For autonomous aerial vehicle (AAV) secure communications, traditional designs based on fixed position antenna (FPA) lack sufficient spatial degrees of freedom (DoF), which leaves the line-of-sight-dominated AAV links vulnerable to eavesdropping. To overcome this problem, this paper proposes a framework that effectively incorporates the fluid antenna (FA) and the artificial noise (AN) techniques. Specifically, the minimum secrecy rate (MSR) among multiple eavesdroppers is maximized by jointly optimizing AAV deployment, signal precoder, AN precoder, and FA positions. In particular, the worst-case MSR is considered by taking the channel uncertainties due to the uncertainty about eavesdropping locations into account. To tackle the highly coupled optimization variables and the channel uncertainties in the formulated problem, an efficient and robust algorithm is proposed. Particularly, the uncertain regions of eavesdroppers, whose shapes can be arbitrary, are disposed by constructing convex hull. In addition, two movement modes of FAs are considered, namely, free movement mode and zonal movement mode, for which different optimization techniques are applied, respectively. Also, both single-user and multi-user scenarios are considered. Numerical results show that, the proposed FA schemes boost security by exploiting additional spatial DoF rather than transmit power, while AN provides remarkable gains under high transmit power. The synergy between FA and AN results in a secure advantage that exceeds the sum of their individual contributions, achieving a balance between security and reliability under limited resources.

This paper proposes a novel diffusion-enabled pluggable encryption/decryption modules design against semantic eavesdropping, where the pluggable modules are optionally assembled into the semantic communication system for preventing eavesdropping. Inspired by the artificial noise (AN)-based security schemes in traditional wireless communication systems, in this paper, AN is introduced into semantic communication systems to prevent semantic eavesdropping. However, the introduction of AN also poses challenges for the legitimate receiver in extracting semantic information. Recently, denoising diffusion probabilistic models (DDPM) have demonstrated their powerful capabilities in generating multimedia content. Here, the paired pluggable modules are carefully designed using DDPM. Specifically, the pluggable encryption module generates AN and adds it to the output of the semantic transmitter, while the pluggable decryption module before semantic receiver uses DDPM to generate the detailed semantic information by removing both AN and the channel noise. In the scenario where the transmitter lacks eavesdropper’s knowledge, the artificial Gaussian noise (AGN) is used as AN. We first model a power allocation optimization problem to determine the power of AGN, in which the objective is to minimize the weighted sum of data reconstruction error of legal link, the mutual information of illegal link, and the channel input distortion. Then, a deep reinforcement learning framework using deep deterministic policy gradient is proposed to solve the optimization problem. In the scenario where the transmitter is aware of the eavesdropper’s knowledge, we propose an AN generation method based on adversarial residual networks (ARN). Unlike the previous scenario, the mutual information term in the objective function is replaced by the confidence of eavesdropper correctly retrieving private information. The adversarial residual network is then trained to minimize the modified objective function. Simulation results show that the diffusion-enabled pluggable encryption module prevents semantic eavesdropping with high covertness while the pluggable decryption module achieves the high-quality semantic communication.

Recently, the noise modulation scheme and active anti-jamming (AAJ) scheme have shown the potential advantages, such as high anti-jamming performance, low transmitter complexity and low power consumption. Inspired by the prior schemes, this paper proposes a novel hybrid noise and jamming modulation (NJM) scheme that enables reliable communication under diverse jamming scenarios. Specifically, this scheme encodes information bits through a noise modulated transmitter and the amplification factor of a programmable gain amplifier (PGA), achieving robust anti-jamming performance. Theoretical bit error rate (BER) is conducted to derive the optimal decoding thresholds for different jamming strategies, and the approximate optimal threshold proposed for practical implementation using training symbols. Furthermore, we formulate and solve the power allocation optimization problem to enhance BER performance by properly splitting power between the two modulation components. To comprehensively evaluate the scheme, we provide the capacity analysis for the hybrid NJM scheme under three jamming strategies. Simulation results show that the theoretical results match well with the simulated ones, which demonstrates the correctness of our BER performance analysis. Moreover, the hybrid NJM scheme can achieve the superior BER performance and the channel capacity under the jamming attacks compared with the benchmark schemes.

Recently, the noise modulation scheme and active anti-jamming (AAJ) scheme have shown the potential advantages, such as high anti-jamming performance, low transmitter complexity and low power consumption. This paper proposes a novel hybrid noise and jamming modulation (NJM) scheme, which aims to effectively resist the jamming attack in wireless communications. Specifically, the information bits are conveyed by the noise modulated transmitter and the amplification factor of the programmable-gain amplifier (PGA). Then, the optimal threshold is derived for decoding the messages in the receiver node. We formulate the power allocation problem of the hybrid NJM scheme as an optimization problem, which aims to obtain the optimal bit error rate (BER) performance by accurate transmit power splitting for two modulation components. Simulation results show that the theoretical results of BER fit well with the simulated ones, which verifies the effectiveness of the derivation. Moreover, the hybrid NJM scheme outperforms in BER performance against the jamming attack compared with other anti-jamming schemes.

The space-terrestrial integrated network (STIN) is one of the key development directions for future network architectures, offering global seamless connectivity, efficient resource utilization, and enhanced service reliability. Recently, the generalized filter bank orthogonal frequency division multiplexing (GFB-OFDM) has been proposed for the STIN system, which enables a flexible waveform mechanism for both terrestrial and non-terrestrial systems. However, research on GFB-OFDM detector remains scarce. In this paper, we model the symbol detection problem and introduce the linear detector and the non-linear detector into the GFB-OFDM system. Furthermore, based on the traditional expectation propagation (EP) algorithm, we propose a low-complexity eigenvalue approximation-based EP detector for the GFB-OFDM system, which achieves a lower complexity compared with the existing EP-like detector. Finally, the better error performance and the lower computational complexity of the proposed receiver are validated by the numerical results.

Physical layer security (PLS) enables secure communication which is independent of cryptography. It generally requires the legitimate channel being advantageous, otherwise, relaying techniques can be adopted to enhance the legitimate transmission and/or suppress the wiretap one. However, untrusted relays become new threatens since unknown passive eavesdroppers may approach either the transmitter or the receiver to wiretap the secure messages. Regarding the threatens in this setup, we propose a nonlinear transmit and relaying approach to protect the secure messages during the overall transmission. The essential idea is that Alice transmits a nonlinear transform of the original secure signal and the relays collaboratively invert the nonlinear transform at Alice when forwarding it to Bob. By carefully designing the layout of the relays, only a confined region where Bob locates is accessible to the non-distortion version of the original secure signal, and there is no secure signal leakage nearby Alice or the relays. Specifically, the proposed scheme consists of two phases. In the first one, a well-chosen nonlinear transform is applied to the secure signal of Alice, and she then broadcasts the transformed signal to both the relays and the eavesdropper. Each relay determines its forwarding approach by using either the Taylor series or the Lagrange inversion theorem on the nonlinear function of Alice to facilitate the perfect recovery of the original secure signal for Bob. In the second phase, the relays process the received signals using the well-designed forwarding approach and then cooperatively send them to Bob. The forwarding signals are aggregated at Bob and the original secure signal can be recovered directly without any further complicated equalization. We therefore define the region with this property by the secure-information-accessible region, and then provide the secrecy suggestions of laying the relays to shrink it. In contrast, the eavesdropper cannot get any useful secure information from either the first phase or the second one if it locates out of this region. Finally, we analyze both the bit error rate and the secrecy rate, and compare them with the existing secrecy methods. Numerical results validate the proposed nonlinear secrecy approach.

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) has been recently proposed as a promising multicarrier modulation scheme to tackle Doppler spread in high-mobility environments. Accurate channel estimation is of paramount importance to guarantee reliable communication for the ODDM, especially when the delays and Dopplers of the propagation paths are off-grid. In this paper, we propose a novel grid refinement and adjustment-based sparse Bayesian inference (GRASBI) scheme for DD domain channel estimation. The GRASBI involves first formulating the channel estimation problem as a sparse signal recovery through the introduction of a virtual DD grid. Then, an iterative process is proposed that involves (i) sparse Bayesian learning to estimate the channel parameters and (ii) a novel grid refinement and adjustment process to adjust the virtual grid points. The grid adjustment in GRASBI relies on the maximum likelihood principle to attain the adjustment and utilizes refined grids that have much higher resolution than the virtual grid. Moreover, a low-complexity grid refinement and adjustment-based channel estimation scheme is proposed, that can provides a good tradeoff between the estimation accuracy and the complexity. Finally, numerical results are provided to demonstrate the accuracy, the convergence, and the efficiency of the proposed channel estimation schemes.

In integrated sensing and communication (ISAC) system, the full-duplex is expected to further improve the integration and the efficiency. Here, we focus on the security of the full-duplex ISAC system. The serious interferences pose the challenges to the secure design of the full-duplex ISAC. In this paper, we propose a joint design of the information beamformer, the radar waveform, and the receive filters to minimize the eavesdropper’s signal-to-interference-plus-noise ratio or maximize the sum secrecy rate, where the sum capacity of the multiple access wiretap channel is taken into account in the optimization problems. An iterative algorithm is proposed for solving the formulated problems. Under appropriate conditions, the proposed iterative algorithm converges to the Karush-Kuhn-Tucker optimal point of the original problem without the rank 1 constraint. Then, we construct the new feasible solution without any performance loss while satisfying the rank 1 constraint. The simulation results indicate that our scheme outperforms the existing method, and can achieve a performance close to that of separate designs.

The orthogonal time frequency space (OTFS) modulation is resilient to the Doppler effect and thus is employed in high-mobility communications. In earlier work, the equivalent representations of the OTFS transmission were established, revealing the profound impact of fractional delay on channel sparsity. However, these representations tend to be distorted and cumbersome when accounting for digital reception and non-ideal filtering. In this letter, we propose an alternative representation with low distortion that uses the variable-fractional-delay filter to characterize the fractional delays in a conventional digital transceiver. In our proposed method, the approximation of the received signal is improved, and the error performance is enhanced compared to the original approaches. At last, the simulations show that our proposed representation is valid.

Blind signal processing techniques of channel and noise power estimation, modulation and channel coding scheme (MCS) recognition, and data detection have played crucial roles in wireless transmission scenarios, where the receiver cannot obtaina prioriinformation of the incoming signals. Each of these blind signal processing tasks has been extensively studied in the literature. Few works studied the combination of partial tasks jointly. However, to the best of our knowledge, an overall problem that involves all the aforementioned tasks has not been investigated previously. Simply cascading the solution of each individual task is apparently not a suitable approach for the overall problem. To address the above issues, we design a jointblindreceiver that jointly improves the performance of parameterestimation, MCSrecognition, and datadetection. Thus, we refer to it as theblind estimation,recognition, anddetection (BERD)receiver. The multipath fading channel, noise power, and MCS of the transmitter are all unknown to the receiver side; the required side information is only the pool of MCS candidates and perfect synchronization. In this BERD receiver, we first propose an expectation-maximization-based channel and noise power estimator block, which solves the non-convex maximum likelihood estimation problem in a tractable way. Then, a soft-information detector and regenerator block is designed to detect the transmitted information bits and regenerate more reliable symbols. The BERD receiver iterates between these two blocks, and consequently, the accuracy of channel estimation is improved and in turn helps the data detection. Finally, a multistage likelihood-based fusion and decision block is proposed to make the final decision on the adopted MCS, the information bits, and the unknown channel information. Numerical results are provided to show the superior performance of the proposed BERD receiver compared to the existing schemes, in terms of data detection, MCS recognition, and channel estimation.

For the modulation classification problems, the deep learning approaches can determine the unknown modulation formats in high confidence. However, it has been maliciously used by eavesdroppers. In this paper, we consider the wireless communication scenario, in which Alice intends to communicate with Bob confidentially in the threat of Eve, who tries to determine the unknown modulation formats of Alice using some deep learning approach. Recent advancements in adversarial machine learning have demonstrated that the deep learning techniques are vulnerable to crafted perturbations. To prevent Eve from classifying Alice’s modulation formats, Alice transmits the modulation signal with the well-designed adversarial perturbation. We first formulate an optimization problem to determine the optimized adversarial perturbation, in which the objective is to mislead the modulation classifier of Eve subject to the communication constraints, i.e., the power efficiency, the achievable rate, and the reliability. Then, the augmented Lagrangian method is adopted to solve the perturbation optimization problem, in which the implicit objective is evaluated using the Monte Carlo method, and the gradients of the implicit constraints are obtained using the Gaussian-based estimation algorithm. We further extend the perturbation design to the both cases of Alice having and not having the prior knowledge of Eve. Finally, the input-independent universal perturbation for the specific modulation type is proposed, which is deployed via a lookup table method. Numerical results show that the designed perturbation with 10% power of the modulated signal can attack Eve’s modulation classifier with the great success while ensuring both the achievable rate and the reliability close to the ideal case (say, no perturbation). Compared to the existing methods, the designed perturbation achieves the better attack performance and is robust to the filtering, the oversampling, and the time/frequency offset. Furthermore, this paper reveals that the structure type of Eve’s model has a large impact on the attack performance, and verifies that the adversarial perturbation can effectively attack the modulation classifiers that resort to the expert knowledge.

This paper studies the joint secure transceiver design for the full-duplex integrated sensing and communication (ISAC) system, in which the base station performs the target tracking and communicates with the downlink and the uplink users by reusing the resources. Here, the target, referred to as Eve, is a potential eavesdropper with the intention of intercepting both the downlink and the uplink information. The security problem of the full-duplex ISAC system has not been studied well, where the sensing and communication signals suffer from the serious interference. In this paper, we jointly design the information beamformer, the radar waveform, the uplink communication receive filter, and the radar receive filter to achieve the downlink and uplink communication security and the target tracking. Specifically, both the Eve’s signal-to-interference-plus-noise ratio minimization and the secrecy rate maximization problems, subject to the sensing and the communication constraints, are formulated for the ISAC system from the perspectives of the quality of service and the secrecy rate. An iterative algorithm is proposed for solving the formulated problems. We prove that, under appropriate conditions, the proposed iterative algorithm converges to the Karush-Kuhn-Tucker optimal point of the original problem without the rank 1 constraint. We further extend the joint design to the case of the imperfect wiretap channel and the angle uncertainty. Numerical results show that our scheme remarkably outperforms the benchmark approaches. The performance is close to that of designing the secure communication and the sensing separately.

Orthogonal time frequency space (OTFS) as a newly proposed two-dimensional modulation scheme outperforms the orthogonal frequency division multiplexing in high-speed scenarios. Most recent studies focus on off-grid channel estimation with the ideal pulse which needs to satisfy the bi-orthogonality robustness condition but does not exist. In this paper, we consider an OTFS system with rectangular pulses and propose a grid evolution based off-grid sparse Bayesian inference (GESBI) by updating the virtual delay-Doppler grid to improve the accuracy of the channel estimation. In particular, different from the recently proposed off-grid channel estimation algorithms in OTFS where the virtual grid is fixed and uniform, the proposed channel estimation method consists of an external and internal iteration, where the grid evolution method performs in the external iteration to update the virtual grid to be non-uniform by utilizing the estimated on-grid and the off-grid information in the internal iteration. In addition, the grid evolution-based efficient sparse Bayesian inference with the student’s T distribution prior (T-GEESBI) is proposed to reduce the channel estimation complexity while improving the channel estimation accuracy. Specifically, the matrix inversion is avoided by approximating the posterior distribution. Then the equal non-convex problem is handled by utilizing the block coordinate descent method in a majorization-minimization framework. Furthermore, the two types of genie bounds on the mean squared error of the estimated channel coefficients are derived. Finally, both theoretical and numerical analyses demonstrate low complexity, convergence, and the efficiency of the proposed channel estimation approach.

Multi-user integrated sensing and communication (ISAC) assisted by intelligent reflecting surface (IRS) has been recently investigated to provide a high spectral and energy efficiency transmission. This paper proposes a practical channel estimation approach for the first time to an IRS-assisted multi-user ISAC system. The estimation problem in such a system is challenging since the sensing and communication (SAC) signals interfere with each other, and the passive IRS lacks signal processing ability. A two-stage approach is proposed to transfer the overall estimation problem into sub-ones, successively including the direct and reflected channels estimation. Based on this scheme, the ISAC base station (BS) estimates all the SAC channels associated with the target and uplink users, while each downlink user estimates the downlink communication channels individually. Considering a low-cost demand of the ISAC BS and downlink users, the proposed two-stage approach is realized by an efficient neural network (NN) framework that contains two different extreme learning machine (ELM) structures to estimate the above SAC channels. Moreover, two types of input-output pairs to train the ELMs are carefully devised, which impact the estimation accuracy and computational complexity under different system parameters. Simulation results reveal a substantial performance improvement achieved by the proposed ELM-based approach over the least-squares and NN-based benchmarks, with reduced training complexity and faster training speed.

Specific emitter identification (SEI) is a technique to identify the unknown emitters by using the hardware impairment of the transmitter. In this paper, we consider the effect of the wireless channel on the SEI, which deteriorates the identification performance severely. Two identifiers are proposed to address the wireless channel effect from the model-based and the data-based perspectives, respectively. From the model-based perspective, the fingerprint extractor using the sparse Bayesian learning (SBL) is first proposed to jointly estimate the fingerprint parameters, the wireless channel, and the noise power in the multipath fading channels. Then, the classifier using the weighted Euclidean distance is designed to identify the unknown emitter. From the data-based perspective, the model-agnostic meta-learning (MAML) algorithm is adopted to meta-train the convolutional neural network (CNN) on the task collection, which is generated based on the transmitter distortion mechanism and the channel distribution. The trained CNN is fine-tuned on the unseen SEI task and then is used to identify the unknown emitter. Moreover, the Cramer-Rao lower bounds of the estimation of the fingerprint parameters are derived to evaluate the performance of the proposed fingerprint extractor. Numerical results show that the SBL identifier outperforms the MAML one in a small number of samples, while the MAML identifier outperforms the SBL one in a large number of samples. Both identifiers are robust to the wireless channel, obtain better identification performance, and require a small number of samples compared to the existing methods. Furthermore, the simulation results indicate that the mean squared error performance of the SBL fingerprint extractor is close to the performance lower bound.

Sparse code multiple access (SCMA) and polar codes have demonstrated superiority in supporting massive connections and short-packets, which are essential in the uplink transmission of the internet of things. This paper proposes a sparse neural network (SNN) for the detection and decoding of the non-binary polar-coded SCMA. The network consists of the message passing algorithms (MPA) and the sparse belief propagation (SBP) modules, which are immigrated from the MPA and BP algorithms, respectively. The nodes of the bipartite BP factor graph that do not contribute to the belief propagation are pruned to form the SBP module. A structure factor$\alpha $defined by the ratio of the number of layers in the MPA modules and that of the SBP modules is analyzed to find a balance between the decoding performance and the complexity. The weights are set on the neurons of the layers and trained by the stochastic gradient descent. Finally, the simulation results show substantial improvements in bit error rate with low complexity over the traditional receiver. Specifically, the SNN receiver outperforms the traditional receiver by 2.56 dB in the AWGN channel when the code length is 16, the code rate is$\frac {1}{2}$, and the user overloading factor is 150%.

Radio frequency fingerprint identification (RFFI) is a promising physical layer security technique that employs the hardware-introduced features extracted from the received signals for device identification. In this paper, we consider an RFFI problem in the presence of hybrid time-varying distortions (HTVDs) induced by multipath fading channel, carrier frequency offset (CFO), and phase offset. To solve this problem, an HTVDs-robust RFFI framework is proposed. Firstly, we derive that the residual HTVDs after CFO correction can be approximated as multiplicative interference in the frequency domain. Secondly, we define a novel signal analysis dimension named spectral quotient (SQ) representation and then present the spectral circular shift division (SCSD) method to generate the HTVDs-robust SQ signals, where the multiplicative interference can be suppressed. Thereafter, the statistics including root mean square (RMS), variance (VAR), skewness (SKE), and kurtosis (KUR) are extracted from the real and imaginary components of the SQ signals, respectively. Finally, the statistical features are used for the training and testing of the support vector machine (SVM) classifiers. To further enhance the performance of the proposed RFFI scheme, we also present the spectral circular multi-shift division (SCMSD) method, which increases the flexibility in the generation of the HTVDs-robust SQ signals. Given what we knew, this is the first time attempting to mitigate the HTVDs by leveraging the strong frequency correlation at the neighboring subcarriers in the multivariate hypothesis tasks. Compared to several handcraft feature-based RFFI methods, the proposed method exhibits superior identification accuracy and strong robustness. Experimental results show that the proposed RFFI scheme can achieve the accuracy of 91.3%with five devices and 86.4% with sixteen devices when the classifiers are trained with the additive white Gaussian noise but are tested with the Rayleigh channel.

Integrated sensing and communication (ISAC) and intelligent reflecting surface (IRS) are viewed as promising technologies for future generations of wireless networks. This paper investigates the channel estimation problem in an IRS-assisted ISAC system. A deep-learning framework is proposed to estimate the sensing and communication (S&C) channels in such a system. Considering different propagation environments of the S&C channels, two deep neural network (DNN) architectures are designed to realize this framework. The first DNN is devised at the ISAC base station for estimating the sensing channel, while the second DNN architecture is assigned to each downlink user equipment to estimate its communication channel. Moreover, the input-output pairs to train the DNNs are carefully designed. Simulation results show the superiority of the proposed estimation approach compared to the benchmark scheme under various signal-to-noise ratio conditions and system parameters.

Orthogonal time frequency space (OTFS) can provide better error performance than orthogonal frequency division modulation in the high-speed scenario. However, the two-dimensional convolution between the information-bearing symbols and the channel response in the delay-Doppler domain induces high-complexity detection, which hinders the implementation of the OTFS. In this paper, expectation propagation (EP) is introduced in the OTFS system to improve the reliability compared to the message passing approaches. The low-complexity EP detection with a log-linear order is proposed by observing the sparsity and the block quasi-banded matrix structure of the time-domain matrix. The exploitation of the lower-upper factorization for the banded matrix, the forward or the back substitution algorithm for the lower or the upper triangular matrices reduces the complexity of the large-scale matrix inversion involved in the EP detection from a cubic order to a linear order. The error performance of the proposed scheme is further analyzed by utilizing the state evolution and discussed under the different frame sizes. Moreover, the proposed low-complexity detector is then explored together with the likelihood decoder in an iterative manner to further improve the reliability of the proposed receiver. Finally, the better error performance and the lower computational complexity of the proposed receiver are validated by the numerical results.

The frequency diverse array (FDA)-based directional modulation (DM) technology plays an important role in the physical-layer security (PLS) transmission of 5G and beyond communications. In order to meet the tremendous increase in mobile data traffic, a new memory-efficient design for the FDA-DM-based PLS transmission is urgently demanded. In this article, an analytical symmetrical multi-carrier FDA model is proposed in three dimensions, namely, range, azimuth angle, and elevation angle, differing from the conventional analytical approach with only range and azimuth angle considered. Then, a single-point (SP) artificial noise (AN) aided FDA-DM scheme is proposed, which reduces the memory consumption significantly compared with the conventional zero-forcing (ZF) and singular value decomposition (SVD) approaches. Moreover, the PLS performance of the proposed FDA-DM scheme is analyzed in fluctuating two-ray (FTR) fading channels for the first time, including the average secrecy capacity (ASC) and the secrecy outage probability (SOP). More importantly, the closed-form expressions for the lower bound on ASC and the upper bound on SOP are derived, respectively. The effectiveness of the analytical expressions is verified by numerical simulations. This work opens a way to lower the memory requirements for DM-based PLS transmission of 5G and beyond communications.

The reconfigurable intelligent surface (RIS) is envisioned to create ultra-secure wireless networks. Previous works on the RIS-assisted security provisioning techniques assumed wiretap channel information at the transmitter, which is practically unavailable in a passive eavesdropping scenario. In this article, we consider a point-to-point anti-eavesdropping system in which a RIS is used to enable the secure transmission from a multi-antenna transmitter to a multi-antenna legitimate receiver. A passive eavesdropper, whose channel state information is completely unknown, attempts to decode the secret messages. We propose a security approach by using the reflection at the RIS as multiplicative randomness against the wiretapper. Specifically, the reflection coefficients in terms of amplitude and phase are updated in each transmission and kept private at the RIS. Through the reflection designs, the effective channel matrix is diagonalized at the legitimate receiver. In contrast, the eavesdropper receives coupled signals with the weights of the randomness at RIS. The main contributions of this article are three reflection designs and correspondingly three secure transmission schemes, which fulfills diverse requirements of the balance amongst performance metrics including the degrees of randomness, spectral efficiency, and reliability. The main benefits of the proposed secure transmission schemes are four-fold. First, the transmitter does not need to know the eavesdropper's channel states. Second, closed-form solutions for the reflection coefficients are provided. Third, the legitimate receiver has a linear decoding complexity. Fourth, the unauthorized wiretapper is unable to cancel out the multiplicative randomness and thus cannot extract much useful information. Numerical results show that exploiting the RIS as a source of multiplicative randomness provides a new perspective to improve the security of the wireless networks.

Specific emitter identification (SEI) is a technique that identifies the unique emitter from its received signal by using the specific characteristics of an emitter. In this paper, we consider an SEI problem with unknown receiver distortion. Two groups of SEI schemes based on signal decomposition are proposed. In the proposed schemes, the received signal is pre-processed by either of the following decomposition, i.e., empirical mode decomposition (EMD), intrinsic time-scale decomposition (ITD), or variational mode decomposition (VMD). In the first group of the proposed schemes, the skewness and the kurtosis are extracted from the decomposed signal, which characterize the non-Gaussian features of the signal. The support vector machine (SVM) or the back-propagation (BP) neural network is applied to fuse the features extracted from the multiple distorted receivers respectively and then determine the unknown emitter. In the second group of the proposed schemes, an approach based on the long short term memory (LSTM) is proposed. The LSTM model learns the deep features rather than the specific non-Gaussian features from the pre-processed signal. In contrast to the first group, the features used to identify the unknown emitter are extracted directly from the pre-processed signal by the trained LSTM model. Simulation results show that the proposed multi-receiver cooperative schemes can achieve the diversity gain in the identification performance. Moreover, we evaluate the identification performance of the proposed schemes in various channels, including the Gaussian channel and the fading channel. Compared to the existing methods based on different time-frequency representations, the proposed schemes possess the merits of high identification accuracy and low complexity. The significance of this paper is that the receive diversity can be achieved by the proposed schemes by using multiple distorted receivers even without compensating the receiver distortion prior to the identification.

This paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices. We take the network slicing technology to solve the problems of network spectral efficiency and URLLC reliability. A mixed-integer programming problem is formulated by maximizing the spectral efficiency of the system in the constraint of users’ requirements for two slices, i.e., the requirement of the eMBB slice and the requirement of the URLLC slice with a high probability for each user. By transforming and relaxing integer variables, the original problem is approximated to a convex optimization problem. Then, we combine the objective function and the constraint conditions through dual variables to form an augmented Lagrangian function, and the optimal solution of this function is the upper bound of the original problem. In addition, we propose a resource allocation algorithm that allocates the network slicing by applying the Powell–Hestenes–Rockafellar method and the branch and bound method, obtaining the optimal solution. The simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm. Furthermore, we analyze the spectral efficiency of the proposed algorithm with the users’ requirements change of two slices and get better spectral efficiency performance.

Joint transmitter-receiver spatial modulation (JSM) is an appealing transmission scheme in the spatial modulation family, where transmit diversity, receive diversity, and multiplexing gain are achieved simultaneously. However, the conventional JSM technique is neither spectrum efficient nor antenna efficient due to its limited candidates of bit-to-antenna mapping. In this paper, we propose a novel JSM approach that involves more mapping candidates to relax the implementation constraint and achieve better reliability. The proposed JSM scheme maximizes the minimum Euclidean distance (MED) between the received signals of different antenna mappings by optimizing the antenna selection and the power allocation over the transmit antennas, named MED-JSM. In particular, an optimal closed-form power allocation solution is derived when there are two receive antennas. For more receive antennas, a lower bound of the minimum Euclidean distance is derived, and a corresponding lower bound-approaching power allocation algorithm is proposed accordingly. Then, the proposed power allocation algorithm and antenna selection are used in a hybrid manner to further enhance the system reliability. Since the optimal MED-JSM requires an exhaustive search over all the received signals, we propose a suboptimal scheme with reduced computational complexity, which shrinks the search space via the Rayleigh-Ritz theorem. Numerical results show that the proposed MED-JSM outperforms the conventional JSM schemes in system's reliability.

The artificial noise (AN) aided multi-beam directional modulation (DM) technology is capable of wireless physical layer secure (PLS) transmissions for multiple desired receivers in free space. The application of AN, however, makes it less power-efficient for such a DM system. To address this problem, the weighted fractional Fourier transform (WFRFT) technology is employed in this paper to achieve power-efficient multi-beam DM transmissions. Specifically, a power-efficient multi-beam WFRFT-DM scheme with cooperative receivers and a power-efficient multi-beam WFRFT-DM scheme with independent receivers are proposed based on frequency diverse array (FDA), respectively. The bit error rate (BER), secrecy rate, and robustness of the proposed multi-beam WFRFT-DM schemes are analyzed. Simulations demonstrate that 1) the proposed multi-beam WFRFT-DM schemes are more power-efficient than the conventional multi-beam AN-DM scheme; 2) the transmission security can also be guaranteed even if the eavesdroppers are located close to or the same as the desired receivers; and 3) the proposed multi-beam WFRFT-DM schemes are capable of independent transmissions for different desired receivers with different modulations.

This paper investigates the robust transceiver design in a wireless multiple-input multiple-output (MIMO) switching network in which multiple users exchange messages via a multi-antenna relay. Previous works assume that perfect channel state information (CSI) is known at the relay, which is intractable in practice. In this paper, channel uncertainty is considered in both frequency division duplex (FDD) and time division duplex (TDD) systems. Regarding different types of CSI imperfection of uplink and downlink transmission in FDD, a statistical and norm-bounded uncertainty models are adopted to characterize the imperfect CSI of uplink and downlink, respectively. In contrast to FDD, the uplink and downlink channels are reciprocal in TDD, and an identical statistical model is adopted for both uplink and downlink channel uncertainty. For each duplex system, an optimization problem is formulated by minimizing the worst-case mean square error (MSE) with respect to channel uncertainty in the constraint of the maximum transmit power of the relay. In FDD, since the problem is non-convex and difficult to solve, we divide the original problem into two subproblems in which the channel uncertainty of uplink and downlink are treated individually. For the uplink subproblem, we propose an iterative approach to determine a closed-form solution of the robust transceiver. In addition, a Sylvester equation is formulated which closed-form solution is provided explicitly for the downlink channel uncertainty subproblem. An overall iterative algorithm is proposed by combining the two algorithms for the subproblems, which can solve the original problem efficiently. Moreover, in TDD, the optimization problem is non-convex and difficult to solve as well. However, the involved channel uncertainty reduces to uplink only due to the reciprocity of uplink and downlink. We propose an iterative algorithm directly for the overall problem to determine the robust transceiver for TDD systems. The simulation results show that the proposed iterative algorithms reduce the sum MSE efficiently and outperform the existing schemes in the channel uncertain scenarios, which validate our conclusion.

As the advent of cognitive radios, blind encoder identification has attracted increasingly attentions since it plays an important role in blind signal processing. The existing works mainly focus on additive white Gaussian noise (AWGN) channel, while the blind identification in multipath scenarios has not been sufficiently investigated. In this paper, we consider the blind low-density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel. Then, a likelihood-based classifier is proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters, including multipath fading channel and modulated symbols. Then, we adopt an average log-likelihood ratio (LLR) estimator to classify the unknown encoder. Numerical results show that the proposed algorithm provides promising identification performance in multipath channels, especially in the low signal- to-noise ratio region.

This paper investigates the transceiver design in a wireless multiple-input multiple-output (MIMO) switching network in which multiple users exchange messages via a multi-antenna relay. Previous work assumed that perfect channel state information (CSI) is known at the relay, which is intractable in practice. Regarding different types of CSI imperfection of uplink and downlink transmission, a statistical and a norm- bounded uncertainty model are adopted to characterize the imperfect CSI of uplink and downlink respectively for the transceiver design. An optimization problem is formulated by minimizing the worst-case mean square error (MSE) with respect to channel uncertainty in the constraint of the maximum transmit power of the relay. Since the problem is non-convex and difficult to solve, we divide the original problem into two subproblems in which the channel uncertainty of uplink and downlink are treated individually. For the uplink subproblem, we propose an iterative approach to determine a closed- form solution of the robust transceiver. In addition, a Sylvester equation is formulated which closed-form solution is provided explicitly for the downlink channel uncertainty subproblem. An overall iterative algorithm is proposed by combining the two algorithms for the subproblems, which can solve the original problem efficiently in a low complexity. Simulation results show that the proposed iterative algorithm reduces the sum MSE significantly in the channel uncertain scenarios.

In this paper, a non-time-slotted cognitive radio network (CRN) is considered, where the primary user (PU) can change its state between transmission and mute at any time within one secondary user (SU) frame. In such a case, the traditional half-duplex sensing, which divides the secondary transmission process into the sensing and transmission stages, has a problem that fails to detect the PU transition during the transmission stage. To overcome this problem, the full-duplex sensing is adopted, which allows the SU to sense the spectrum and transmit signals simultaneously. The SU throughput per frame is evaluated to determine the optimal duration of the SU frame. Note that the throughput scales with the duration of the SU frame in the absence of the PU. However, the PU may start to transmit at any time within the SU frame. Hence, we formulate a throughput optimization problem with respect to the duration of the SU frame, under the constraint that the interference induced by the SU does not exceed the interference temperature of the PU, which is nonconvex. We further indicate that the optimal solution can be achieved when considering some practical constraints.

In this paper, a wireless wideband downlink transmission through a multipath and highly time-varying channel is considered for high-speed railway. In this scenario, non-Gaussian noise is generally involved at the receiver side which may lead to communication outage. In previous studies, the optimal receiver algorithms we designed are only applicable for Gaussian noise and time-invariant channels. But for impulsive noise and time-varying multipath channels, new algorithms are needed to be proposed. In order to resolve the issue, a synthesis scheme is proposed in this paper. First, we adopt impulsive noise detection algorithm to discriminate impulsive noise from the received signal. Then the discriminated noise can be suppressed by the blanking algorithm. After that, the refined signal goes through a well-designed beamforming network, which transforms the received signal with varying frequency offsets into an angle domain. Thus, the frequency offset in each angle is near-constant which can be accurately estimated. The simulation results indicate that the proposed scheme significantly improves the reliable performance of the high-speed railway communication systems.

In industrial internet of things (IIoT), massive machine-type communications (mMTC) system is introduced to provide communication services for large-scale industrial devices. To achieve massive access with scarce radio resources in mMTC, multiple access protocol has to be reconsidered to cope with the resulting collision problem. Currently, various novel multiple access schemes have been proposed. Among them, coded tandem spreading multiple access (CTSMA) is an emerging physical (PHY) layer multiple access scheme to resolve the collision in mMTC. In this paper, a multislot design scheme of CTSMA is proposed to further promote the collision resolution capability. In this scheme, CTSMA is combined with the MAC layer scheme coded slotted ALOHA (CSA). The analysis shows that the multislot design can effectively take advantage of CTSMA and CSA to enhance the mMTC system performance for the short uplink contention period, which is suitable for the IIoT applications.

Ultra-dense small cells operating in a cooperative manner, such as cloud radio access network (C-RAN), have been introduced to serve the numerous mobile devices in the next generation wireless networks. One of the major challenges in efficiently operating the C-RAN in a cooperative manner is the excessive overhead signaling and computation load, such as downlink channel state information (CSI) acquisition at the cloud for cooperative downlink transmission, which scales rapidly with the size of the network. In this paper, the exploitation of mobility information of devices is proposed to address the challenge of efficiently operating the C-RAN. We introduce a mobility-assisted CSI acquisition method to complement conventional pilot-based CSI acquisition methods. This method enables the C-RAN to avoid excessive overhead signaling. A low-complexity algorithm is designed to maximize the sum rate of all devices subject to the limits of backhaul capacity and base station transmit power. An adaptive algorithm has also been proposed to improve the robustness against channel uncertainty. Both theoretical and numerical analyses show that the exploitation of mobility information provides a new dimension to improve conventional transmission schemes for next-generation massive cooperative networks.

With the thriving of internet of things (IoT) industry, the importance of massive machine type communication (mMTC) is increasingly significant. Due to massive connections in mMTC, grant-free random access is preferred for the sake of saving the control signaling overheads. Currently, various techniques has been proposed to address the challenges in the grant-free random access system. Among them, tandem spreading is a novel spreading mechanism to solve the collision problem. In this paper, a generalized version of tandem spreading is introduced as the coded tandem spreading multiple access (CTSMA). Compared to the previous work, CTSMA enables multiple redundancy segments to support more simultaneous transmissions. Moreover, a flexible tandem spreading codebook design is introduced. Simulation results show the advantage of CTSMA on other related schemes and indicate the factors to influence the CTSMA performance under different conditions.

One of the major challenges in effectively operating a cloud radio access network (C-RAN) is the excessive overhead signaling and computation load that scale rapidly with the size of the network. In this paper, the exploitation of location information of the mobile devices is proposed to address this challenge. We consider an approach in which location-assisted channel state information (CSI) acquisition methods are introduced to complement conventional pilot-based CSI acquisition methods and avoid excessive overhead signaling. A low-complexity algorithm is designed to maximize the sum rate. An adaptive algorithm is also proposed to address the uncertainty issue in CSI acquisition. Both theoretical and numerical analyses show that location information provides a new dimension to improve throughput for next-generation massive cooperative networks.

With the advent of cognitive radio (CR) and dynamic spectrum access techniques, where multiple signals may coexist within the same frequency band, multiuser modulation classification problem becomes a vital issue, which has not been sufficiently investigated. In this paper, we consider a cooperative multiuser modulation classification problem, in the presence of unknown multipath channels. A likelihood-based (LB) classifier using the expectation-maximization (EM) algorithm is proposed, which enables to find the maximum likelihood estimates (MLEs) iteratively. Numerical results show that the proposed algorithm achieves significant improvement on the classification performance with a small number of samples when compared to the conventional methods, which demonstrates its reliability and efficiency of identifying modulations of multiple users under the multipath scenarios.

Massive machine-type communication (MMTC) is the key technology to meet the flourishing Internet of things industry. One prominent characteristic of MMTC is massive connections which brings a critical challenge of multiple access interference (MAI) on channel estimation, user identification, and data detection. Currently, various techniques have been proposed to mitigate the MAI, but those MAI alleviations are limited. Therefore, this paper proposes a novel technique called tandem spreading network-coded division multiple access (TSNDMA) to effectively cope with the MAI problem. In this technique, first a novel channel-reciprocity-based precompensation scheme is proposed for channel estimation. Then, a tandem spreading mechanism and a physical-layer network coding-based redundancy design are introduced for user identification and data detection with corresponding algorithms at the receiver. Simulation results show that TSNDMA can effectively mitigate the MAI to achieve a favorable system performance.

In this paper, we investigate the cooperative modulation classification problem under multipath scenarios with blind channel information. Multipath channels cause severe degradation on the modulation classification performance, which has not yet been thoroughly solved in the existing literature. To address this issue, a likelihood-based classifier using the expectation-maximization algorithm is proposed, which is capable of finding the maximum likelihood estimates of unknown parameters in a tractable way. Furthermore, to evaluate the upper bound performance of the proposed algorithm, the Cramér-Rao lower bounds of the joint estimates of unknown parameters are derived. Extensive simulations show that the classification performance of the proposed algorithm with good initialization scheme is close to the performance upper bound in the high signal-to-noise ratio region. The results also demonstrate that the proposed algorithm provides significant performance improvement in the multipath channels compared with conventional approaches.

With the explosive development in Internet of Things (IoT) and Internet of Vehicles (IoV) technologies, massive connections with sporadic transmission will commonly exist in the future communication network. Upon that, compressive sensing based multi-user detection (CS-MUD) technique was proposed in previous works. However, CS-MUD has not been considered in high mobility scenario which will be widely applied in the future communication. With the existence of high mobility, the frequency synchronization is no longer valid because the Doppler shift appears and results in the carrier frequency offset (CFO). In this paper, CS-MUD will be analyzed and evaluated in high mobility scenarios. It can be shown that the user activity detection of CS-MUD will be influenced by the CFO from the Doppler shift. In addition, constant amplitude zero auto-correlation sequences (CAZAC) are applied as the spreading sequences in this paper. The correlation property of the CAZAC sequences can help the CS-MUD to mitigate the CFO sensitivity to make the system robust to the mobility.

Modulation classification is crucial in applications such as electronic warfare and interference cancellation. In this letter, a novel feature-based Kolmogorov-Smirnov classifier is proposed for the identification of the modulation formats. The received signal is first preprocessed with a folding operation that helps identify the modulation formats based on their different axes of symmetry. Simulation results show that the performance of the proposed classifier is close to that of the optimal likelihood-based classifier, while its robustness to noise uncertainty is improved and its computational complexity is reduced compared to that of the optimal likelihood-based classifier.

In this paper, we investigate the specific emitter identification (SEI) problem, which distinguishes different emitters using features generated by the nonlinearity of the power amplifiers of emitters. SEI is performed by measuring the features representing the individual specifications of emitters and making a decision based on their differences. In this paper, the SEI problem is considered in both single-hop and relaying scenarios, and three algorithms based on the Hilbert spectrum are proposed. The first employs the entropy and the first- and second-order moments as identification features, which describe the uniformity of the Hilbert spectrum. The second uses the correlation coefficient as an identification feature, by evaluating the similarity between different Hilbert spectra. The third exploits Fisher's discriminant ratio to obtain the identification features by selecting the Hilbert spectrum elements with strong class separability. When compared with the existing literature, we further consider the identification problem in a relaying scenario, in which the fingerprint of different emitters is contaminated by the relay's fingerprints. Moreover, we explore the identification performance under various channel conditions, such as additive white Gaussian noise, non-Gaussian noise, and fading. Extensive simulation experiments are performed to evaluate the identification performance of the proposed algorithms, and results show their effectiveness in both single-hop and relaying scenarios, as well as under different channel conditions.

A novel approach for specific emitter identification using Hilbert spectrum is proposed for both single-hop and relaying scenarios. In particular, two features, i.e., the energy entropy and color moments, are extracted from the Hilbert spectrum of the signal of interest as identification features. The spectrum is obtained through the Hilbert-Huang transform, which is a powerful tool for the analysis of non-linear and nonstationary signals by decomposing them into a set of intrinsic mode functions. The identification task is solved by applying the support vector machine. We further extend the identification problem to a relaying scenario, in which the fingerprint of different emitters may be contaminated by the relay's fingerprints. To the best of our knowledge, this case has not been investigated so far in the literature. At last, simulation results validate that the proposed approach can effectively cope with the specific emitter identification problems in both single-hop and relaying scenarios.

We investigate the degrees of freedom (DoF) and secrecy DoF for a general framework of multiway relay networks named wireless MIMO switching, where a number of users exchange information via a common relay. Each round of data exchange consists of one uplink transmission from users to relay and one downlink transmission from relay back to users. The data exchange model is unicast, i.e., every user transmits one message and intends to receive one message from one other user. We categorize unicast patterns using the notion of orbit borrowed from abstract algebra. Roughly speaking, an orbit is a minimum subset of users such that data exchange is closed within this subset. We analyze the achievable DoF of wireless MIMO switching with various numbers of orbits. Particularly, the DoF capacity for unicast with one and two orbits are established. Furthermore, we study communication secrecy with an untrusted relay in wireless MIMO switching. We present an achievable secrecy sum rate and the corresponding achievable secrecy DoF by assuming a non-regenerative relay. Then, we focus on unicast patterns with one and two orbits, and show that this achievable lower bound is actually the secrecy DoF capacity based on a novel genie-aided technique. Our results build a bridge between the DoF and the secrecy DoF in multiway relaying. The methodology of the proof can be generally applied to analyze the secrecy DoF in other relay networks.

In this paper, we investigate the artificial noise-aided jamming design for a transmitter equipped with large antenna array in Rician fading channels. We figure out that when the number of transmit antennas tends to infinity, whether the secrecy outage happens in a Rician channel depends on the geometric locations of eavesdroppers. In this light, we first define and analytically describe the secrecy outage region (SOR), indicating all possible locations of an eavesdropper that can cause secrecy outage. After that, the secrecy outage probability (SOP) is derived, and a jamming-beneficial range, i.e., the distance range of eavesdroppers which enables uniform jamming to reduce the SOP, is determined. Then, the optimal power allocation between messages and artificial noise is investigated for different scenarios. Furthermore, to use the jamming power more efficiently and further reduce the SOP, we propose directional jamming that generates jamming signals at selected beams (mapped to physical angles) only, and power allocation algorithms are proposed for the cases with and without the information of the suspicious area, i.e., possible locations of eavesdroppers. We further extend the discussions to multiuser and multi-cell scenarios. At last, numerical results validate our conclusions and show the effectiveness of our proposed jamming power allocation schemes.

Amplify-and-forward (AF) based two-way relaying (TWR) has shown its sum rate advantage over AF based one-way relaying (OWR) in the literature. However, this advantage can not be achieved in some cases. In this paper, we evaluate the sum rates of AF based OWR/TWR and provide the relationship with respect to the relative channel gains and the relative transmit power of the three nodes, i.e., the relay node and two source nodes. Upon this, we determine the metric on choosing OWR or TWR given instantaneous channel state information and we further devise power allocation schemes. At last, the numerical results validate our conclusion and reveal some interesting facts.

This paper addresses relay design for a wireless multiple-input-multiple-output (MIMO) switching scheme that enables data exchange among multiple users. Here, a multi-antenna relay linearly precodes the received (uplink) signals from multiple users before forwarding the signal in the downlink, where the purpose of precoding is to let each user receive its desired signal with interference from other users suppressed. The problem of optimizing the precoder based on sum-rate maximization criteria is typically non-convex and difficult to solve. The main contribution of this paper is that we show the sum-rate maximization problem can be converted to an equivalent weighted sum-MSE minimization problem and can therefore be solved using an iterative algorithm proposed in our previous work. Asymptotic analysis reveals that, with properly chosen initial values, the proposed iterative algorithms are asymptotically optimal in both high and low signal-to-noise-ratio (SNR) regimes for MIMO switching, either with or without self-interference cancellation (a.k.a., physical-layer network coding). Numerical results show that the optimized MIMO switching scheme based on the proposed algorithms significantly outperforms existing approaches in the literature.

In this paper, we consider a bidirectional cellular relay network with distributed relays where a single base station exchanges information with multiple independent users through multiple single-antenna relays. We design the transceivers at the base station, the relays, and the users. The related optimization problems are generally non-convex and difficult to solve. In this paper, we propose a unified framework to design the transceiver algorithms based on two criteria, i.e. weighted sum MSE minimization and sum rate maximization. Specifically, we show that the sum rate maximization problem can be converted into an iterative weighted sum MSE minimization problem. Low-complexity iterative algorithms are developed for both weighted sum MSE minimization and sum rate maximization optimization problems. However, the convergence points of the proposed iterative algorithms are sensitive to the initial conditions, especially in the high signal-to-noise ratio (SNR) regime. For this reason, we further derive the high-SNR asymptotically optimal solutions and use them as the initials for the proposed iterative algorithms. Simulation results show that the proposed scheme can approximately double the system throughput, compared to the conventional four-stage transmission schemes.

This paper addresses joint transceiver and relay design for a wireless multiple-input multiple-output (MIMO) switching scheme that enables data exchange among multiple users. Here, a multiantenna relay linearly precodes the received (uplink) signals from multiple users and forwards the signal in the downlink, where the purpose of precoding is to let each user receive its desired signal with interference from other users suppressed. The problem of optimizing the precoder based on various design criteria is typically nonconvex and difficult to solve. The main contribution of this paper is a unified approach to solve the weighted sum mean square error (MSE) minimization and weighted sum rate maximization problems in MIMO switching. Specifically, an iterative algorithm is proposed for jointly optimizing the relay's precoder and the users' receive filters to minimize the weighted sum MSE. It is also shown that the weighted sum rate maximization problem can be reformulated as an iterated weighted sum MSE minimization problem and can, therefore, be solved similarly to the case of weighted sum MSE minimization. With properly chosen initial values, the proposed iterative algorithms are asymptotically optimal in both high- and low-signal-to-noise-ratio regimes for MIMO switching, either with or without self-interference cancellation (a.k.a., physical-layer network coding). Numerical results show that the optimized MIMO switching scheme based on the proposed algorithms significantly outperforms existing approaches in the literature.

In a generic switching problem, a switching pattern consists of a one-to-one mapping from a set of inputs to a set of outputs (i.e., a permutation). We propose and investigate a wireless switching framework in which a multi-antenna relay is responsible for switching traffic among a set of N stations. We refer to such a relay as a MIMO switch. With beamforming and linear detection, the MIMO switch controls which stations are connected to which stations. Each beamforming matrix realizes a permutation pattern among the stations. We refer to the corresponding permutation matrix as a switch matrix. By scheduling a set of different switch matrices, full connectivity among the stations can be established. In this paper, we focus on “fair switching” in which equal amounts of traffic are to be delivered for all the N(N - 1) ordered pairs of stations. In particular, we investigate how the system throughput can be maximized. In general, for large N the number of possible switch matrices N! is huge, making the scheduling problem combinatorially challenging. We show that for N = 4 and 5, only a subset of N - 1 of the N! switch matrices need to be considered in the scheduling problem to achieve good throughput. We conjecture that this will be the case for large N as well. This conjecture, if valid, implies that for practical purposes, fair-switching scheduling is not an intractable problem.

We consider a wireless relay network in which multiple single-antenna users communicate with each other through a multi-antenna relay. With our earlier proposed framework of zero-forcing relaying, the inter-user interferences are canceled. In this paper, we investigate power allocation schemes for both regenerative and non-regenerative relays to improve the throughput performance. Considering different traffic demand of each user, a queue-aware power allocation (QPA) scheme is proposed to stabilize the queue of each user by maximizing a “sum demand” metric. The demand metric of each user is proportional to both its channel throughput and its queue length. The fairness in the sense of transmission delay or packet loss ratio is thus achieved as queue length is associated with the two performances. The power allocation problem schemes use Lagrangian method and bisection search. With the QPA scheme, the system throughput performance is improved while keeping all queues relatively stable. The simulation results indicate that the QPA scheme strikes a balance among the fairness of all the users and improve the system throughput.

A wireless relay which forms a one-to-one mapping from the inputs (uplinks) to the outputs (downlinks) is called a multiple-input-multiple-output (MIMO) switch. The MIMO switch carries out precode-and-forward, where all users send their signals in the uplink and then the MIMO switch precodes the received vector signal for broadcasting in the downlink. Ideally, each user employs a receive filter to recover its desired signal from one other user with no or little interference from other users. We propose a joint design of the precoder and the receive filters to achieve the minimum-mean-square-error (MMSE), assuming full channel state information is available at the relay. Our results indicate that the proposed MMSE relaying scheme outperforms the existing ZF/MMSE schemes.

Consider a wireless network, in which multiple users exchange data with each other via a multi-antenna relay. The users transmit to the relay simultaneously. Then the relay regenerates the transmitted signal and precodes it before broadcasting to the users. In particular, we propose a minimum mean square error (MMSE) based precoder, which takes the relay detection errors into account. We indicate that this precoder can improve the bit error performance when uplink channel is in a bad condition. Afterwards, we deploy the ordered MMSE successive interference cancellation (MMSE-SIC) receiver, which exploits temporal diversity gains over the multiple downlink slots. Simulation results show that the ordered MMSE-SIC receiver can achieve sufficient diversity gains over traditional MMSE receiver.

A wireless relay with multiple antennas is called a multiple-input-multiple-output (MIMO) switch if it maps its input links to its output links using "precode-and-forward." Namely, the MIMO switch precodes the received signal vector in the uplink using some matrix for transmission in the downlink. This paper studies the scenario of K stations and a MIMO switch, which has full channel state information. The precoder at the MIMO switch is either a zero-forcing matrix or a network-coding matrix. With the zero-forcing precoder, each destination station receives only its desired signal with enhanced noise but no interference. With the network-coding precoder, each station receives not only its desired signal and noise, but possibly also self-interference, which can be canceled. Precoder design for optimizing the received signal-to-noise ratios at the destinations is investigated. For zero-forcing relaying, the problem is solved in closed form in the two-user case, whereas in the case of more users, efficient algorithms are proposed and shown to be close to what can be achieved by extensive random search. For network-coded relaying, we present efficient iterative algorithms that can boost the throughput further.

Kolmogorov-Smirnov (K-S) test-a non-parametric method to measure the goodness of fit, is applied for automatic modulation classification (AMC) in this paper. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic derived from the received signal, and comparing it with the CDFs of the signal under each candidate modulation format. The K-S-based modulation classifier is first developed for AWGN channel, then it is applied to OFDM-SDMA systems to cancel multiuser interference. Regarding the complexity issue of K-S modulation classification, we propose a low-complexity method based on the robustness of the K-S classifier. Extensive simulation results demonstrate that compared with the traditional cumulant-based classifiers, the proposed K-S classifier offers superior classification performance and requires less number of signal samples (thus is fast).

In multiuser cognitive radio (CR) network, we address the problem of joint transmit beamforming (BF) and power control (PC) for secondary users (SUs) when they are allowed to transmit simultaneously with primary users (PUs). The objective is to optimize the network sum rate under the interference constraints of PUs. Due to lack of cooperation among different nodes in the network, channel uncertainty is considered. In a worst case philosophy, a closed-form worst-case expression is derived, with which the uncertainty optimization problem is transformed into a certain one. Second-order cone programming approximation (SOCPA) method is proposed as a robust algorithm. Typical network models are approximated to second-order cone programming problems and solved by interior-point method. Finally the network sum rates for different PU and SU numbers are assessed for both certainty and uncertainty channel models by simulation.

In multiuser cognitive radio (CR) network, we address the problem of joint transmit beamforming (BF) and power control (PC) for secondary users (SUs) when they are allowed to transmit simultaneously with primary users (PUs). The objective is to optimize the network sum rate under the interference constraints of PUs, which is nonconvex problem. Iterative dual subgradient (IDuSuG) algorithm is proposed to solve such problems. It iteratively performs BF and PC to optimize the sum rate, among which minimum mean square error (MMSE) or virtual power-weighed projection (VIP2) is used to design beamformers and subgradient method optimizes the PC. VIP2algorithm is devised for the case of the interference caused by MMSE beamformer over the threshold. Channel uncertainty is considered and robust algorithm is provided by modifying updates in iterative process. Finally the network sum rates for different PU and SU numbers are assessed for both certainty and uncertainty channel model by simulation.

A new approach to modulation classification based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic derived from the received signal, and comparing it with the CDFs or the ECDFs of the signal under each candidate modulation format. The K-S-based modulation classifiers are developed for various channels, including the AWGN channel, the flat-fading channel, the OFDM channel, and the channel with unknown phase and frequency offsets, as well as the non-Gaussian noise channel, for both QAM and PSK modulations. Extensive simulation results demonstrate that compared with the traditional cumulant-based classifiers, the proposed K-S classifiers offer superior classification performance, require less number of signal samples (thus is fast), and is more robust to various channel impairments.

This paper presents a general detection method for linearly constructed distributed space-time codes (DSTC) in amplify-and-forward (AF) mode of wireless cooperation networks. A two-phase model for the network is utilized. In the first phase, multiple-antenna transmit nodes send signals simultaneously. Then the relays encode received signals with linearly constructed DSTC. Since linear dispersion (LD) codes subsume all linearly constructed STCs as special cases, when such DSTCs are used in AF mode of the network, a LD equivalent system model which is similar to V-BLAST structure is given in this paper. Thus, detection algorithms for V-BLAST can be utilized for the model effectively. Especially, linear complexity detection algorithms can be used in the network for practical implementations. Simulation results prove the LD equivalent model valid at last.

This paper investigates the multiple-input multiple-output (MIMO) antenna subset selection combined with space-time coding (STC) in retransmit system, e.g. hybrid automatic repeated request (HARQ). As we know space-time coding (STC), transmit antenna selection (TAS), as well as some receive diversity combining techniques such as selection combing (SC) and maximal ratio combining (MRC) offer considerable diversity gain. Therefore, they are combined in a system named TAS/STBC/HARQ which has been investigated in this paper. The performance of bit error rate (BER) is analyzed in flat fading channel. The numerical results based on the SC and MRC reveal that the scheme achieves great diversity gain. The diversity gain is greater than the MIMO transmit antenna subset selection with STC, when no HARQ is utilized, except for little coding gain loss and larger time delay. Moreover, our system also has the advantage of throughput with turbo encoder.

This paper introduces two kinds of receivers, chip-level receivers and symbol-level receivers with Grake receivers. We propose an adaptive finger selection scheme of symbol- level receivers and it reduces almost a half of original complexity. When channel paths are dense, for symbol-level receivers with non-ideal channel estimation, we develop a receive scheme which combines channel estimation and receive finger selection. It increases system reliability effectively. We introduce serial interference cancellation (SIC) and soft-decision SIC to suppress multiple antennas interference. At the same time, it refers that soft-decision is necessary for V-BLAST structure of multiple antennas multiplexing. At last, simulation results prove the conclusion valid in this paper.