw time data and a dataset of 25GB real mobile phone's raw time signals. Experiment results show that our RFFResNet can achieve about 95%-99% identification accuracy in real LTE application scenario and show great superiority compared with other existing DL model, such as ResNet18-1D, ResNet34-1D and VGG16-1D.

Secure wireless access in ultra-reliable low-latency communications (URLLC), which is a critical aspect of 5G security, has become increasingly important due to its potential support of grant-free configuration. In grant-free URLLC, precise allocation of different pilot resources to different users that share the same time-frequency resource is essential for the next generation NodeB (gNB) to exactly identify those users under access collision and to maintain precise channel estimation required for reliable data transmission. However, this process easily suffers from attacks on pilots. We in this article propose a quantum learning based nonrandom superimposed coding method to encode and decode pilots on multidimensional resources, such that the uncertainty of attacks can be learned quickly and eliminated precisely. Particularly, multiuser pilots for uplink access are encoded as distinguishable subcarrier activation patterns (SAPs) and gNB decodes pilots of interest from observed SAPs, a superposition of SAPs from access users, by joint design of attack mode detection and user activity detection though a quantum learning network (QLN). We found that the uncertainty lies in the identification process of codeword digits from the attacker, which can be always modelled as a black-box model, resolved by a quantum learning algorithm and quantum circuit. Novel analytical closed-form expressions of failure probability are derived to characterize the reliability of this URLLC system with short packet transmission. Simulations how that our method can bring ultra-high reliability and low latency despite attacks on pilots.

Based on the concept of constructive interference (CI), multiuser interference (MUI) has recently been shown to be beneficial for communication secrecy. A few CI-based secure precoding algorithms have been proposed that use both the channel state information (CSI) and knowledge of the instantaneous transmit symbols. In this article, we examine the CI-based secure precoding problem with a focus on smart eavesdroppers that exploit statistical information gleaned from the precoded data for symbol detection. Moreover, the impact of correlation between the main and eavesdropper channels is taken into account. We first modify an existing CI-based precoding scheme to better utilize the destructive impact of the interference. Then, we point out the drawback of both the existing and the new modified CI-based precoders when faced with a smart eavesdropper. To address this deficiency, we provide a general principle for precoder design and then give two specific design examples. Finally, the scenario where the eavesdropper's CSI is unavailable is studied. Numerical results show that although our modified CI-based precoder can achieve a better energy-secrecy trade-off than the existing approach, both have a limited secrecy benefit. On the contrary, the precoders developed using the new CI-design principle can achieve a much improved tradeoff and significantly degrade the eavesdropper's performance.

While constructive interference offers indirect advantages in physical layer security by reducing the transmit power required to achieve a desired performance level, additional gains are possible by choosing the symbols to degrade the eavesdropper's ability to decode the desired data. An algorithm was recently proposed for this purpose, but it assumes an eavesdropper that employs simple nearest-neighbor decoding, and that only exploits a portion of the space available for destructive interference (DI). In this paper, we modify the technique to exploit the full DI region, but we show that even with this improvement, the general approach is vulnerable to an intelligent eavesdropper who can perform maximum likelihood detection. Based on this observation, we propose an alternative approach that, while requiring increased transmit power, can yield the best possible security.

In this paper, we investigate the uplink transmission performance of URLLC in multi-cell interference channel, which is characterized by the delay-bound violation probability under given end-to-end (E2E) latency bound. To avoid extra channel estimation and feedback overheads, we assume that each device transmits with fixed rate and power, without the knowledge of instantaneous channel state information (CSI). Moreover, the statistical queuing behaviour of each device is described by the tool of stochastic network calculus. Due to the stringent performance metrics and short codeword blocklength of URLLC, we develop a performance optimization framework to maximize the transmission reliability of URLLC in the finite blocklength regime, via optimizing the fixed transmission rate. Numerical and simulation results demonstrate the effectiveness of our proposed performance optimization framework, in terms of improving the transmission reliability of URLLC.

Unlike many existing studies on physical layer security which use multi-antenna techniques to improve the transmission security, this paper exploits the multipath wireless channel to achieve secure transmission. Specifically, we consider a time-reversal transmission system where the signal waveform is designed as the conjugated time reversed counterpart of the wireless multipath channel. Thus, the wireless channel acts as a matched filter that can boost the signal power at the intended receiver. To further improve the transmission security, we also inject a time-domain artificial noise which causes no interference to the intended receiver's signal detection. As for the eavesdroppers, we assume that their specific number and locations are unknown and use the homogenous Poisson point process (HPPP) to model the distribution of them. First, we study the average achievable rate of the intended user and a given eavesdropper, respectively. Then, based on the HPPP model, we can obtain the secrecy outage probability under a given density of eavesdropper. With a given requirement on the secrecy outage probability, we can finally obtain the optimal energy allocation parameter and the corresponding maximum secure transmission rate. Numerical results are presented to show that the proposed scheme can guarantee the transmission security under various system parameters.

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

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

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

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.

The non-orthogonal multiple access (NOMA) technology can improve spectral efficiency by introducing tolerable inter-user interferences. However, the applicability of NOMA suffers from co-channel interferences and intentional jamming (CIIJ) signals, which severely degrading the estimation and detection performances of NOMA. To overcome this problem, we propose an interference/jamming-immune receiver design, featured by optimal manifold filtering (OMF) for MIMO-NOMA systems. In particular, we formulate an optimization problem of minimizing mean-squared-error (MSE) of filtered symbols. We verify the optimization problem is non-convex and its solutions of filters lie on the Stiefel manifold. Here, an insightful observation is that the problem can be transformed to be convex if a variable transformation is performed. Following this discovery, we prove that the optimal filtering matrices for symbol estimation form a Grassmann manifold on the matrix space. An explicit expression of signal-to-interference- plus-noise ratio (SINR) under those filters is further presented to show the superiority of our proposed receiver in terms of system spectral efficiency, bit-error-rate (BER), and interference and jamming signal suppression. These results demonstrate the capability of NOMA in combating CIIJ signals, thus offering an effective approach for implementation of practical NOMA systems.

Wireless channel state information (CSI) from intelligent vehicles to the roadside unit (RSU) is a must for vehicleto-infrastructure (V2I) communications in Internet of Vehicles, but easily suffers the risks of being attacked due to the publicly known and deterministic characteristic of PHY-layer pilots that are employed for CSI acquisition. This incurs the issue of wireless pilot authentication (WPA), that is, verifying the authenticity of pilots and claimed CSIs. In this paper, we, for multiantenna V2I orthogonal frequency division multiplexed communications, develop a PHY-layer cover-free (PHY-CF) coding theory to build up a secure WPA (SWPA) protocol. Here, we encode and convey vehicle pilot signals into diversified subcarrier activation patterns (SAPs) on the time-frequency domain by employing cover-free coding. We redesign the decoding procedure using the signal independence characteristic such that those encoded SAPs, though camouflaged by malicious signals and superimposed onto each other in wireless environment, could be separated, identified and decoded into the original pilots securely. For this protocol, we prove that perfect pilot conveying and separation could be both guaranteed. We formulate the pilot identification error probability (IEP) and show how PHY-CF coding could help position the location of attacker and reduce IEP to further achieve ultrasecurity. Considering 20 MHz long-term evolution bandwidth, we prove that the number of co-time co-frequency vehicles that are securely authenticated achieves up to 19 × X for X serving sectors of base station type RSU and the latency time of uplink data access is up to 1.5 ms, thus furthering the autonomous driving. Computer simulations comprehensively verify those benefits of proposed SWPA protocol.

The broadcast nature of wireless communications makes transmission susceptible to eavesdropping. Recently, physical-layer security has been extensively studied to secure the wireless transmissions, but the security is severely compromised when the eavesdropper has the superiority in the channel quality. Toward this issue, we in this paper propose a joint ARQ and network coding method exploiting the characteristics of both the physical layer and the link layer. Specifically, we relate the message to be transmitted in the current slot with the successfully decoded messages in previous slots, where the ARQ is employed at the legitimate side to guarantee the successful transmission and decoding. Once one or more received messages fail to be decoded at the eavesdropper, it further prevents the eavesdropper from decoding the transmissions in later slots and thus the message transmitted currently and later is secured at the legitimate side. Moreover, for the more disadvantageous case that the eavesdropper is geographically closer to the transmitter, we further propose a destination-aided network coding scheme, which secures private information no matter where the eavesdropper is. The closed-form expressions of intercept probability and loss probability of the private information are analytically presented. The simulation results are provided to confirm our theoretical findings.

In wireless orthogonal frequency-division multiplexing communications systems, pilot tones, due to their publicly known and deterministic characteristic, suffer significant jamming/nulling/spoofing risks. Thus, the convectional channel training protocol using pilot tones could be attacked and paralyzed, which raises the issue of anti-attack channel training authentication (CTA), i.e., verifying the claims of identities of pilot tones and channel estimation samples. In this paper, we consider one-ring scattering scenarios with large-scale uniform linear arrays (ULA) and develop an independence-checking coding (ICC) theory to build a secure and stable CTA protocol, namely, ICC-based CTA (ICC-CTA) protocol. In this protocol, the pilot tones are not only merely randomized and inserted into subcarriers but also encoded as diversified subcarrier activation patterns (SAPs) simultaneously. Those encoded SAPs, though camouflaged by malicious signals, can be identified and decoded into original pilots for high-accuracy channel impulse response (CIR) estimation. The CTA security is first characterized by the error probability of identifying legitimate CIR estimation samples. We prove that the identification error probability (IEP) is equal to zero under the continuously distributed mean angle of arrival (AoA) and also derive a closed-form expression of IEP under the discretely distributed case. The CTA instability is formulated as the function of probability of stably estimating CIR against all available diversified SAPs. A realistic tradeoff between the CTA security and instability under the discretely distributed AoA is identified and an optimally stable tradeoff problem is formulated, with the objective of optimizing the code rate to maximize security while maintaining maximum stability for ever. Solving this, we derive the closed-form expression of optimal code rate. Numerical results finally validate the resilience of proposed ICC-CTA protocol.

Due to the publicly known and deterministic characteristic of pilot tones, pilot authentication (PA) in multi-user multi-antenna orthogonal frequency-division multiplexing systems is very susceptible to the jamming/nulling/spoofing behaviors. To solve this, in this paper, we develop a hierarchical 2-D feature (H2DF) coding theory that exploits the hidden pilot signal features, i.e., the energy feature and independence feature, to secure pilot information coding which is applied between legitimate parties through a well-designed five-layer hierarchical coding model to achieve secure multiuser PA (SMPA). The reliability of SMPA is characterized using the identification error probability (IEP) of pilot encoding and decoding with the exact closed-form upper and lower bounds. However, this phenomenon of non-tight bounds brings about the risk of long-term instability in SMPA. Therefore, a reliability bound contraction theory is developed to shrink the bound interval, and practically, this is done by an easy-to-implement technique, namely, codebook partition within the H2DF code. In this process, a tradeoff between the upper and lower bounds of IEP is identified and a problem of optimal upper and lower bound tradeoff is formulated, with the objective of optimizing the cardinality of sub-codebooks such that the upper and lower bounds coincide. Solving this, we finally derive an exact closed-form expression for IEP, which realizes a stable and highly reliable SMPA. Numerical results validate the stability and resilience of H2DF coding in SMPA.

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

Small cell networks are envisioned as one of the critical enabling technologies for the next-generation wireless systems. However, due to the limited capability of small cell base stations as compared with the macro-cell base stations, the secure wireless communication faces significant challenges. Towards this issue, we target at enhancing the wireless security for small cell networks by employing the physical layer security techniques. Specifically, we maximize the secrecy rate for each individual small cell in a distributed manner, while protecting the transmissions in the macro-cell by imposing the aggregate interference constraint over the small cell transmissions. The distributed secrecy competition is formulated as a generalized Nash equilibrium problem, for which we adopt its equivalence in the form of the quasi-variational inequality to analyze the existence and uniqueness of the Nash equilibrium. Furthermore, we tackle the interference constraint as the penalty over the secrecy rate of the small cells and introduce the Nash equilibrium problem formulation. Then, the distributed algorithm is proposed based on the best-response strategy to solve for the Nash equilibrium of the secrecy competition game. Finally, simulation results are provided to corroborate our theoretical findings.

In order to protect the primary information and provide some licensed spectrum for the secondary system, we propose a secure transmission scheme, where the primary privacy information is protected by a full-duplex secondary system. In the proposed scheme, the secondary receiver utilizes one antenna to receive the secondary information, and the remaining antennas will be utilized to jamming the eavesdropper. In addition, the secondary system allocates a fraction of the transmit power for the secondary transmission and the remaining power will be allocated for jamming protection in the null space of both the primary and secondary transmission links. For the proposed scheme, we first analyze the primary secrecy outage probability and the secondary transmission rare as well as the energy-efficiency. Moreover, we optimally allocate the secondary transmit power such that the primary average secrecy rate is maximized under the requirements of the secondary transmission rate and energy-efficiency. Numerical results have been given to show the performance improvement of the proposed scheme about both the primary and secondary performances.

This paper studies the secrecy energy efficiency problem for a massive multiple-input multiple-output (MIMO) relaying system. Specifically, the imperfect analog-to-digital converters (ADCs), i.e., ADCs with limited resolution, are employed at the relay, which will cause quantization noise. For the relay strategy, we adopt the amplify-and-forward (AF) relaying protocol. Meanwhile, a null-space artificial noise (AN) is also injected by the relay. To measure the quantization noise, we use the additive quantization noise model (AQNM), which models the quantization error as independent Gaussian noise. Based on this model, the ergodic secrecy rate of the considered relaying system is studied. To maximize the ergodic secrecy rate, we study the power allocation problem at the relay, which allocates power between the forwarded signal and the null-space AN. With the derived optimal power allocation parameter, we then focus on the secrecy energy efficiency, that is, the secrecy rate normalized by the total power consumed at the relay. By using a reasonable energy consumption model, the energy consumption of the ADCs and the transmit power are both included. Numerical results are provided to show that low-resolution ADCs with quantization bits being one or two can achieve the highest secrecy energy efficiency. In addition, higher-resolution ADCs are preferred when the number of antennas becomes smaller.

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

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

Due to the publicly-known deterministic character- istic of pilot tones, pilot-aware attack, by jamming, nulling and spoofing pilot tones, can significantly paralyze the uplink channel training in large-scale MISO-OFDM systems. To solve this, we in this paper develop an independence-checking coding based (ICCB) uplink training architecture for one-ring scattering scenarios allowing for uniform linear arrays (ULA) deployment. Here, we not only insert randomized pilots on subcarriers for channel impulse response (CIR) estimation, but also diversify and encode subcarrier activation patterns (SAPs) to convey those pilots simultaneously. The coded SAPs, though interfered by arbitrary unknown SAPs in wireless environment, are qualified to be reliably identified and decoded into the original pilots by checking the hidden channel independence existing in sub- carriers. Specifically, an independence-checking coding (ICC) theory is formulated to support the encoding/decoding process in this architecture. The optimal ICC code is further devel- oped for guaranteeing a well-imposed estimation of CIR while maximizing the code rate. Based on this code, the identification error probability (IEP) is characterized to evaluate the reliability of this architecture. Interestingly, we discover the principle of IEP reduction by exploiting the array spatial correlation, and prove that zero- IEP, i.e., perfect reliability, can be guaranteed under continuously-distributed mean angle of arrival (AoA). Besides this, a novel closed form of IEP expression is derived in discretely-distributed case. Simulation results finally verify the effectiveness of the proposed architecture.

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.

In this paper, a secure generalised precoding aided quadrature spatial modulation (SGPQSM) scheme is proposed to resist the passive eavesdropping that is unknown to the transmitter. With the help of artificial noise in the null space, the SGPQSM scheme can retain all the advantages of generalised precoding aided quadrature spatial modulation (GPQSM) at the legitimate receiver while producing interference to the eavesdropper. Signal space diversity (SSD) is also used to increase the "diversity order". Then the secrecy capacity of our proposed SGPQSM is analyzed and the optimal power allocation between signal and artificial noise is investigated. Simulation results demonstrate that our SGPQSM can significantly improve the secrecy performance. In multiple-input multiple-output systems, the proposed SGPQSM scheme performs better than the existing secure scheme.

Physical layer security (PLS) has emerged as a promising technique to guarantee the secrecy of wireless communication against eavesdroppers. Although extensive studies have been devoted to the PLS-based transmission design, the combination of this physical layer technique and network layer mechanisms such as routing still remains an open problem. This paper concentrates on the security issue for the cognitive radio networks. Specifically, we focus on the routing protocol design for the artificial-noise-aided multi-hop multi-antenna secondary communication against randomly distributed eavesdroppers. We first derive the closed-form expression of secure connection probability (SCP) for a single hop link. We optimize the power allocation between the information signal and artificial noise to obtain the maximum SCP of each link. Then a secure routing problem is formulated whose objective is to find the multi-hop path having the highest secrecy throughput. An optimal routing strategy based on a revised Bellman-Ford algorithm is proposed. We also propose a suboptimal strategy based on the Dijkstra algorithm with lower complexity. From numerical results we find that the selected route always makes a detour around the primary user to enhance the secrecy throughput.

Conventional time-division duplex (TDD) orthogonal frequency division multiplexing (TDD-OFDM) system is vulnerable to the pilot spoofing attack as it mainly relies on employing the deterministic pilot to capture the channel state information (CSI). To solve the abovementioned issue, we in this paper propose an independent component analysis (ICA) based channel identification and estimation (ICA-CIE) mechanism which uses randomized pilot to paralyze the pilot spoofing attack. Specifically, we first convert the pilot spoofing attack to the pilot jamming attack by exploiting pilot randomization. Then, an eigenstructure-based detector (EID) is proposed to realize efficient pilot jamming attack detection. In particular, on one hand, Least Square (LS) method is adopted if no jamming attack is detected. On the other hand, if jamming attack is detected, we further design a channelseparation oriented joint approximate diagonalization of eigenmatrices (CS-JADE) algorithm for channel estimation and identification. Therein, an optimization problem is first formulated to optimize the fourth-order statistical information of the observation data, after which a pilot signal reconstruction scheme is further developed to help identify true sub-channels from the optimization output. Simulation results demonstrate that our proposed ICA-CIE mechanism can achieve highly-accurate channel estimation for the legitimate transceiver pair only by using 6 OFDM symbols within the coherence time. Moreover, we also observe that the estimation accuracy of our proposed mechanism is immune to Eve's transmit power, which further verifies superiority of our proposed mechanism.

Information security is a critical yet challenging issue for wireless communications. In this paper, we consider the distributed resource competition in a network that consists of both security-oriented users (SeUs) and regular users (ReUs) which, respectively, intend for secrecy rate and transmission rate maximization. To enhance wireless security, the SeUs are given higher priorities such that they are allowed to take action first in the competition, which gives rise to the multi-leader-follower hierarchical game formulation where the SeUs are the leaders in the upper layer and ReUs are the followers in the lower layer. However, the solution to the lower sub-game among the ReUs, in the form of a Nash equilibrium parameterized by the upper strategy, lacks closed-form expression, which hinders us from solving the hierarchical game effectively. To tackle this issue, we first consider the case with one leader and reformulate the game as a mathematical program with equilibrium constraints (MPEC). Then, the MPEC is transformed as a single-level optimization and solved through successive concave approximation. For the general case that comprises multiple leaders, the equilibrium program with equilibrium constraints (EPEC) is introduced for the game reformulation. Due to the inherent difficulties of EPEC, the relaxed concept of local Nash equilibrium (LNE) is introduced as the solution. Furthermore, the existence and uniqueness of the LNE are investigated with the variational inequality-based analysis. Finally, simulation results are provided to corroborate our theoretical findings.

We propose two cooperative secure transmission schemes to protect a two-hop buffer-aided network assisted by an energy harvesting relay. In the first scheme, we assume that the knowledge of the energy harvesting and fading channels states is known in a non-causal manner (offline). In the second scheme, we assume that this knowledge is known in a causal manner (online). For both schemes, we first design an effective link selection policy by taking into account of the transmission efficiency and information security requirements. We then optimally allocate the harvested power at the relay node. For the offline scheme, we maximize the average secrecy rate under the stability constraints of the data queue and the energy queue according to the proposed link selection policy, and design a two-stage iterative algorithm to select the transmission link and allocate relay’s transmit power. In the online scheme, we first model the average secrecy rate maximum problem as a Markov decision process, and then utilize the causal knowledge to select the best transmission link and optimally allocate relay’s transmit power. In addition, the exact and asymptotic closed-form expressions are derived for the ergodic secrecy rate. Numerical results are presented to validate our analysis and demonstrate that the proposed schemes outperform the other buffered-aided secure transmission schemes assisted by the energy harvesting relay in terms of average secrecy rate.

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.

We in this paper introduce an advanced eavesdropper that aims to paralyze the artificial-noise-aided secure communications. We consider the M-1-2 Gaussian MISO wiretap channel, which consists of a M-antenna transmitter, a single-antenna receiver, and a two-antenna eavesdropper. This type of eavesdropper, by adopting an optimal Grassmann manifold (OGM) filtering structure, can reduce the maximum achievable secrecy rate (MASR) to be zero by using only two receive antennas, regardless of the number of antennas at the transmitter. Specifically, the eavesdropper exploits linear filters to serially recover the legitimate information symbols and intends to find the optimal filter that minimizes the mean-square error (MSE) in estimating the symbols. During the process, a convex semidefinite programming (SDP) problem with constraints on the filter matrix can be formulated and solved. Interestingly, the resulted optimal filters constitute a complex Grassmann manifold on the matrix space. Based on the filters, a novel expression of MASR is derived and further verified to be zero under the noiseless environment. Besides this, an achievable variable region (AVR) that induces zero MASR is presented analytically in the noisy case. Numerical results are provided to illustrate the huge disaster in the respect of secrecy rate.

With large coverage area, high data rate, low latency and high spectral efficiency, LTE-V has been considered as a promising communication technology in the vehicular networks. However, as all vehicles share the same wireless resource, in LTE-V, how to design power allocation strategy for each vehicle while motivating other vehicles to forward the data remains challenging. In this paper, we model the data transmission in the uplink scenario of the vehicular network as an equilibrium program with equilibrium constraints (EPEC), where the upper-layer vehicles as receivers (VaRs) provide priced relaying service to vehicle as a transmitter (VaT) in the bottom layer. Observing the prices set by serving VaRs in allocated channels, we adopt multi-level water- filling algorithm at the VaT for power allocation. With joint consideration on the optimal reaction of the VaT and the pricing strategy of other VaRs, each VaR optimizes its setting price by adopting the sub-gradient algorithm such that the equilibrium of the formulated EPEC is finally achieved. Simulation results corroborate our theoretical analysis and demonstrate the performance superiority of our proposal as compared with classic pricing strategies of VaRs.

Information security is a critical yet challenging issue for wireless communications. In this paper, we investigate this problem from a networked perspective. Specifically, we consider a wireless network where there coexists users with and without security concerns, which respectively maximize the secrecy rate and transmission rate. To enhance wireless security, we give the security-oriented user higher priorities and thus formulate the hierarchical power competition game with the security-oriented user at the upper layer as the leader and the regular users at the lower layer as followers. However, the solution to the power competition at the lower layer, which is a Nash equilibrium parameterized by the upper power strategy, lacks closed-form expressions and thus impedes us from solving the hierarchical game by directly applying the backward induction method. As such, we instead employ the mathematical program with equilibrium constraints (MPEC) formulation for our considered problem. Leveraging the concavity of the lower-layer problem, we then transform the MPEC problem into a single-level optimization and solve for the optimal by applying the difference-of-two-concave-functions (D.C.) programming. Numerical results are provided to validate our theoretical analysis, which also demonstrates the advantages of our model to enhance security as compared with the case of single-level competition.

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

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

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

Physical layer security has emerged as a promising technique to safeguard the information security in wireless networks. In this paper, we investigate the physical layer security issue for a wireless network where there coexist multiple users with security concerns. Specifically, we tackle the problem from a distributed perspective and formulate the secure transmissions at different users as a non-cooperative game. Consider the practical situation that the legitimate transmitter may not always have the perfect information regarding the channel state information of the eavesdropper, we adopt the robust secrecy rate to combat the potential worst cases. Accordingly, the robust Nash equilibrium is employed as the solution to the resource competition game among the users. Further, we analyze properties of the equilibrium and derive the optimal transmission strategy for each individual user to maximize its own robust secrecy rate, following which the distributed algorithm is proposed for the network-wide competition to reach the equilibrium. Finally, simulation results are provided to corroborate our theoretical findings.

In this paper, we investigate the possibility of the use of suprathreshold stochastic resonance system in the physical layer security scheme. The suprathreshold stochastic resonance (SSR) array is used as a preprocessor at the front-end of the legitimate receiver, which reduces the noise in the receiving signal. Analysis in two cases are given, which are (1) the eavesdropper uses the minimum error probability detection and (2) the eavesdropper also uses the SSR-based detection. For (1), as the ambient noise of the channel is non-Gaussian it is shown that even as the legitimate channel is inferior to the eavesdropping channel in a certain range, the mutual information of the legitimate channel is higher than that of the eavesdropper's, which provides the possibility of enlarging the secrecy capacity. For (2), analysis shows that the eavesdropper experiences greater performance deterioration as the legitimate receiver sends the Gaussian interference with timevarying mean. The bit-error-rate simulations testify the above performance analyses.

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.

We consider secure communications over MISO wiretap channels, in the presence of a passive eavesdropper with multiple antennas. In this scenario, an artificial-noise-resistant (ANR) eavesdropping behavior is introduced to invalidate the artificial noise (AN) scheme proposed by Goel et al. This novel eavesdropper, by exploiting the statistical filter derived from the collected signals, can completely eliminate the influence of AN by using only two receive antennas. In particular, the received signals are firstly syphered to eliminate the power influence of AN by a linear weight which is generated from the statistical estimation for the signal covariance matrix. In comparison with the original legitimate signals, the weighted signals are however imposed by a phase difference which can be then erased by a weight vector inferred from the available information at the eavesdropper. Based on the two filtering processes, we derive a novel expression of achievable secrecy rate and give an analytical expression for the zero-secrecy-rate distance of eavesdropper to the transmitter. Finally, we characterize the expression of maximum achievable secrecy rate (MASR) and show that the optimal power allocation strategy under an ANR eavesdropping behavior is transformed into no allocation of transmission power to AN. Numerical results are presented to illustrate the damage caused by the investigated eavesdropping behavior. Interestingly, the consequence of AN elimination is not influenced even under large number of transmit antennas.

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

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

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

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

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

In this paper, we consider the physical layer security issue for the multi-channel full-duplex (FD) communications in the presence of eavesdroppers. There co-exist multiple FD pairs, where the two users in each pair perform bi-directional transmissions. The secure communication is then challenged by the users' self-interference, external interference from other pairs, and threats from the eavesdroppers. We investigate the problem from a distributed perspective and formulate the problem as a non-cooperative game, where each user optimizes their power allocation over the channels to maximize their own secrecy rate. Confirming the existence of the Nash equilibrium, we introduce an equivalent variational inequality (VI) formulation to derive the sufficient condition for the equilibrium to be unique. We then develop the iterative security-aware water-filling (ISWF) algorithm that can be implemented at each individual user in a distributed manner and prove that the condition for the unique equilibrium also claims the convergence of ISWF algorithm. Furthermore, we extend our formulation to the heterogeneous cases that there co-exist FD and half-duplex users with different security requirements in the networks, and demonstrate that they can all be covered as special cases under our formulated VI framework. Finally, we present simulation results to validate our theoretical findings.

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.

In this paper, we consider the multi-channel power optimization to enhance security for a full-duplex (FD) transmission pair. The FD-enabled concurrent transmissions between the users, on one hand, induce self-interference at their own receivers, and on the other hand, act as friendly jamming to degrade the eavesdropping for the other. To elaborate on such a tradeoff for secrecy maximization, we investigate the power allocation problem from both centralized and distributed perspectives. For the centralized approach, we intend to maximize users' sum secrecy rate. The non-concave problem is tackled by the difference-of- two-concave-functions programming, where the local optimal is obtained by iteratively solving a series of concave problems. For the distributed approach, we formulate the secrecy rate competition between the users as a game. The Nash equilibrium of the game is then achieved by the users' best-response iterations. In particular, by leveraging the theory of variational inequality, we derive the conditions for the equilibrium to be unique. Finally, we present simulation results that verify our theoretical analysis. Also, it is demonstrated that, for the security performance of FD and half- duplex transmissions, one may outperform the other depending on the self-interference cancellation.

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.

Security issue is of paramount importance yet significant challenge for wireless communications, and this problem can be even more intricate when facing with a full-duplex adversary. In this paper, we investigate the physical layer security of a legitimate transmission link in the presence of a full-duplex active eavesdropper, who is capable to perform eavesdropping and jamming simultaneously. The legitimate user aims at a target secrecy rate while the eavesdropper intends to maximize its wiretap rate. To this end, the eavesdropper imposes a jamming signal at the legitimate receiver to stimulate higher-power legitimate transmissions and thus facilitates its eavesdropping. This, however, generates residual self-interference at the eavesdropper itself and is subject to a linear price for the jamming power. The problem is then formulated within a game-theoretic framework, where the closed-form strategies of both the legitimate user and active eavesdropper are obtained. Moreover, we analyze the performance in terms of secrecy outage probability for the legitimate link in such a hostile situation. Also provided are the simulation results which validate our theoretical analysis.

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

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

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

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

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

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

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

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

Coordinated multi‐point (CoMP) techniques can be utilised in several wireless systems, such as LTE‐A and fifth generation. However, inter‐user interferences (IEIs) and intra‐user interferences (IAIs) may degrade the quality of service in joint transmission (JT), CoMP systems. To suppress these interferences, the authors design an interference mitigation scheme via interference alignment (IA) and channel diagonalisation (IMS‐IACD), which is implemented through a local two‐step precoder‐and‐receiver design process. First, IA is employed to force the IEIs of each user into the interfering space. Second, the IAIs of each user are effectively suppressed by channel diagonalisation with a selective method. Performance analyses show that the IMS‐IACD is feasible in general JT scenarios. The IMS‐IACD does not contain any iterative process and superabundant information interaction. The structure of receiver generated by the authors’ channel diagonalisation method is quite simple, so the energy consumption can be saved by using this receiver. Moreover, computational complexities of the IMS‐IACD and traditional schemes are in the same order of magnitude, and simulation results illustrate that the IMS‐IACD improves the bit error rates and data rates for users.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

With the rapid development of vehicular and ICT technologies, Vehicular Content Networks (VCNs) play an important role in facilitating better road safety and comfortable driving. Because the content, such as traffic reports and disaster warning messages, must be delivered efficiently and timely, how to guarantee and improve the performance of VCNs becomes a hot issue. Recently, caching with the parked vehicles shows its efficiency to distribute the large-sized vehicular content. However, the related caching algorithm has not been studied thoroughly. In this paper, based on the analyses of the arrival of vehicles, mobility in parking area and access pattern of vehicular content, we propose an algorithm to decide how to use the cached replicas in the parking area to provide users with the requested content. Simulation results prove that our proposal can outperform the conventional methods.

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.

Energy detection based spectrum sensing, focusing on threshold design, is studied for cognitive radio networks in this paper. Our objective is to maximize the secondary user's throughput by dynamically controlling the energy detection threshold according to the instantaneous channel sate between secondary sender and receiver. An optimization problem is formulated and transformed to convex optimization problem after adding some reasonable regulations. Analytical solution is found which gives some insight on optimal threshold control policy design. Analysis and simulations show that the proposed strategy can obviously increase the throughput of secondary users while meeting the miss-detection probability request.

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

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

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.

ABSTRACT For amplify‐and‐forward relay networks, we propose an iterative scheme to estimate channel and detect information symbols for the multi‐antenna destination in spatially correlated noise. The equivalent channel coefficients and noise covariance are estimated by expectation–maximization algorithm. In addition, we discuss the initialization of iteration and analyze the modified Cramér–Rao bound to show the performance of the proposed iterative estimation. Moreover, on the basis of the structure of the proposed iterative estimator, a joint channel estimation and detection receiver is also provided. Finally, simulation results show that the proposed channel estimator and receiver can achieve the optimal performances in amplify‐and‐forward relay networks with unknown noise correlation. Copyright © 2012 John Wiley & Sons, Ltd.

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

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

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

Intercell interference severely degrades the performance of cell-edge users in cellular networks. To effectively manage the intercell interference, a heuristic scheme that jointly considers user scheduling and power control is proposed for multicell orthogonal frequency division multiple-access (OFDMA) networks with BS cooperation and limited backhaul capacity. First, a greedy user scheduling is proposed, taking both channel gain and the cost of directional cooperation into consideration. This scheme schedules the cell-edge users of all cells in the same subchannels. Second, under the guiding of user scheduling, the interference among cell-edge users of different cells can be preferentially cancelled with zero-forcing precoding. Third, a two-step power control is conducted to further improve cell-edge users performance. In the first step, with the strong duality of the nonconvex optimization subproblem, we obtain the minimum power consumption that guaranteeing the cellcenter users' rate requirement. In the second step, the residual power of per cell can be utilized to maximize cell-edge users performance. Then, with the fixed user scheduling, the iteration among interference cancelation and power control can be conducted until the backhaul capacity constraints are satisfied. Performance analysis proves the convergence of the proposed algorithm, and shows the effectiveness of the user scheduling. Simulation results show that, the proposed algorithm achieves a significant improvement of sum-rate for cell-edge users with negligible rate degradation for cell-center users.

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

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

With the rapid revolution of wireless transmissions technologies, Internet access through wireless mobile terminals has attracted numerous attentions from not only the research community but also industry and end users. In the past 20 years, efforts contributed by fundamental research, industry standardization, and construction of infrastructure have turned the theoretic wireless internet into reality. The major approach of current wireless-internet service provisioning can be usually decomposed by two parts. Specifically, the mobile users often first connect to a telecommunication networks, typically the cellular networks or the Wi-Fi hotspots. Then, the telecommunication networks are responsible for connecting to the internet and fulfilling the data request from mobile users. This approach maximally makes use of the existing infrastructures of both wireless and wired communications networks and has efficiently accommodated current service load of wireless internet connections. However, as the demands on internet connection over wireless networks increase explosively, we face many new challenges in wireless internet service provisioning, which are introduced by the limitation on bandwidth available to internet services, the difficulty in QoS guarantees, the shortage of radio spectrum resources, the heterogeneous networking structures, and so forth. First, telecommunications networks need to accommodate the conventional text, voice call, and video call services between mobile users as well as internet services. The proportion of resources allocated for wireless internet services cannot be arbitrarily high. Second, as aforementioned, the wireless internet services need to handle hybrid wireless and wired connections with different channel features, which make QoS guarantees an unsolved problem. Moreover, the Wi-Fi connections use the industrial, scientific, and medical radio bands, which are shared by many other wireless networks. Thus, the provisioning of wireless internet services over Wi-Fi hotspots suffers from the severe spectrum shortage problem. Last but not the least, although multiple co-existing telecommunications networks bring a better chance for robust internet connection, how to take advantage of the heterogeneous connection resources with well-balanced benefit and costs has not been well addressed nor thoroughly studied. Despite the extensive research work proposed in recent years, the aforementioned issues for wireless internet still remain open problems. This special issue is targeted at addressing the urgent need in the research community as well as the industry to discuss the recent progress and future research directions on wireless Internet. The response to our Call for Papers on this special issue was overwhelming, with over 200 articles submitted from diverse places all over the world. We held a rigorous peer-review process with each paper reviewed by at least experts in corresponding research area, and finally selected nine excellent papers published in this special issue. These nine papers cover a variety of hot topics and cutting-edge technologies in wireless Internet, involving cross-layer design, QoS-provisioning, game-theoretic approach for resource allocation, cognitive femtocell, video quality enhancement over wireless channels, fair scheduling, and so forth. The first paper, Bargaining-based Spectrum Sharing in Cognitive Radio Network 1, contributed by Y. Yan et al. proposes a bilateral bargaining mechanism between two secondary users (SUs) to avoid interference to primary users. The general network scenario with multiple SUs can be decomposed into multiple pairs of bilateral bargaining accordingly. The authors model the bargaining process through dynamic finite/infinite horizon multi-stage games and identify the corresponding equilibria. As a result, spectrum sharing across SUs can be effectively achieved. In the second paper, Queuing Analyses and Statistically-bounded Delay Control for Two-hop Green Wireless Relay Transmissions 2, Q. Du et al. studies the statistically-bounded delay control and green communications over two-hop wireless relay links. The authors showed that subject to the delay-QoS constraint, asymmetric resource allocation over the two-hop transmission is efficient in terms of reducing resource consumption. The corresponding results also give valuable guidance for position selection for relay nodes towards delay-QoS constrained relay transmissions. In the third paper, Face Detection Algorithm Based on Hybrid Monte Carlo Method and Bayesian Support Vector Machine 3, contributed by L. Wang et al., a hybrid Monte Carlo method of the Bayesian support vector machine is proposed to solve the high-dimension and long-training-time problems. Under the proposed scheme, the training time can be effectively reduced as well as the face detection result is more accurate. In the fourth paper, Energy-efficient Radio Remote Units Placement for Single-user Uplink in C-RAN 4, authored by S. Shao et al., the authors derive the energy efficiency of the newly emerged radio access network (RAN), called C-RAN, in order to achieve QoS requirements in terms of circuitry energy consumption. Optimal placement of remote radio unit, targeting at minimization of energy consumed per bit, is obtained and is validated via numerical simulations. In the fifth paper, Multi-user Multimedia Communication Over Orthogonal Frequency-division Multiple Access Downlink Systems 5, the author, L. Fan et al., investigates the problem of the multi-user multimedia communication over orthogonal frequency-division multiple-access (OFDMA) downlink systems. The authors proposed a cross-layer design towards maximizing the video quality over all users with limited network resource. Global optimal solution is derived via Lagrange dual decomposition method, and the effectiveness of the proposed scheme is verified via abundant simulations. In the sixth paper, A Fair Scheduling Scheme Based on Collision Statistics for Cognitive Radio Networks 6, contributed by W. Tang et al., develops a fair scheduling scheme by using collision statistics for cognitive radio networks, which aims at not only improving the fairness across all SUs but also control the collisions among SUs. This goal is achieved by predicting idle probabilities of available channels. Simulations demonstrate that the proposed scheme significantly improve the fairness with little impact on spectrum utilization. In the seventh paper, Adaptive Spectrum Access Strategies in the Context of Spectrum Fragmentation in Cognitive Radio Networks 7, Y. Lu et al. present two adaptive spectrum access strategies, both of which not only select the best transmission channel but also efficiently solve the spectrum fragmentation problem. The first strategy partially remedies the fragmentation problem using higher-layer solutions. The second strategy suppresses the impact of spectrum fragmentation at the physical layer by combining k spectrum fragments to form a single transmission. The superior of the authors’ proposed schemes over existing solutions are validated via simulations. In the eighth paper, Energy-efficient Resource Allocation in Multi-user Relay-based OFDMA Networks 8, contributed by J. Zhang et al., the energy-efficient resource allocation for two-hop uplink multi-user relay-based system is studied. A near-optimal resource allocation scheme to maximize the overall energy efficiency is first proposed, accompanied by a low-complexity resource allocation algorithm. Fairness constraint among users is also taken into consideration. Simulation experiments show the improvement in terms of energy-saving as well as outage probability reduction. The ninth paper, A DOF-based Dynamic Spectrum Auction Algorithm in Cognitive Femtocell 9, authored by G. Wu et al., develops a double optimization framework (DOF) based dynamic spectrum auction algorithm in cognitive femtocell. A dynamic spectrum auction algorithm under this framework employs two-stage optimization processing techniques to maximize the auction revenue. The authors proposed scheme is shown to outperform the greedy algorithm and Vickrey–Clarke–Groves auction, while keeping the high spectrum utilization efficiency. We would like to express our appreciation of all authors' excellent contributions. We also thank all reviewers for their dedication in reviewing the papers. Their valuable suggestions and comments play an important role in further improving the quality of the papers. Moreover, we appreciate the strong support and precious advice from Professor Geoffrey C. Fox, Editor-in-Chief of Concurrency and Computation: Practice and Experience, and thanks to Paterno c. Mojados JR. for his help during the publication process. Finally, we hope that readers in both academia and industry find this special issue interesting and contribute new results and developments in related research areas.

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

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

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.

ABSTRACT In this paper, the main properties of digital television broadcasting signals based on the Digital Video Broadcasting—Terrestrial (DVB‐T) standard are analyzed, and these properties are utilized to design a new pilot‐aided detector for spectrum sensing in cognitive radio networks. The proposed detector consists of a processing unit and a combination and decision unit. In the processing unit, multiple statistics that correspond to different enhanced pilot components are computed. In the combination and decision unit, three newly proposed combination schemes are adopted to combine these statistics, and then, a final decision on the presence or absence of the DVB‐T signals is made on the basis of the Neyman–Pearson criterion. The proposed pilot‐aided detector exploits both the periodic continual and scattered pilots that are intrinsic in the DVB‐T signals, processes the observed data timely, experiences short sensing duration, and requires no time synchronization information. Furthermore, the proposed pilot‐aided detector is able to distinguish DVB‐T signals from interference. Theoretical analysis and simulation results show that spectrum bands that are not currently occupied by the DVB‐T systems can be detected accurately by using the proposed pilot‐aided detector. Simulation results also demonstrate the significant performance gain of the proposed detector compared with the counterparts.Copyright © 2011 John Wiley & Sons, Ltd.

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.

Joint transmission (JT) allows base stations (BSs) to serve multiple users (UEs) in the same time and frequency resources, wherefore inter-user interference (IUI) will arise. To solve this problem, an inter-user interference suppressed precoding (IUISP) scheme is proposed in this paper. IUISP contains two steps: firstly, we utilize the system degree of freedom to suppress interference generated by the signal of any UE leaking to all others at each BS independently with sharing the linear combination matrixes designed at UEs before precoding; secondly, based on IUI suppression, signal-to-leakage-and-noise ratio (SLNR) criterion is applied to attain better performance. It can be found that IUISP proposed in this paper can't need the channel state information (CSI) interaction between any two BSs in a cooperation cluster. Furthermore, the performance analysis and simulation results show IUISP has application value in noncoherent JT and can improve the system performance.

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

Joint transmission (JT) allows multiple base stations (BSs) to serve multiple users (UEs) in the same time and frequency resources, which will causes inter-user interference (IUI). Moreover, different signals from different BSs can't be executed coherent combination directly in non-coherent JT. In this paper, a two-step precoding scheme (TSPS) is proposed to solve these problems. Firstly, an alternating minimization algorithm over the precoders at BSs and the interference subspace matrixes at UEs based on interference alignment (IA) is applied, which can force interference at each UE into the subspace and make each UE observe the interference-free desired signals. Secondly, we use the phase adjustment (PHA) based on QR decomposition to implement the coherent combination of desired signals from different BSs. This paper proves the convergence of alternating minimization algorithm in TSPS. Furthermore, numerical results show TSPS can improve the system performance by the IUI suppression and the signal coherent combination.

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

Due to common interference or noise propagation, noise correlation between relays could occur in Amplify-and-Forward relay networks. To estimate channel coefficient, the noise covariance is required in traditional channel estimations. On the other hand, we also need channel coefficient to estimate the noise covariance. Therefore,traditional channel estimators can not be utilized when noise correlation is unknown. In this paper, we propose an Expectation-Maximization algorithm based iterative channel estimator to solve this problem. Moreover, we analyze the modified Cram'er-Rao bound to show the performance of the proposed channel estimation. Finally, simulation results show that the proposed channel estimator can work well in Amplify-and- Forward relay networks with unknown noise correlation.

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.

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

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

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

To improve driving comfort and provide entertainment services, vehicular communication networks (VCNs) have appeared as an emerging solution, which consists of road-side units (RSUs) and on-board units (OBUs) to distribute multimedia contents. However, as most of OBUs always request the stored contents in the RSUs, how to update the contents in these RSUs when the original changes at its original servers has become an important issue to be dealt with. This paper proposes a novel method to resolve the above problem. Firstly, based on the characteristics of peers and geographical information, we decide which replica of which content in RSUs should be updated when its original changes. Secondly, by comparing the delivery cost of wired and wireless transmission, we decide whether the updated content should be delivered from a fixed peer or other mobile peers. Lastly, the detailed algorithm is presented and summarized.

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.

Auction based dynamic spectrum access is an efficient approach to solve the spectrum shortage problem. However, conventional spectrum auction algorithms mainly concentrated on maximizing the revenue, under which the dynamic spectrum access (DSA) users with poorer cognitive abilities are usually hard to win the auction for spectrum access. In this paper, we propose a novel waiting-time auction (WTA) algorithm to improve the winning probabilities for the DSA users with poorer cognitive abilities. Specifically, we formulate the spectrum allocation as an auction game. In the auction, all game users bid with waiting time to obtain the spectrum access opportunities, unlike the conventional approaches where users bid by using money. Correspondingly, users with poorer cognitive ability, usually having the lower time cost, can win the auction with higher probabilities through bidding longer waiting time. We prove that there exists a unique Nash equilibrium (NE) in the WTA based spectrum-allocation game and the NE is desirable for all game users. Both theoretical and simulation analyses show that our proposed WTA algorithm can effective improve the winning probabilities of users with lower cognitive abilities.

Spectrum sensing is performed by secondary users (SUs) to detect primary users (PUs) in cognitive radio networks. The limited time duration allocated for spectrum sensing may result in high false alarm probability, which always leads to low spectrum utilization and degrades the SU throughput. To overcome this problem, we propose a novel joint sensingtime adaption and data transmission (JSTA-DT) scheme. In our proposed JSTA-DT scheme, two adjacent sensing periods are bundled to form a sensing block. In each sensing block, the SU performs partial time spectrum sensing in the first sensing period. If the sensing result indicates that the PU is absent, the SU transmits its data; otherwise, the SU performs full time spectrum sensing adaptively. Both theoretical analyses and simulation results show that compared to the conventional partial time spectrum sensing and data transmission scheme, the spectrum utilization and achievable SU throughput are improved significantly by using our proposed JSTA-DT scheme.

The optimal detector for CP (Cyclic Prefix)-OFDM (Orthogonal Frequency Division Multiplexing) signal with inherent periodic pilots in low signal to noise ratio environments relies on perfect time synchronization. In this paper, we modify the optimal detector and propose a new suboptimal sensing scheme based on pilots estimation (SSS-BPE). In the proposed SSS-BPE, the likelihood criterion and periodicity of the pilot components are utilized to estimate the pilots. The estimated pilots together with the modified optimal sensing scheme are then utilized to detect the primary user signal based on the Neyman-Pearson Criterion. The proposed SSS-BPE efficiently exploits typical features in the CP-OFDM signal with no requirement on time synchronization information, and is able to distinguish between the primary user signal and interference. Theoretical analysis and simulation results show that the proposed scheme can effectively and reliably detect the presence of spectrum bands that are not used by the primary users. Furthermore, the computational complexity of the proposed SSS-BPE is relatively low compared to the counterparts.

With the development of recent vehicular communication technologies, distributing multimedia contents in the vehicular communication networks (VCNs) has become more and more popular, to provide conveniences and entertainment services during the time of driving. However, as multimedia contents are changed and updated dynamically, how to keep the consistency between the original and these replicas in VCNs is very important. Therefore, this paper designs a novel algorithm to control the consistency for the VCNs. In our proposal, after the analyses of the status of road-side units, on-board units and local geographical information, we divide all replicas into two groups, where one is necessary for update and the other are not. Then, we compare the cost to update replicas by using wireless and wired connection, and propose a method to make selection between them. The performance of our proposal is tested by simulation experiments. And the results show that our method can reduce the delay successfully.

A mobile ad-hoc network (MANET) is usually power constrained due to the limited battery energy on each node. For MANETS, energy efficiency is crucial for the design of new routing protocols. In this paper, a new energy-aware routing algorithm, called Energy Saving Ad-hoc On-demand Distance Vector (ESAODV) routing, is proposed. In the route discovery process of ESAODV, intermediate nodes estimate the current average energy of the network (CAEN) as a comparison threshold to determine how to respond to the received route request (RREQ) packets. After that, the effects of ESAODV on network performance are addressed. Analytical and simulation results show that the proposed ESAODV can effectively protect the energy-overused nodes and can greatly prolong the network lifetime.

The impact of user relaying on the behavior of a relay node, which acts as the source node at the same time, is analyzed in a wireless relay network at the packet level. The analysis process models the behavior of the relay node as a queueing system and represents the service for its own packet transmission as an M/G/1-type Markov chain. By considering the fact that the maximum number of packet arrivals is ordinarily limited in a practical system, the M/G/1-type Markov chain is further reformatted into a quasi-birth-death (QBD) process through re-blocking so as to simplify the analysis and obtain the associated performance, such as average packet transmission delay. As an application of the results arising from the analysis, a new relay node selection scheme, based on a utility function approach that jointly considers the channel and the queue conditions at the relay node, is proposed. Numerical results show that the proposed analysis model is quite accurate and the proposed relay node selection scheme is effective in balancing cooperative diversity gain and packet transmission delay.

In order to improve the spectral efficiency of the V-BLAST system employing adaptive modulation, a novel hybrid detection order is proposed that detects saturated, invalid and unsaturated valid subchannels in turn. Compared with the existing forward and reverse orderings, hybrid ordering can achieve higher spectral efficiency and smaller gap between the simulated BER and the target BER due to the flexible detection order. In addition, we demonstrate that the higher equivalent channel gain does not imply higher spectral efficiency at the scenario of adaptive modulation.

This paper presents the evaluation work of the air interface technologies developed for beyond third generation (B3G) mobile communications. Strategy of the evaluation is discussed and a set of performances selected for evaluation is justified. A software platform developed for the evaluation of potential B3G systems is then presented. Preliminary results show that OFDM+MIMO are probably one of the best choices of the air interface for B3G.