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

Task-oriented semantic communication offers a promising solution for providing real-time computer vision services. However, existing research on semantic communication ignores the connection between the key performance indicators (KPIs) used to measure semantic encoding-decoding networks (i.e., inference accuracy) and wireless semantic communication networks (i.e., transmission latency). Therefore, the designed semantic communication schemes are difficult to simultaneously meet the requirements of low latency and high accuracy of emerging intelligent applications. In this paper, by deeply exploring the relationship and interdependencies between the two kinds of KPIs, we propose a real-time and efficient task-oriented end-to-end semantic communication scheme enabled by deep reinforcement learning (DRL) and information bottleneck to improve both inference and communication efficiency. Specifically, we initially use information bottleneck theory to model the optimal tradeoff between inference accuracy and communication latency, which is subsequently reformulated by variational inference to be differentiable and tractable. Then, we introduce DRL to address the non-differentiability of dynamic stochastic fading channels and channel mismatch between the training phase and deployment phase, enabling accurate selection of the most task-relevant semantic feature dimensions for transmission under dynamic fading channels. Finally, extensive experiments show that our proposed scheme achieves better performance in latency and accuracy than comparison methods.

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

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

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

In this paper, an uplink signal detection problem is considered for Internet-of-Things (IoT) networks. Owing to the imperfections of IoT devices including I/Q imbalance and amplifier non-linearity, exact end-to-end channel models and accurate channel state information (CSI) are typically unavailable at the receiver, which obstructs the application of traditional model-based signal detection algorithms. A consensus has been reached recently that Deep learning (DL) is a promising tool to cope with this problem. However, for the IoT scenarios under consideration, devices typically transmit data using short packets with few pilot symbols, the amount of which is insufficient for each device to individually train a detector. In order to combat the data scarcity barrier and enable few-shot learning, a novel training paradigm is proposed where pilot symbols from different devices are aggregated in an intelligent manner to train a universal signal detector. Specifically, this paper devises a knowledge-driven signal detector architecture following the modular design methodology typically used in classical communication system receivers. Under this framework, three neural networks (NNs), a signal classifier, a channel feature extractor, and a signal feature extractor are created to form decision statistics and produce estimates of the transmitted symbols. Furthermore, borrowing ideas from domain adaptation, a novel component referred to as a link discriminator is integrated into the architecture to improve its generalizability. The proposed signal detector exploits pilot symbols from various IoT devices to train a universal detector that can be applied to different channel conditions without retraining, including those not seen in the training phase. Simulation results verify the superiority of the proposed knowledge-driven detector compared with existing solutions in the sense that it enjoys higher detection accuracy and can be well trained with less data.

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

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

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

This paper considers a signal detection problem for uplink transmission in Internet-of-Things (IoT) networks. Due to the non-idealities of IoT devices such as amplifier’s non-linearity, the exact end-to-end channel model as well as the accurate channel state information (CSI) is not available at the receiver (i.e., base station), which precludes the possibility of using classical model-based signal detection methods. Deep learning (DL) techniques have been recognized recently as an effective tool to deal with this challenge. However, for IoT scenarios, devices typically transmit data using short packets with few pilot symbols. The amount of training data is insufficient to train a detector for each device individually. In order to combat the data scarcity barrier and enable few-shot learning, this paper proposes to aggregate pilot symbols from different devices in an intelligent manner to train a universal signal detector which is applicable to all possible channel conditions. To be specific, a knowledge-driven signal detector architecture is devised following the modular design methodology for classical communication system receivers. Under this framework, three neural networks (NN), termed as channel feature extractor, signal feature extractor, and signal classifier, respectively, are employed to form decision statistics and make estimates on the transmitted symbols. Furthermore, borrowing the ideas in domain adaptation, a novel component termed as link discriminator is integrated into the architecture to improve the generalization capability of the detector. Simulation results demonstrate that the proposed knowledge-driven detector outperforms the existing solutions in the sense that it enjoys higher detection accuracy and can be well trained with less training data.

Physical layer encryption is a promising solution to provide transmission security for Internet-of-Things (IoT). To realize physical layer encryption, channel state information (CSI) as a random source to generate secret keys should be exploited. The reciprocity of wireless channel between legitimate communication nodes which is typically assumed for time division duplex (TDD) systems, is of critical importance to ensure the consistency in generated keys at two nodes. However, in practical TDD systems, due to the asynchronous measurement of CSI, the receiver noise and the the difference in hardware, etc., the reciprocity of channel measurements between legitimate parties is hard to be guaranteed, which makes physical layer key generation approach infeasible. To address this issue, we propose a wireless key generation scheme based on deep learning techniques. Firstly, a feature extraction network, which consists of two auto-encoders trained jointly, is developed to extract the common feature from the different yet correlated channel observations at two legitimate nodes, which will be used as the source to generate keys. Then a quantizer with an adaptive guard band width is devised to output a binary secret-key sequence from the feature vector. Numerical results show that the proposed scheme achieves remarkable improvement in terms of key disagreement rate (KDR) and key generation rate (KGR) compared existing counterparts in the literature.

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

In this paper, we develop a wireless physical-layer surveillance scheme where two devices (M1and M2) work cooperatively to eavesdrop on and intervene in a suspicious transmission link from a source (S) to a destination (D). Unlike existing approaches which rely on the use of a multi-antenna fullduplex radio as the monitor, in our scheme, M1and M2are both single-antenna nodes operating in half-duplex mode. Within any odd time slot, M1sends a jamming signal to deteriorate the signal reception at D and M2eavesdrops on the transmission from S. During the next (even) slot, M1overhears the signal sent from S, and M2forwards its received signal during the previous slot to realize jamming. In this manner, the jamming signal received at M1can be perfectly removed after self-interference cancellation, and the signals from S during the two consecutive slots are jointly decoded with high reliability, thus enabling successful surveillance. On the other hand, the detection performance at D is heavily degraded due to the injection of the jamming signal, thereby preventing information leakage from S to D.

The Viterbi algorithm is widely adopted in digital communication systems because of its capability of realizing maximum-likelihood signal sequence detection. However, implementation of the Viterbi algorithm requires instantaneous channel state information (CSI) to be available at the receiver. This is difficult to satisfy in some emerging communication systems such as molecular communications, underwater optical communications, etc, where the underlying channel models are highly complex or completely unknown. ViterbiNet, developed in the prior literature, is a promising framework to cope with this challenge, where deep learning (DL) techniques are combined with the Viterbi Algorithm to enable near-optimal signal detection without CSI. This paper offers a non-trivial variation of ViterbiNet based on generative adversarial networks (GAN). Specifically, a novel architecture using GAN is designed to directly learn the channel transition probability (CTP) from receiver observations, which is the only part of the Viterbi algorithm that is channel-dependent. With the learned CTP, the classical Viterbi algorithm can be implemented without modifications. To make the proposed architecture applicable to time-varying channels, we further develop two methods to fine-tune the learned CTP online. In the first method, pilots within each frame are exploited to update the CTP learning network; In the second method, a decision-directed approach is devised to generate training data in real-time, which is utilized to re-train the learning network. By combining these two approaches, the receiver is able to track the dynamic channel conditions without being trained from scratch. Numerical simulations demonstrate the superiority of the proposed design compared to existing methods.

In this article, we develop an alternate-jamming-aided wireless physical-layer surveillance protocol where two devices (M1and M2) work cooperatively to eavesdrop on and intervene in a suspicious transmission link from a source (S) to a destination (D). Unlike existing approaches which rely on the use of a multi-antenna full-duplex radio as the monitor, in our protocol, M1and M2are both single-antenna nodes operating in half-duplex mode, which alternately perform proactive eavesdropping and jamming to mimic the behavior of a full-duplex monitor. Within any time slot, M1sends a jamming signal to deteriorate the signal reception at D and M2eavesdrops on the transmission from S. During the next slot, M1overhears the signal sent from S, and M2forwards its received signal during the previous slot to realize jamming. In this manner, the jamming signal received at M1can be perfectly removed after self-interference cancellation, and the signals from S during the two consecutive slots are jointly decoded with high reliability, thus enabling successful surveillance. On the other hand, the detection performance at D is heavily degraded due to the injection of the jamming signal, thereby preventing information leakage from S to D. The performance of the proposed protocol is analyzed in terms of the eavesdropping non-outage probability, the surveillance success probability, as well as the symbol error probability. Theoretical analysis and simulation results demonstrate the superiority of our design compared to competing solutions in the literature.

In this letter, a deep-learning based method is proposed for signal sequence detection. A novel neural network (NN) architecture, in communications systems called Cooperative and Time-varying Bidirectional Recurrent Neural Network (CTBRNN), is developed, which learns from the training data and estimates the transmitted signal sequence without knowing the underlying channel model. Furthermore, we develop a chemical communication experimental platform to collect real data, which is used to train the NN and evaluate the performance of the developed detector. Experimental results demonstrate that, the proposed detection method outperforms the existing NN-based and NN-free candidate solutions in terms of the detection accuracy.

In this paper, we consider a wireless surveillance system, where two single-antenna legitimate devices work cooperatively to monitor a suspicious link between a source (S) and a destination (D). A rotated-jamming aided proactive eavesdropping scheme is proposed. In our scheme, one device acts as a legitimate eavesdropper (E) to perform information interception while the other works as a helping jammer (J) to interfere with the suspicious link. The proposed scheme is based on the fact that the received information- bearing signal at E can be totally represented by its projection onto a specific direction u. If node J's jamming signal is adaptively rotated to be restricted in the direction orthogonal to u, the detection of S's information at E can be free-of-interference, and the legitimate eavesdropping is successfully realized. Meanwhile, since the jamming signal arrives at E and D through different channels, the orthogonality does not hold at D and the jamming signal cannot be eliminated, thereby causing an irreducible error floor at the suspicious destination. We present a detailed description of the proposed scheme, and develop a criterion of choosing the optimal direction u* such that the symbol error probability (SEP) at E can be minimized. Finally, simulation results verify the effectiveness of the proposed scheme.

This paper proposes a message-prioritization based unequal secrecy protection framework for untrusted two- way relaying networks, where two terminal users communicate bidirectionally with the assistance of an untrusted relay. Each user is assumed to have two messages with distinct priorities: high priority and low priority. The high priority messages (HPM) are first transmitted, for which we devise a constellation overlapping method such that the received signals at the relay overlap with each other and a high error floor is created to prevent the untrusted relay from deciphering the information. Upon the completion of HPM exchange, users transmit their low priority messages (LPM) using a noise aggregation approach. To be specific, each user superposes its LPM onto the previously decoded HPM from the other user. By exploiting the difference between the error patterns for HPM at the terminal users and the relay, channel noises in various time slots can be aggregated at the relay to secure the LPM transmission. It is shown from simulation results that HPM is guaranteed to have greater reliability and higher secrecy level than that of LPM. However, the transmission of LPM enjoys lower implementation complexity and reduced system overhead, which fully demonstrates that the proposed unequal secrecy protection scheme can realize a good performance-complexity tradeoff.

This paper proposes a constellation overlapping scheme to secure two-way untrusted relaying systems, where the relay acts as both a helper facilitating data transmission and an eavesdropper intercepting users' messages. A truncated-channel-inversion based approach is developed to make the signals transmitted from two users experience the same equivalent channel, thereby realizing full constellation overlapping at the relay. Consequently, it is extremely difficult for the relay to recover the users' individual signals, and data confidentiality is thus protected. The achieved error floor level at the untrusted relay is analyzed, and the truncation thresholds are optimized to maximize the sum rate for end-to-end information exchange. Simulation results demonstrate the superiority of our scheme in terms of security and transmission efficiency compared with the existing alternatives.

This paper proposes a novel technique called constellation overlapping to secure two-way untrusted relaying systems. We first consider the symmetric case where two terminal users adopt the same modulation type. For this case, a truncated-channel-inversion-based approach is developed to make the signals from different users experience the same equivalent channel, thereby realizing full constellation overlapping at the relay. Consequently, an error floor is created at the untrusted relay, and data confidentiality is protected. Then, the asymmetric case is investigated, where different modulations are employed at the two users and the constellation overlapping cannot be realized. To address this issue, we develop a constellation virtualization method, which exploits the intrinsic user-cooperation mechanism to “virtualize” two identical constellations at the two users. In this manner, the constellation overlapping effect can still be achieved, without sacrificing much reliability performance. We optimize the truncation threshold to maximize the sum rate for end-to-end information exchange, and design the bit-to-symbol mapping criterion and the signal detection method as well. Unlike the existing physical layer security approaches that guarantee secrecy with additional resource consumptions, this method realizes security enhancement by letting different users mutually encrypt their signals over the air, which is more resource-efficient.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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.

The configuration of the almost blank subframe (ABS) is a critical technique for enhanced intercell interference coordination (eICIC) in heterogeneous network (HetNet). Due to great channel changes during the ABS and the normal subframes, it is challenging to estimate the signal to interference plus noise ratio (SINR) exactly. In this paper, a novel method based on gray model is proposed to estimate SINR. Gray model can use a small amount of ABS information as original series and get accumulative generation series to predict the current SINR of ABS. Performance evaluation shows that the proposed method can get more accurate SINR estimation and improve user average throughput when channel correlation is strong or the ratios of ABS are high. The proposed SINR estimation method is more appropriate for macro-pico network with strong correlated channels.

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

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

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

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

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

In this paper, the user cooperation behaviors under eavesdropping attack are analyzed through game theory. Considering the physical layer security, we prove that the conventional cooperation scheme actually deteriorates the secrecy performance compared to the direct transmission, given that the eavesdropper has a better channel condition to the users than the destination. In this case, the necessary condition of the cooperation that the users should obtain additional utilities from the cooperation is not satisfied, which makes the users have no incentive to participate in the cooperation game. In order to motivate users, an adaptive cooperation scheme is designed to improve the secrecy performance even if the eavesdropping channel is superior to the destination's channel, and it is also observed that the mutual cooperation is one of the Nash equilibriums. We further exploit the Stackelberg game with a punishment mechanism to make the mutual cooperation as the unique Nash equilibrium.

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.

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.

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

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

In this paper, an energy-efficient cooperative geographic routing (ECGR) is proposed for Wireless Sensor Networks(WSN). ECGR fully takes advantage of cooperative diversity and "multi-sensor diversity" to reach energy-efficient routing. At physical layer, thanks to the high redundancy of the nodes in WSN, the nodes that have correctly decoded the packet are selected to realize "multi-sensor diversity" gain, and these nodes form a new forwarding cluster in a distributed way to obtain transmit diversity. At the network layer, based on the geographic routing, ECGR makes the packet always rout toward the destination and avoids diverging routes, simultaneously. In this way, we increase the transmitting distance of each hop, meanwhile, the overall network energy consumption is reduced and are balanced among the sensor nodes to extend the life of the network. We also discuss an appropriate packet error rate (PER) to optimize average transmitting distance.

In this paper, we present a framework to analyze the outage performance of the cooperative systems exploiting multiuser diversity (MUD). A two-hop wireless network consisting of a destination node, a relay node and multiple source nodes is considered. The asymptotic expressions of the system outage probability are derived for amplify-and-forward (AF) and decode-and-forward (DF) protocols. From these expressions it is shown that the diversity orders of K+1 and K can be achieved for AF and DF, respectively, where K is the number of source nodes in the network.

This Letter studies the two-path digital relaying systems, where two relay nodes alternately forward messages from the source to the destination. By applying the signal space diversity (SSD) technique, a novel adaptive scheme is proposed to deal with the inter-relay interference (IRI). Our work reveals that, with careful protocol design, the IRI becomes a beneficial resource that can be utilized rather than a detrimental factor that has to be suppressed. Simulation results demonstrate that, in high average SNR regions, the proposed method outperforms the existing alternatives in terms of symbol error probability.

This letter presents an analysis of the combined use of cooperative diversity and multiuser diversity (MUD) in multi-source multi-relay networks. A joint selection scheme, which selects the best source-relay pair to access the channel, is proposed. The main contribution of our work is the derivation of the exact and asymptotic expressions for the outage probability of the system with amplify-and-forward (AF) protocol. From these expressions it is indicated that the total diversity order ofM+Ncan be achieved, whereMandNare the number of source nodes and relay nodes, respectively. Based on the outage probability in high signal-to-noise-ratio (SNR) region, the optimum power allocation scheme is also given to improve the system performance.

This letter considers the amplify-and-forward (AF) system consisting of a destination node with M antennas, and a source node and N relay nodes with a single antenna each. The “best” relay, which is selected depending on the instantaneous and partial channel knowledge, is assigned to assist the source transmission. By deriving the lower and upper bounds for the outage probability of the system, we show that the diversity order of min(M,N) can be achieved. This analysis gives a promising solution to improve the diversity performance of partial relay selection scheme.

In the above mentioned paper, the authors proposed an incremental transmission relay selection (ITRS) protocol, whose diversity-multiplexing-tradeoff performance was illustrated and compared with existing alternatives in Fig. 3. However, in our opinion, these comparisons are unfair and a curve depicted in Fig. 3 is wrong. First, in [ibid., vol. 26, no. 8, pp. 1419'1428, Oct. 2008], the authors assumed that the channel coefficients change in every time slot, whereas other protocols assumed that the channel coefficients remain constant during the whole frame transmission. This implies that an additional temporal diversity can be extracted by the ITRS protocol whereas other protocols (DDF, DSTC, opportunistic relaying) can only benefit from spatial diversity. So, the comparisons in the original paper are unfair because they are made under different conditions. Second, according to the original authors and references therein, the maximum diversity order of the ITRS protocol is M+2, and that of other protocols (DDF, DSTC, opportunistic relaying) are all equal to M+1, where M is the number of relay nodes (i.e. eight for Fig. 3). Thus, the DMT curve for DSTC (it is also the DMT curve for opportunistic relaying) is wrong in Fig. 3. Note that although the authors allowed the source to participate in the second phase of transmission when they considered DSTC and opportunistic relaying, the diversity order cannot be enhanced at all due to the fact that the channel coefficients remain constant during the two phases. And our declaration holds true all the same.