The upcoming generation of Internet of Things (IoT) technology requires the seamless integration of two fundamental modules: communication for information sharing and computation for data processing. In parallel, security and privacy are essential requirements for future IoT deployments. Thus, taking effective measures to address security concerns in complex IoT networks is evolving into a valuable research topic. Driven by these demands, we investigate the physical layer security robust resource allocation issue in cooperative relay-assisted integrated communication and over-the-air computation networks for IoT applications. Moreover, we consider a practical scenario where only imperfect channel state information is available. To guarantee the communication security between IoT sensors and the legitimate base station, we propose to jointly design the transmit and receive processing factors, along with relay amplification coefficients. Specifically, we formulate a robust non-convex optimization problem that maximizes the sum achievable secrecy rate, subject to transmit power constraints at sensors and relays, as well as the maximal tolerable computation error constraint of the legitimate base station. Subsequently, to address the intractably robust non-convex problem, we propose an efficient generalize Lagrangian dual transform based algorithm with cutting-set method. To demonstrate the robustness and effectiveness of our proposed algorithm, two benchmark algorithms are considered. The first is the weighted minimum mean square error based algorithm that incorporates the cutting-set method, while the second is the conservative approximation based algorithm. In comparison to the former, our proposed algorithm demonstrates faster convergence; in relation to the latter, it obtains superior performance.

In wireless communications with high mobility, an interpolation-based channel estimation scheme shows the feasibility of windowed orthogonal frequency division multiplexing (OFDM) systems, where severe inter-carrier interference (ICI) is mitigated by a wrap-around based frequency-varying Viterbi detection scheme. In the interpolation-based channel estimation, insertion of pilot and null subcarriers leads to a decrease in available spectral rate. To reduce null subcarriers and thus improve available spectral rate, we propose an iterative channel estimation and signal decoding scheme, which mitigates the ICI introduced by reduction of null subcarriers. In the proposed iterative scheme, an initial coarse channel estimation procedure is conducted before the iterative channel estimation and signal decoding procedure. Two procedures are skillfully designed to prevent error propagation. Furthermore, the properties of raised-cosine roll-off window function are exploited to design the proposed scheme, where repetition data transmissions are proposed to safeguard against decoding errors. Simulation results demonstrate that the available spectral rate increases at the price of tolerable performance degradation.

In this paper, we study a multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA) downlink system, where a base station employs beamforming to transmit multiple data streams simultaneously to a central user and a cell-edge user. Over each beam, the orthogonal frequency division multiplexing (OFDM) modulated signals intended for both users are superimposed and then transmitted. Because of beamforming and OFDM modulation, the central user may have weaker beams compared with the cell-edge user. This causes the unsuccessful successive interference cancellation (SIC). To solve this problem, we derive the necessary and sufficient conditions to ensure the successful SIC for arbitrary beamforming matrices. Based on these conditions, we design a specialized beamforming structure where signals over weak beams can be orthogonally separated from those over strong ones. We propose that joint encoding and decoding are applied to signals over weak beams and similarly to those over strong ones. To optimize the beamforming matrices, we propose a constrained convex concave procedure based algorithm and a matrix fractional programming based algorithm. It is verified through simulation results that the proposed scheme performs better than the conventional OFDM-NOMA scheme.

This paper characterizes the physical layer security (PLS) in a network utilizing massive multiple-input multiple-output (MIMO) for over-the-air computation (AirComp). When the direct links between the access point (AP) and the sensors are blocked, an intelligent reflecting surface (IRS) is employed to establish communication. Furthermore, the AP sends artificial noise (AN) to the eavesdropper to prevent wiretapping. We study the problem of minimizing the mean-square-error (MSE) between the original and intercepted signals subject to the transmit power constraints at the AP and the sensors, as well as how the MSE threshold hinders the eavesdropper under both perfect and imperfect channel state information (CSI). In the case of perfect CSI, obtaining a globally optimal solution for the investigated non-convex problem is challenging due to the optimization variables’ couple nature. Hence, we convert the problem into two sub-problems to obtain locally optimal solutions. One sub-problem can be solved by an exact penalty-based algorithm, while the other has a closed-form solution using the popular majorization-minimization (MM) algorithm. For the imperfect CSI, the robust beamforming optimization problem formulated is still non-convex. To address this, we harness the block coordinate descent (BCD) algorithm for alternately optimizing the variables to solve it. The results of our simulations demonstrate that the superior MSE performance exhibited by the proposed scheme.

With the rapid evolution of wireless technologies, the deep integration of sensing, communication and computation has heralded a novel and promising paradigm. In this paper, we propose a secure beamforming design framework for integrated sensing, non-orthogonal multiple access (NOMA) communication and over-the-air computation (AirComp) networks, which can provide multi-functional intelligent services for communication-intensive, computation-intensive, delay-sensitive and security-sensitive applications. In the considered network, each dual-functional intelligent device engages in NOMA information transmission and AirComp. Meanwhile, the triple-functional base station conducts target sensing, NOMA signal decoding and data aggregation simultaneously. Our aim is to maximize the sum secrecy rate (SSR) of NOAM devices while ensuring that the quality of service requirements for both sensing and AirComp are met within the transmit power constraints imposed on all nodes. The formulated optimization problem involves coupled variables and logarithmic determinant, thus it is highly non-convex. To solve it, we propose an efficient matrix-extended generalized Lagrangian dual transformation based algorithm with penalty method, which can obtain the Karush-Kuhn-Tucker (KKT) solution to the original problem with low-complexity and convergence guarantee. Additionally, the well-known successive convex approximation based algorithm is also employed to address the formulated SSR maximization problem. However, its computational complexity significantly exceeds that of our proposed algorithm. Finally, extensive experiments demonstrate the performance improvement of our proposal compared with the benchmark approaches.

As the number of blockchain platforms continues to grow, the independence of these networks poses challenges for transferring assets and information across chains. Cross-chain bridge technology has emerged to address this issue, establishing communication protocols to facilitate cross-chain interaction of assets and information, thereby enhancing user experience. However, the complexity of cross-chain transactions increases the difficulty of security regulation, rendering traditional single-chain detection methods inadequate for cross-chain scenarios. Therefore, understanding cross-chain transaction semantics is crucial, as it forms the foundation for cross-chain security detection tasks. Although there are existing methods for extracting transaction semantics specifically for single chains, these approaches often overlook the unique characteristics of cross-chain scenarios, limiting their applicability. This paper introduces XSema, a novel cross-chain semantic extraction framework grounded in asset transfer and message-passing, designed specifically for cross-chain contexts. Experimental results demonstrate that XSema effectively distinguishes between cross-chain and non-cross-chain transactions, surpassing existing methods by over 9% for the generality metric and over 10% for the generalization metric. Furthermore, we analyze the underlying asset transfer patterns and message-passing event logs associated with cross-chain transactions. We offer new insights into the coexistence of multiple blockchains and the cross-chain ecosystem.

In this paper, we consider a physical layer security issue in a multiple-input multiple-output (MIMO) relay assisted over-the-air computation (AirComp) network, where sensors send their sensing data to the access point (AP) via a harvest and forward relay, and an eavesdropper (Eve) attempts to wiretap the aggregation result of sensors’ sensing signals. In the first time slot, the AP applies artificial noise (AN) to protect the aggregation result from being wiretapped by the Eve, and the relay harvests energy from the received signal according to the power splitting protocol. In the second time slot, the relay forwards the received signal, containing AN and sensors’ sensing signals, to the AP. The channel state information (CSI) between each node is assumed to be imperfect, and the channel uncertainties are molded as bounded errors. Specifically, we investigate a joint secure beamforming and power splitting design to minimize the worst-case mean-square error (MSE) at the AP, subject to the worst-case MSE constraint of the Eve and the worst-case transmit power constraints at each node. Different from the common approach that ignores the higher-order terms of channel uncertainties caused by cascade channels, by exploiting the block coordinate descent (BCD) algorithm, cutting set (CS) method and constrained concave-convex procedure (CCCP), we propose a BCD-CS-CCCP algorithm attempting to solve the robust optimization problem while retaining the higher-order terms of channel uncertainties. Numerical results indicate the effectiveness of our proposed schemes.

With the popularity of Non-Fungible Tokens (NFTs), the high value of NFTs makes them a target for phishing scammers, which harms the security and reliability of the Web3 NFT ecosystem. Despite the significance of this issue, there is a lack of systematic research in the area of emerging NFT phishing scams. To address this gap, we are the first to conduct a case retrospective analysis and empirical measurement study of real-world historical NFT phishing scams on Ethereum. We collect and publicly release the first NFT phishing dataset which includes 1,625 NFT phishing accounts and transaction records as of August 2023. We further categorize the existing scams into four phishing patterns and investigate their distinguishable behaviors. Then, we reveal the modus operandi preferences and economic impacts to characterize NFT phishing scams. We find that NFT phishers stole 67,188 NFTs, with a total direct selling profit of${\$}$20.92 million. We also observe that scammers favor certain categories and collections of NFTs, coupled with signs of gang theft. Furthermore, we design a variety of account features for the classification task of NFT phishers based on empirical conclusions. Experimental results on real-world NFT transaction data demonstrate the effectiveness of these features in detecting NFT phishing accounts, and outperform traditional phishing detection methods with 41% average Precision and 44% average Recall.

Overdue risk detection of consumer loans is a critical issue faced by consumer finance companies. Unlike other types of loans, such as mortgage loans and guaranteed loans, consumer loans only regard personal credit as collateral. As a high overdue rate will result in economic losses to financial companies, it is of great significance for lenders to accurately detect risky customers. However, the large volume of credit data and the variety of customer characteristics make the risk detection via manual expert analysis rather challenging. Additionally, previous loan risk detection approaches based on machine learning classification neglect the relations between different customers, and traditional graph neural networks lack the exploration of loan overdue patterns. In this paper, we construct a heterogeneous graph based on real credit data from a consumer finance company. We analyze the distribution of meta-paths and propose a meta-path-based graph neural network that combines both lower-order and higher-order features. Experimental results show that our model is able to detect more risky customers by exploring overdue patterns and can achieve the best effect in the loan overdue detection task.

As one of the most active blockchain platforms at present, Ethereum attracts a great deal of interest, including that of fraudsters. They exploit the anonymity of Ethereum accounts to perpetrate varieties of scams, the most common of which is phishing frauds. However, existing phishing detection work ignores the heterogeneity of Ethereum transaction edges. In fact, the activities on Ethereum include external transactions, internal transactions, and token transactions. Therefore, this paper proposes an Ethereum account phishing fraud detection method named HTSGCN. Based on heterogeneous transaction subnets, our method makes full use of the type and direction information contained in transactions. First, we collect Ethereum transaction data and construct a k-order heterogeneous subnet for each account. To aggregate the neighbor feature, we design a message propagation mechanism based on graph convolution network. Finally, we classify node representation vectors containing neighborhood and its own characteristics. Experimental results show that HTSGCN has a better effect on detecting phishing accounts than previous work which is based on homogeneous networks.

In this paper, we investigate joint optimization of secure AirComp and reliable multicasting assisted by a multiple-input multiple-output untrusted two-way relay, where artificial noise is employed at the access point (AP) to interfere the relay for ensuring secure AirComp. We aim to minimize the computation distortion at the AP by jointly designing the transmit variables of all the nodes and the aggregation variables at the AP and relay, under the secure AirComp constraint, the reliable multicasting constraint, and the transmit power constraints of all the nodes. We consider two scenarios that perfect and imperfect channel state information (CSI) are available. For the former, the formulated optimization problem is highly nonconvex, and we propose an effective block coordinate descent (BCD)-penalty successive convex approximation (penaltySCA) method to solve the nonconvex problem. For the latter, we model the imperfect CSI by using worst-case criterion and the formulated robust optimization problem is much more challenging than its counterpart with perfect CSI. To solve the robust problem effectively, we first transform it to a deterministic optimization problem by employing some powerful mathematical lemmas, and then apply the proposed BCD-penaltySCA method to solve the reformulated deterministic problem. The proposed methods are shown by simulations to significantly reduce the computation distortion compared with other benchmarks under considering secure AirComp and reliable multicasting.

In this paper, we investigate the physical layer security of an untrusted relay assisted over-the-air computation (AirComp) network, where each node is equipped with multiple antennas and the relay is operated in an amplify-and-forward mode. The relay receives the data from each sensor and sends them to the access point (AP) in the first and second time slot, respectively. The AP applies artificial noise (AN) to protect the aggregation of sensors’ data from being wiretapped by the untrusted relay in the first time slot. In particular, we are interested in minimizing the computation distortion measured by the mean-squared error (MSE) via jointly optimizing beamforming matrices at all nodes, subject to the MSE constraint at the relay and individual power constraints at the AP, the relay and each sensor. In the case of the perfect channel state information (CSI), we convert the nonconvex MSE minimization problem into a difference-of-convex (DC) form and propose a constrained concave-convex procedure that can obtain a local minimum to solve the DC problem. We also generalize the framework to an imperfect CSI case where the additional interference term due to incomplete interference cancellation is considered, and the nonconvex robust MSE minimization problem is solved by a proposed inexact block coordinate descent algorithm. Numerical results are presented to show the effectiveness of our proposed schemes.

Due to the widespread use of smart contracts, Ethereum has become the second-largest blockchain platform after Bitcoin. Many different types of Ethereum accounts (ICO, Mining, Gambling, etc.) also have quite active trading activities on Ethereum. Studying the transaction records of these specific Ethereum accounts is very important for understanding their particular transaction characteristics, and further labeling the pseudonymous accounts. However, traditional methods are generally based on static and global transaction networks to conduct research, ignoring useful information about dynamic changes. Our work chooses six kinds of important account labels, and builds ego networks for each kind of Ethereum account. We focus on the interaction between the target node and neighbor nodes with temporal analysis. Experiments show that there is a significant difference between various types of accounts in terms of several network features, helping us better understand their transaction patterns. To the best of our knowledge, this is the first work to analyze the dynamic characteristics of Ethereum labeled accounts from the perspective of transaction ego networks.

This paper investigates the physical layer security issue in an amplify-and-forward (AF) multi-input multi-output (MIMO) two-way relay assisted cognitive radio (CR) nonorthogonal multiple access (NOMA) network, where the simultaneous wireless information and power transfer (SWIPT) technology is employed to improve network energy efficiency. We consider the scenario that a pair of primary users and two pairs of secondary users (SUs) exchange information via a MIMO two-way relay, where the edge SU of each SU pair is untrusted and tries to wiretap the central SU’s information. For ensuring security, we aim to maximize the sum achievable secrecy rate (SASR) by jointly optimizing the power allocation at all users, power splitting factor and relay beamforming subject to the quality of service (QoS), energy harvesting and transmit power constraints. The formulated optimization problem is highly nonconvex due to coupling variables, thus it is challenging to solve. An effective path-following (PF)-based algorithm is proposed, which is proven to converge to a stationary point. Theoretical and simulation results show that the proposed PF-based algorithm has lower complexity than the state-of-art algorithm. To further reduce complexity, we proposed a zero-forcing (ZF)-based scheme. Numerical simulations show that the proposed PF-based algorithm achieves the same SASR as the state-of-art algorithm with moderate complexity, while the proposed ZF-based scheme strikes a good balance between performance and complexity.

In this paper, we investigate the secure beamforming design in multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) en-abled Internet of Things (IoT) networks, where a controller transmits confidential messages to multiple actuators and a cooperative controller acts as a jammer to prevent a potential eavesdropper from wiretapping the information. Our goal is to maximize achievable secrecy sum rate subject to the successful successive interference cancellation constraint and transmit power constraints of the controller and jammer. When channel state information (CSI) is perfect, we design the secure beamforming by developing an iterative optimization algorithm based on minimum mean square error (MMSE) method. When perfect CSI is not available, we model the channel errors as deterministically-bounded, and propose a robust secure beamforming design algorithm based on weighted MMSE and cutting-set method. The effectiveness of the proposed algorithms is verified by the simulation results.

In this paper, secure beamforming design in cooperative cognitive radio networks for Internet of Things (IoT) is investigated, where we maximize the secrecy sum rate for the IoT devices (IoDs) to protect their communication and employ the nonorthogonal multiple access scheme at the cognitive access point (AP) to serve the cognitive users. The formulated secure beamforming optimization problem is a non-convex problem and its global optimal solution is very hard to find. To overcome this difficulty, we propose an iterative algorithm based on semidefinite programming and monotonic optimization method to obtain the global optimal solution, which has high computational complexity and is suitable for the case that the cognitive AP has a powerful computing ability. For some application scenarios in which the cognitive AP has a lower computing ability, we propose suboptimal solutions based on zero-forcing scheme, which have much lower computational complexity. Furthermore, we derive the limit value of the secrecy sum rate for the IoDs when the transmit power of the cognitive AP goes to infinity, which can serve as an upper bound for our proposed solutions. Numerical results are also provided to verify the effectiveness of our proposed solutions.

By the integration of cooperative cognitive radio (CR) and nonorthogonal multiple access (NOMA), cooperative CR NOMA networks can improve the spectrum efficiency of wireless networks significantly. Due to the openness and exposure of wireless signals, secure communication is an important issue for cooperative CR NOMA networks. In this paper, we investigate the physical layer security design for cooperative CR NOMA networks. Our objective is to achieve maximum secrecy rate of the secondary user by designing optimal beamformers and artificial noise covariance matrix at the multiantenna secondary transmitter under the quality-of-service at the primary user and the transmit power constraint at the secondary transmitter. We consider the practical case that the channel state information (CSI) of the eavesdropper is imperfect, and we model the imperfect CSI by the worst-case model. We show that the robust secrecy rate maximization problem can be transformed to a series of semidefinite programmings based on S-procedure and rank-one relaxation. We also propose an effective method to recover the optimal rank-one solution. Simulations are provided to show the effectiveness of our proposed robust secure algorithm with comparison to the nonrobust secure design and traditional orthogonal multiple access schemes.

Non-orthogonal multiple access (NOMA) and cognitive radio (CR) are envisioned as promising solutions to achieve high spectral efficiency for future wireless networks. This work investigates the outage performance of an overlay cognitive NOMA system with imperfect successive interference cancellation (SIC). The outage probability of primary user and secondary user are derived in closed forms. To obtain further insights, asymptotic expressions of outage probability and system throughput are evaluated when the transmit signal-to-noise ratio approaches infinity. An optimal power allocation coefficient is provided to maximize the system throughput. Moreover, the overlay cognitive NOMA system is compared with the underlay CR system and the overlay cognitive orthogonal multiple access (OMA) system in terms of outage probability. Finally, the performance analysis is validated by simulations. The simulation results demonstrate that the outage performance and system throughput of overlay cognitive NOMA system are superior to those of overlay cognitive OMA system and underlay CR system when the imperfect SIC satisfies certain conditions.

In this paper, we investigate beamforming design for cooperative secure transmission in cognitive two-way relay networks, where the cognitive transmitter (CT) with multiple antennas helps to forward the signals of two primary transmitters (PTs) and tries to protect the PTs from wiretapping by a single-antenna eavesdropper. With the objective of maximizing the secrecy sum rate (SSR) for PTs, we jointly design the beamforming matrix for the PTs' signals, the beamforming vector for the cognitive receiver (CR)'s signal, and the artificial noise (AN)'s beamforming matrix, under the quality of service constraint at the CR and the transmit power constraint at the CT. We propose the monotonic optimization-based algorithm to obtain the global optimal solution to the SSR maximization problem, which is a double-layer iterative algorithm and has very high complexity. To balance the complexity and the performance, we also propose a sequential parametric convex approximation-based single-layer iterative algorithm and a zero-forcing-based closed-form algorithm, the latter of which has the lowest complexity. Furthermore, we derive the asymptotic achievable secrecy sum rate when the CT transmit power goes to infinity. We present the numerical results to verify the effectiveness of our proposed algorithms.

In this article, we study the secure beamforming design for a two-way cognitive radio (CR) Internet of Things (IoT) network aided with the simultaneous wireless information and power transfer (SWIPT). Located at the center of secondary network, the IoT controller helps to provide relay assistance and cooperative physical layer security (PLS) for two primary users (PUs) against an eavesdropper, while transmitting information and power to the other IoT devices (IoDs) with primary spectrum. To enhance the information security, we aim to maximize the secrecy sum rate (SSR) for PUs by jointly designing the beamforming matrix and vectors at the central controller. To efficiently solve the nonconvex problem, we first propose the branch-reduce-and-bound (BRB)-based algorithm to obtain an upper bound for the SSR and offer a feasible solution by Gaussian randomization, which demands two-level iteration and thus has high complexity. To strike a balance between the complexity and the performance, we then propose iterative algorithm based on constrained-convex-concave programming (CCCP) and a zero forcing (ZF)-based noniterative algorithm, the latter of which with lowest complexity is suitable for the central controller with limited-power supply. The simulation results are provided to demonstrate the effectiveness of our proposed optimization algorithms in comparison to the traditional schemes.

Considering that a legitimate full-duplex monitor aims to intercept the suspicious wireless transmission, we study the robust jamming design problem where imperfect channel state information is assumed. We model channel estimation errors as deterministically bounded and define the successful monitoring event as that the signal-to-interference-plus-noise ratio (SINR) at the legitimate monitor is higher than the SINR at the suspicious receiver. To increase the worst-case probability of successful monitoring, we propose a robust proactive monitoring via jamming scheme. From simulation results, our proposed robust scheme is superior to the nonrobust one.

Physical-layer secure is an important issue for untrusted relay systems. In this paper, we investigate secure precoding design for non-regenerative multiple-input multiple-output untrusted two-way relay systems, where artificial noise (AN) is applied at the source and the relay is untrusted. We aim to maximize the secrecy sum rate by jointly designing the source's signal and AN precoders and the relay's precoder, under the sources and relay transmit power constraints. We consider two scenarios that perfect and imperfect channel state information of all the channels is available. For the former, we convert the secure precoding problem to a difference of convex programming and develop a constrained concave convex procedure based iterative algorithm which can obtain a local optimal solution. We also provide asymptotic analysis on the maximum secrecy sum rate when the relay transmit power approaches infinity, and propose alternating optimization method to compute asymptotic optimal precoders. For the latter, we model the channel uncertainties by using worst-case criterion and propose the weighted minimum mean square error based method to solve the robust secure precoding problem. Numerical simulations are presented to show the effectiveness of our proposed schemes.

Cooperative non-orthogonal multiple access (NOMA) systems inherit advantages of the NOMA protocol and the cooperative relay. In this paper, we propose cooperative NOMA systems in multiple-input-multiple-output channels. The whole transmission is divided into two phases. In the first phase, the base station broadcasts signals using the NOMA protocol to a central user and a cell-edge user. In the second phase, the central user helps the base station cooperatively relay signals intended for the cell-edge user. Our objective is to maximize achievable rate from the base station to the cell-edge user under transmit power constraints and achievable rate constraint from the base station to the central user. The difficulty of this problem is the joint beamforming of the base station and the central user in the second phase. We propose a constrained convex-concave procedure (CCCP)-based algorithm. To reduce computational complexity, we also propose a closed-form search-based suboptimal algorithm. Simulation results demonstrate that our proposed cooperative NOMA system with CCCP-based algorithm outperforms the conventional NOMA scheme. When achievable rate constraint to the central user is low, our proposed cooperative NOMA system with the closed-form search-based suboptimal algorithm outperforms the NOMA scheme.

In this letter, we consider a cellular downlink multiple-input-multiple-output nonorthogonal multiple access (NOMA) secure transmission network, which consists of a base station, a central user, and a cell-edge user. The base station and two users are all equipped with multiple antennas. The central user is an entrusted user and the cell-edge user is a potential eavesdropper. We focus on secure beamforming optimization problem, which maximizes achievable secrecy rate of the central user subject to transmit power constraint at the base station and transmission rate requirement at the cell-edge user. The optimization problem is nonconvex. We employ majorization-minimization method to iteratively optimize a sequence of valid surrogate functions for the nonconvex optimization problem. Furthermore, in each iteration, we derive the semi-closed form solution to optimize the valid surrogate functions. Simulation results demonstrate that our proposed NOMA scheme outperforms the zero-forcing-based NOMA scheme and conventional orthogonal multiple access scheme.

Nonorthogonal multiple access (NOMA) is expected to be a promising technique for future wireless networks. In this letter, we investigate the secrecy sum rate optimization problem for a downlink multiple-input-multiple-output NOMA system that consists of a base station, multiple legitimate users, and an eavesdropper. Our objective is to maximize achievable secrecy sum rate subject to successful successive interference cancellation constraints and transmit power constraint. The formulated optimization problem is nonconvex. Motivated by the relationship between mutual information rate and minimum mean square error, we propose to transform the secrecy sum rate optimization problem into a biconvex problem. The biconvex problem is solved by alternating optimization method where in each iteration, we solve a second-order cone programming. Simulation results demonstrate that our proposed NOMA scheme outperforms conventional orthogonal multiple access scheme.

Non-orthogonal multiple access (NOMA) is a promising technology in future communication systems. In this paper, we propose a buffer-aided NOMA relaying system, which consists of a source, a relay, and two destinations. In the relaying system, the relay helps the source transmit packets to two destinations simultaneously using NOMA scheme. We theoretically derive outage probabilities of source-to-relay link and relay-to-destinations links considering two scenarios that the relay does and does not know the channel state information (CSI) from itself to two destinations. When the relay knows CSI, the obtained outage probability of relay-to-destinations links involves integration operation. Thus, we derive an upper bound and two lower bounds. Simulation results demonstrate that two lower bounds approach exact outage probability at low and high signal-to-noise ratios, respectively. We also propose a relay decision scheme for the buffer-aided NOMA relaying system. Based on the obtained system outage probability, we theoretically derive the diversity order. It is found that no matter whether the relay knows CSI or not, the diversity order of 2 can be achieved when the buffer size is larger than or equal to 3.

Transceiver with Tomlinson-Harashima (TH) precoding outperforms the linear minimum mean-square-error (MSE) architecture in terms of minimum achievable MSE. In this paper, we investigate transceiver design optimization problem for nonregenerative multiple-input multiple-output cognitive relay networks (CRNs) with TH precoding. In the CRN, a secondary user (SU) source, an SU relay and an SU destination employ a TH precoder, a relay precoder, and a linear equalizer, respectively. For scenario in which SUs know perfect channel state information (CSI) from SUs to primary users, we propose an alternating optimization (AO)-based suboptimal algorithm. Given TH precoder and relay precoder, we derive a closed-form optimal solution of linear equalizer. Given relay precoder, TH precoder can be found by convex optimization. Given TH precoder, we transform nonconvex relay precoder design problem into a difference of convex programming and propose a constrained concave convex procedure-based iterative algorithm to find its local optimum. For scenario in which SUs know imperfect CSI, the channel uncertainties are modeled by worst case model. We derive equivalent worst case interference power constraints and extend the proposed AO-based suboptimal algorithm to cope with the worst case interference power constraints. Simulation results demonstrate that the proposed transceiver design with TH precoding outperforms linear transceiver designs.

In this paper, we investigate information and energy cooperation in cognitive non-regenerative two-way relay networks, where the multi-antenna secondary user (SU) transmitter harvests energy from primary users' (PUs) signals by the power splitting (PS) scheme to forward PUs' signals and transmit its own signals. Our objective is to design beamforming matrix and vector at the SU transmitter and PS factor to maximize achievable rate at SUs while maintaining achievable rate requirements at PUs subject to transmit power constraint at SU transmitter. We consider two scenarios that the SU transmitter knows perfect and imperfect channel state information of all links. For the former, we propose an optimal solution based on Charnes-Cooper transformation and 1-D search. We also propose a low-complexity suboptimal solution based on the zero-forcing (ZF) scheme and the algebraic norm-maximizing scheme. For the latter, channel uncertainties are modeled by a worst-case model. We propose to employ a ZF scheme and transform the problem into a semidefinite programming with the help of S-procedure and rank-one relaxation. The rank-one feasible solution is proposed to be recovered by the Gaussian randomization method. Simulation results demonstrate that our proposed designs outperform the beamforming scheme without energy cooperation.

Cognitive radio is a potential technique to solve the spectrum shortage problem in wireless communications. Integrating wireless relaying into the cognitive radio networks can further improve the spectrum efficiency. In this study, a multiple‐input multiple‐output cognitive relay network is considered, where the primary network (PN) consists of one transmitter‐receiver pair and the secondary network consists of multiple active source‐destination pairs and non‐regenerative relays. All nodes in the networks are deployed with multiple antennas. How to avoid interference to the PN is an important task in the secondary network design. The precoders of the secondary sources and the receivers of the secondary destinations are designed in a well‐known zero‐forcing way. The secondary relays forward signals for the secondary source‐destination pairs by beamforming. With interference cancellation constraints and individual transmit power constraints, a relay beamforming scheme is proposed to maximise the total signal‐to‐interference‐plus‐noise ratio (SINR) at the secondary destinations. Then a maximising minimum SINR relay beamforming scheme is further proposed to provide max–min fairness among the secondary source‐destination pairs. The beamforming problems are formulated into the quadratically constrained quadratic fractional programming problems, and are solved by the semi‐definite relaxation technique. The performances of these beamforming schemes have been verified by computer simulations.

For wireless networks which aim at high speed communication and computation, we propose a parallel analog function computation scheme via wireless multiple-access multiple-input-multiple-output channels. We consider that each sensor node only has imperfect channel state information from itself to the fusion center. Modeling the channel uncertainties by the worst-case model, the robust transceiver design problem for parallel analog function computation is formulated as a non-convex optimization problem which minimizes the worst-case mean-square-error subject to individual transmit power constraints. We then propose an alternating optimization algorithm to solve the problem. Simulation results demonstrate that the proposed robust scheme outperforms the non-robust one.

Considering simultaneous wireless information and power transfer (SWIPT), we investigate cooperative-jamming (CJ) aided robust secure transmission design in multiple-input-single-output channels, where a cooperative jammer introduces jamming interferences and assists a source to supply wireless power for both an energy receiver and a legitimate destination. The destination employs a power splitting (PS) scheme to split the received signals for both information decoding and energy harvesting (EH). Compared with conventional transmission without SWIPT, the transmission with SWIPT should satisfy additional worst-case EH constraints. Furthermore, the PS scheme introduces an additional multiplicative optimization variable, i.e., the PS factor. Our objective is to maximize worst-case secrecy rate under transmit power constraints and worst-case EH constraints. We propose to decouple the problem into three optimization problems and employ alternating optimization algorithm to obtain the locally optimal solution. For the optimization of transmit covariance matrices and PS factor, we propose to employ the S-procedure and its extension to reformulate it as a convex semidefinite programming. It is shown through the simulation results that our proposed CJ aided robust secure transmission scheme outperforms the robust direct transmission scheme without CJ and the CJ aided non-robust scheme.

This paper studies optimal precoder design for non-regenerative multiple-input multiple-output MIMO cognitive relay systems, where the secondary user SU and relay station RS share the same spectrum with the primary user PU. We aim to maximize the system capacity subject to the transmit power constraints at the SU transmitter SU-Tx and RS, and the interference power constraint at the PU. We jointly optimize precoders for the SU-Tx and RS with perfect and imperfect channel state information CSI between the SU-Tx/RS and PU, where our design approach is based on the alternate optimization method. With perfect CSI, we derive the optimal structures of the RS and SU-Tx precoding matrices and develop the gradient projection algorithm to find numerical solution of the RS precoder. Under imperfect CSI, we derive equivalent conditions for the interference power constraints and convert the robust SU-Tx precoder optimization into the form of semi-definite programming. For the robust RS precoder optimization, we relax the interference power constraint related with the RS precoder to be convex by using an upper bound and apply the gradient projection algorithm to deal with it. Simulation results demonstrate the effectiveness of the proposed schemes. Copyright © 2013 John Wiley & Sons, Ltd.

For amplify-and-forward multi-antenna relay networks with energy harvesting (EH) constraint, we study the optimal relay beamforming problem which maximizes the achievable rate from source to information-decoding receiver subject to the transmit power constraint at relay and the EH constraint at EH receiver. Because of the EH constraint, the beamforming problem is not convex. We propose the optimal beamforming scheme by converting the beamforming problem into a convex semidefinite programming with the rank-one relaxation and Charnes-Cooper transformation. We also propose a suboptimal closed-form beamforming scheme. It is shown from simulations that when the maximum allowable relay transmit power to noise power ratio is high, the performance of proposed suboptimal scheme approaches that of the optimal scheme.

In cognitive relay networks, the interferences from secondary users (SUs) and relays to primary users are constrained to be lower than a threshold. The interference constraints are difficult to satisfy when the channel state information (CSI) is imperfect. In this paper, we propose a robust beamforming scheme for the multi-antenna non-regenerative cognitive relay network where the multi-antenna relay with imperfect CSIs helps the communication of single-antenna SU. Our objective is to design a robust beamforming scheme which maximizes the system capacity subject to transmit power constraint and interference constraints. The bounded channel uncertainties are modeled using the worst-case model. The robust beamforming problem, neglecting the correlation of channel uncertainties, is reformulated as a convex semidefinite programming (SDP) by rank-one relaxation. This convex SDP is related with the worst-case relay transmit power minimization problem, which is further reformulated as a convex SDP, whose rank-one solution is proved to exist. Thus, we propose the suboptimal solution to the robust beamforming problem which is found effectively by solving two convex SDPs. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

Simultaneous wireless information and power transfer (SWIPT) is able to prolong the lifetime of energy constrained wireless networks. In this paper, we consider the relay beamforming design problem for SWIPT scheme in a non-regenerative two-way multi-antenna relay network. Our objective is to maximize the sum rate of two-way relay network under the transmit power constraint at relay and the energy harvesting (EH) constraint at EH receiver. For the non-convex EH-constrained relay beamforming optimization problem, we propose an iterative algorithm to find the global optimal solution based on semidefinite programming and rank-one decomposition theorem. To reduce computational complexity of global optimal solution, we transform the EH-constrained optimization problem to a difference of convex programming and propose a constrained concave convex procedure based iterative algorithm to find a local optimum. To further reduce the complexity, we propose a suboptimal solution based on the generalized eigenvectors method. When the case of multi-antenna sources is considered, we propose the alternating optimization based iterative algorithms. It is shown from simulations that considering the EH constraint, our proposed schemes outperform conventional relay beamforming schemes in the literature.

The antenna subarray formation (ASF) is a promising technique for multiple-input multiple-output (MIMO) receiver. For MIMO cognitive radio systems, we propose a joint beamforming and ASF scheme in this letter which maximizes the cognitive achievable capacity subject to the peak transmit power constraint at the secondary transmitter, peak interference power constraint at the primary receiver, and the limited number of nonzero elements in the ASF matrix. To solve the joint optimization problem, we propose a relax-and-recover scheme. Simulation results have shown that the proposed scheme outperforms the conventional antenna selection scheme.