Multiple-input Single-output Networks Research Articles

Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

In this paper, the application of Non-Orthogonal Multiple Access (NOMA) is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential eavesdropper. To support secure transmissions from legitimate users, two NOMA Secrecy Sum Rate Transmit BeamForming (NOMA-SSR-TBF) schemes are proposed to maximise the SSR of a Base Station (BS) with sufficient and insufficient transmit power. For BS with sufficient transmit power, an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate transmissions. In addition, for BS with insufficient transmit power, a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate transmissions. More specifically, iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial jamming. Experimental results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works, such as the maximized artificial jamming power scheme, the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

M-DA: A Multifeature Text Data-Augmentation Model for Improving Accuracy of Chinese Sentiment Analysis

A neural network based on a word or character embedding is a mainstream model framework in text sentiment analysis and has achieved good results. However, there is a lack of learning about POS-Tagging and Sequence-Tagging. In this research, we propose a multifeature text data-augmentation model (M-DA) with a multiple-input single-output network structure to overcome this problem of Chinese text sentiment analysis. First, this paper sequentially obtains various sequences of Chinese text, including word sequence, pos sequence, char sequence, char_pos sequence, and char_4tag sequence, we use char_pos and the char_4tag to construct a new sequence (4tag_pos) and then use 4tag_pos to mark the characters to obtain the reconstructed characters sequence (char_4tag_pos), so as to achieve the purpose of text enhancement. Then, the Word2Vec method is used to train the initial reconstruction of the character embedding. Finally, the BiLSTM network is used to capture the long-term dependence between the sequences, and the dropout technology and attention are used to improve the accuracy. In the course of the experiment, we also realized that it is better to use the original sequence and the sequence after text enhancement technology as the input of the BiLSTM network. Therefore, our proposed model also discusses the concatenate or dot method to fuse multiple sequences as the final embedding. Multigroup comparison experiments are conducted on the data set, and the results show that the proposed M-DA model is superior to the traditional deep learning technology in terms of accuracy, recall rate, f-measure, and accuracy, and the relative time cost is small.

Weighted Sum Rate Maximization in IRS-BackCom Enabled Downlink Multi-Cell MISO Network

This letter investigates intelligent reflecting surface backscatter communication (IRS-BackCom) assisted downlink multi-cell multiple-input single-output network for the first time. In such multi-cell network, each IRS instead of active transmit antennas serves as a cell base station (BS) to communicate with its single-antenna users through modulation and backscatter of ambient wireless signal from a power beacon (PB). To maximize weighted sum rate (WSR) of network subject to total power budget, coordinated multipoint transmission (CoMP) is implemented at the IRSs. By alternately optimizing the beamforming vectors at the PB and all the IRSs, the sub-optimal WSR is obtained. Simulations show the achievable WSR and verify the feasibility of the proposed IRS-BackCom enabled CoMP scheme.

Energy Efficient Robust Beamforming and Cooperative Jamming Design for IRS-Assisted MISO Networks

Energy-efficient design and secure communications are of crucial importance in wireless communication networks. However, the energy efficiency achieved by using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix under both the perfect channel state information (CSI) and the imperfect CSI. In order to tackle the challenging non-convex fractional problems, an algorithm based on semidefinite programming (SDP) relaxation is proposed for solving energy efficiency maximization problem under the perfect CSI case while an alternate optimization algorithm based on <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> -procedure is used for solving the problem under the imperfect CSI case. Simulation results demonstrate that the proposed design outperforms the benchmark schemes in term of energy efficiency. Moreover, the tradeoff between energy efficiency and the secrecy rate is found in the IRS-assisted MISO network. Furthermore, it is shown that IRS can help improve energy efficiency even with the uncertainty of the CSI.

A New Lower Bound Based Secure Beamforming in MISO Communication Networks

This letter considers a multiple-input single-output network that consists of a base station, multiple legitimate user equipments, and an eavesdropper. A new expression is developed to lower-bound the achievable rates of the legitimate user equipments. Applying this new lower bound, we optimize the beamforming design at the base station to maximize the system performance in terms of the minimum secrecy rate and the secure energy efficiency. We consider both scenarios with perfect and partial channel state information for the eavesdropper. Iterative algorithms with low computational complexity and fast convergence are proposed to solve these non-convex optimization problems. The validity and convergence are demonstrated by simulation results.

Robust Beamforming and Power Allocation in CR MISO Networks with SWIPT to Maximize the Minimum Achievable Rate

In this paper, a new method is presented to maximize the minimum achievable rate of the users in a cognitive radio multiple input single output network. The secondary system provides simultaneous wireless information and power transfer (SWIPT) for energy harvesting receivers. Considering random Gaussian vector for the estimation error of the channel state information (CSI), we impose some outage constraints to guarantee the quality of service of the network. Outage constraints on the received power and the information leakage at the energy harvesting users are imposed to assure SWIPT. We introduce new convex inequalities to replace the original non-convex constraints and write the objective function as the minimization of the maximum of some fractional functions. This new objective function and convex inequalities result in a new quasi-convex general fractional programming problem. The new quasi-convex optimization problem is written in such a way that the computational costs to solve this problem are as low as possible. We develop an algorithm to obtain the optimal beamforming weights and artificial noise covariance matrix. All stochastic constraints are rewritten for the special case that the covariance matrices of error in the estimation of CSI are a factor of identity matrix. This reformulation results in less computation costs to obtain optimal beamforming weights and AN covariance matrix. Simulation results confirm that, all stochastic constraints are satisfied with certain probability. In comparison with the previous robust related work, the proposed method achieves higher rates which confirms superiority of our method.

Outage probability of scheduled MRT scheme in cognitive MISO networks with imperfect CSI

In this study, the authors assess the performance of a cognitive multiple-input single-output (MISO) network, where the secondary base station schedules a secondary destination D k ∗ to communicate from K candidates, by employing scheduled maximal ratio transmission (MRT) scheme. A practical scenario is considered where imperfect channel state information (CSI) is taken into account. Specially, assuming Rayleigh fading channels, the exact expression for the cumulative distribution function of the received signal-to-noise ratio (SNR) at D k ∗ is derived, from which the outage probability (OP) at D k ∗ is obtained. An asymptotic expression for the OP at high SNR region is also presented, indicating the diversity gain that the scheduled MRT scheme can achieve. Furthermore, the interference OP at the primary receiver is discussed due to imperfect CSI in the link from base station to primary receiver link. Finally, extensive performance evaluation results accompanied with equivalent ones obtained by computer simulations are presented to corroborate the proposed analysis.

Achieving Full Secrecy Rate With Energy-Efficient Transmission Control

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

Secrecy Enhancement of Multiuser MISO Networks Using OSTBC and Artificial Noise

In this paper, we propose a novel physical layer strategy to improve the secrecy performance of multiuser multiple-input single-output networks. In this strategy, orthogonal space-time block code (OSTBC) is employed at an $A_{\text{A}}$ -antenna base station and artificial noise (AN) is employed at an $A_{\text{J}}$ -antenna cooperative relay to enhance the security level of the network. Moreover, two opportunistic scheduling schemes, namely selection combining (SC) and scan-and-wait combining (SWC), are leveraged to select one legitimate user for data transmission. To evaluate the secrecy performance of the proposed OSTBC-SC-AN and OSTBC-SWC-AN schemes, we derive new exact closed-form expressions for the secrecy outage probability and the effective secrecy throughput. Using numerical results, we show that the OSTBC-SWC-AN scheme outperforms the OSTBC-SC-AN scheme when the switching threshold is carefully chosen, while the OSTBC-SC-AN scheme is independent of the switching threshold. We also show that increasing $A_{\text{A}}$ at a low switching threshold or decreasing ${A_{\rm{A}}}$ at a high switching threshold achieves secrecy enhancement for the OSTBC-SWC-AN scheme, while increasing ${A_{\rm{A}}}$ brings better secrecy performance for the OSTBC-SC-AN scheme.

Cooperative Beamforming and User Selection for Improving the Security of Relay-Aided Systems

A relay network in which a source wishes to convey a confidential message to a legitimate destination with the assistance of trusted relays is considered. In particular, cooperative beamforming and user selection techniques are applied to protect the confidential message. The secrecy rate (SR) and secrecy outage probability (SOP) of the network are investigated first, and a tight upper bound for the SR and an exact formula for the SOP are derived. Next, asymptotic approximations for the SR and SOP in the high signal-to-noise ratio (SNR) regime are derived for two different schemes: 1) cooperative beamforming and 2) multiuser selection. Furthermore, a new concept of cooperative diversity gain, namely, adapted cooperative diversity gain (ACDG), which can be used to evaluate the security level of a cooperative relaying network, is investigated. It is shown that the ACDG of cooperative beamforming is equal to the conventional cooperative diversity gain of traditional multiple-input single-output networks, while the ACDG of the multiuser scenario is equal to that of traditional single-input multiple-output networks.

Beamforming Duality and Algorithms for Weighted Sum Rate Maximization in Cognitive Radio Networks

In this paper, we investigate the joint design of transmit beamforming and power control to maximize the weighted sum rate in the multiple-input single-output (MISO) cognitive radio network constrained by arbitrary power budgets and interference temperatures. The nonnegativity of the physical quantities, e.g., channel parameters, powers, and rates, is exploited to enable key tools in nonnegative matrix theory, such as the (linear and nonlinear) Perron-Frobenius theory, quasi-invertibility, and Friedland-Karlin inequalities, to tackle this nonconvex problem. Under certain (quasi-invertibility) sufficient conditions, we propose a tight convex relaxation technique that relaxes multiple constraints to bound the global optimal value in a systematic way. Then, a single-input multiple-output (SIMO)-MISO duality is established through a virtual dual SIMO network and Lagrange duality. This SIMO-MISO duality proved to have the zero duality gap that connects the optimality conditions of the primal MISO network and the virtual dual SIMO network. Moreover, by exploiting the SIMO-MISO duality, an algorithm is developed to optimally solve the sum rate maximization problem. Numerical examples demonstrate the computational efficiency of our algorithm, when the number of transmit antennas is large.