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Jiangzhou Wang

Publications and source records attributed to Jiangzhou Wang.

At least 253 records · Page 14Linked to original sources

Bandit Inspired Beam Searching Scheme for mmWave High-Speed Train Communications

High-speed trains (HSTs) are being widely deployed around the world. To meet the high-rate data transmission requirements on HSTs, millimeter wave (mmWave) HST communications have drawn increasingly attentions. To realize sufficient link margin, mmWave HST systems employ directional beamforming with large antenna arrays, which results in that the channel estimation is rather time-consuming. In HST scenarios, channel conditions vary quickly and channel estimations should be performed frequently. Since the period of each transmission time interval (TTI) is too short to allocate enough time for accurate channel estimation, the key challenge is how to design an efficient beam searching scheme to leave more time for data transmission. Motivated by the successful applications of machine learning, this paper tries to exploit the similarities between current and historical wireless propagation environments. Using the knowledge of reinforcement learning, the beam searching problem of mmWave HST communications is formulated as a multi-armed bandit (MAB) problem and a bandit inspired beam searching scheme is proposed to reduce the number of measurements as many as possible. Unlike the popular deep learning methods, the proposed scheme does not need to collect and store a massive amount of training data in advance, which can save a huge amount of resources such as storage space, computing time, and power energy. Moreover, the performance of the proposed scheme is analyzed in terms of regret. The regret analysis indicates that the proposed schemes can approach the theoretical limit very quickly, which is further verified by simulation results.

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High-performance Power Allocation Strategies for Secure Spatial Modulation

Optimal power allocation (PA) strategies can make a significant rate improvement in secure spatial modulation (SM). Due to the lack of secrecy rate (SR) closed-form expression in secure SM networks, it is hard to optimize the PA factor. In this paper, two PA strategies are proposed: gradient descent, and maximum product of signal-to-interference-plus-noise ratio (SINR) and artificial-noise-to-signal-plus-noise ratio (ANSNR)(Max-P-SINR-ANSNR). The former is an iterative method and the latter is a closed-form solution. Compared to the former, the latter is of low-complexity. Simulation results show that the proposed two PA methods can approximately achieve the same SR performance as exhaustive search method and perform far better than three fixed PA ones. With extremely low complexity, the SR performance of the proposed Max-P-SINR-ANSNR performs slightly better and worse than that of the proposed GD in the low to medium, and high signal-to-noise ratio regions, respectively.

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Covert Communication Achieved by A Greedy Relay in Wireless Networks

Covert communication aims to hide the very existence of wireless transmissions in order to guarantee a strong security in wireless networks. In this work, we examine the possibility and achievable performance of covert communication in one-way relay networks. Specifically, the relay is greedy and opportunistically transmits its own information to the destination covertly on top of forwarding the source's message, while the source tries to detect this covert transmission to discover the illegitimate usage of the resource (e.g., power, spectrum) allocated only for the purpose of forwarding source's information. We propose two strategies for the relay to transmit its covert information, namely fixed-rate and fixed-power transmission schemes, for which the source's detection limits are analysed in terms of the false alarm and miss detection rates and the achievable effective covert rates from the relay to destination are derived. Our examination determines the conditions under which the fixed-rate transmission scheme outperforms the fixed-power transmission scheme, and vice versa, which enables the relay to achieve the maximum effective covert rate. Our analysis indicates that the relay has to forward the source's message to shield its covert transmission and the effective covert rate increases with its forwarding ability (e.g., its maximum transmit power).

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Robust Directional Modulation Design for Secrecy Rate Maximization in Multi-User Networks

In this paper, based on directional modulation (DM), robust beamforming matrix design for sum secrecy rate maximization is investigated in multi-user systems. The base station (BS) is assumed to have the imperfect knowledge of the direction angle toward each eavesdropper, with the estimation error following the Von Mises distribution. To this end, a Von Mises distribution-Sum Secrecy Rate Maximization (VMD-SSRM) method is proposed to maximize the sum secrecy rate by employing semi-definite relaxation and first-order approximation based on Taylor expansion to solve the optimization problem. Then in order to optimize the sum secrecy rate in the case of the worst estimation error of direction angle toward each eavesdropper, we propose a maximum angle estimation error-SSRM (MAEE-SSRM) method. The optimization problem is constructed based on the upper and lower bounds of the estimated eavesdropping channel related coefficient and then solved by the change of the variable method. Simulation results show that our two proposed methods have better sum secrecy rate than zero-forcing (ZF) method and signal-to-leakage-and-noise ratio (SLNR) method. Furthermore, the sum secrecy rate performance of our VMD-SSRM method is better than that of our MAEE-SSRM method.

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Two Practical Random-Subcarrier-Selection Methods for Secure Precise Wireless Transmission

In secure precise directional modulation (DM) networks, two practical random-subcarrier-selection (RSS) methods are proposed to transmit confidential message to the desired user per orthogonal frequency division multiplexing (OFDM) symbol with only single receive power peak formed by constructing random subcarrier set and performing a randomization procedure. This scheme completely addresses the crucial problem facing secure precise wireless transmission (SPWT), how to achieve the SPWT per OFDM symbol while the traditional SPWT holds only in statistically average sense. Several necessary conditions for SPWT per OFDM is derived and proposed: randomly distributed, sparse, and distinct subcarrier distance between two pair of adjacent antennas. Two random subcarrier sets (RSSs), quadratic subcarrier set (QSS) and prime subcarrier set (PSS), are constructed, where the former means the subcarrier index associated with any antennas is the square of antenna index, and the latter implies that the subcarrier indices over all antennas are prime numbers. Subsequently, following those conditions for SPWT per OFDM, a random factor is defined, and a randomization procedure (RP) is proposed. Its detailed process includes the following steps: integer mod, ordering, blocking, and block interleaving where BI is repeated until the random factor is greater than the predefined threshold. This yields a single main receive energy peak (SMREP) at the desired position with other positions, outside the SMREP, harvesting only weak receive energy seriously corrupted by AN.

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Power Allocation Strategies for Secure Spatial Modulation

In secure spatial modulation (SM) networks, power allocation (PA) strategies are investigated in this paper under the total power constraint. Considering that there is no closed-form expression for secrecy rate (SR), an approximate closed-form expression of SR is presented, which is used as an efficient metric to optimize PA factor and can greatly reduce the computation complexity. Based on this expression, a convex optimization (CO) method of maximizing SR (Max-SR) is proposed accordingly. Furthermore, a method of maximizing the product of signal-to-leakage and noise ratio (SLNR) and artificial noise-to-leakage-and noise ratio (ANLNR) (Max-P-SAN) is proposed to provide an analytic solution to PA with extremely low-complexity. Simulation results demonstrate that the SR performance of the proposed CO method is close to that of the optimal PA strategy of Max-SR with exhaustive search and better than that of Max-P-SAN in the high signal-to-noise ratio (SNR) region. However, in the low and medium SNR regions, the SR performance of the proposed Max-P-SAN slightly exceeds that of the proposed CO.

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A Robust Secure Hybrid Analog and Digital Receive Beamforming Scheme for Efficient Interference Reduction

Medium-scale or large-scale receive antenna array with digital beamforming can be employed at receiver to make a significant interference reduction, but leads to expensive cost and high complexity of the RF-chain circuit. To deal with this issue, a classic analog-and-digital beamforming (ADB) structure was proposed in the literature for greatly reducing the number of RF-chains. Based on the ADB structure, we in this paper propose a robust hybrid ADB scheme to resist directions of arrival (DOAs) estimation errors. The key idea of our scheme is to employ null space projection (NSP) in analog beamforming domain and diagonal loading (DL) method in digital beamforming domain. Simulation results show that the proposed scheme performs more robustly, and moreover, has a significant improvement on the receive signal to interference plus noise ratio (SINR) compared to NSP ADB scheme and DL method.

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Directional Modulation: A Secure Solution to 5G and Beyond Mobile Networks

Directional modulation (DM), as an efficient secure transmission way, offers security through its directive property and is suitable for line-of-propagation (LoP) channels such as millimeter wave (mmWave) massive multiple-input multiple-output (MIMO), satellite communication, unmanned aerial vehicle (UAV), and smart transportation. If the direction angle of the desired received is known, the desired channel gain vector is obtainable. Thus, in advance, the DM transmitter knows the values of directional angles of desired user and eavesdropper, or their direction of arrival (DOAs) because the beamforming vector of confidential messages and artificial noise (AN) projection matrix is mainly determined by directional angles of desired user and eavesdropper. For a DM transceiver, working as a receiver, the first step is to measure the DOAs of desired user and eavesdropper. Then, in the second step, using the measured DOAs, the beamforming vector of confidential messages and AN projection matrix is designed. In this paper, we describe the DOA measurement methods, power allocation, and beamforming in DM networks. A machine learning-based DOA measurement method is proposed to make a substantial SR performance gain compared to single-snapshot measurement without machine learning for a given null-space projection beamforming scheme. However, for a conventional DM network, there still exists a serious secure issue: the eavesdropper moves inside the main beam of the desired user and may intercept the confidential messages intended to the desired users because the beamforming vector of confidential messages and AN projection matrix are only angle-dependence. To address this problem, we present a new concept of secure and precise transmission, where the transmit waveform has two-dimensional even three-dimensional dependence by using DM, random frequency selection, and phase alignment at DM transmitter.

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Power Allocation Strategy of Maximizing Secrecy Rate for Secure Directional Modulation Networks

In this paper, given the beamforming vector of confidential messages and artificial noise (AN) projection matrix and total power constraint, a power allocation (PA) strategy of maximizing secrecy rate (Max-SR) is proposed for secure directional modulation (DM) networks. By the method of Lagrange multiplier, the analytic expression of the proposed PA strategy is derived. To confirm the benefit from the Max-SR-based PA strategy, we take the null-space projection (NSP) beamforming scheme as an example and derive its closed-form expression of optimal PA strategy. From simulation results, we find the following facts: in the medium and high signal-to-noise-ratio (SNR) regions, compared with three typical PA parameters such $β=0.1, 0.5$, and $0.9$, the optimal PA shows a substantial SR performance gain with maximum gain percent up to more than $60\%$. Additionally, as the PA factor increases from 0 to 1, the achievable SR increases accordingly in the low SNR region whereas it first increases and then decreases in the medium and high SNR regions, where the SR can be approximately viewed as a convex function of the PA factor. Finally, as the number of antennas increases, the optimal PA factor becomes large and tends to one in the medium and high SNR region. In other words, the contribution of AN to SR can be trivial in such a situation.

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Tradeoff Caching Strategy of Outage Probability and Fronthaul Usage in Cloud-RAN

In this paper, optimal content caching strategy is proposed to jointly minimize the cell average outage probability and fronthaul usage in cloud radio access network (Cloud-RAN). At first, an accurate closed form expression of the outage probability conditioned on the user's location is presented, and the cell average outage probability is obtained through the composite Simpson's integration. The caching strategy for jointly optimizing the cell average outage probability and fronthaul usage is then formulated as a weighted sum minimization problem, which is a nonlinear 0-1 integer NP-hard problem. In order to deal with the NP-hard problem, two heuristic algorithms are proposed in this paper. Firstly, a genetic algorithm (GA) based approach is proposed. Numerical results show that the performance of the proposed GA-based approach with significantly reduced computational complexity is close to the optimal performance achieved by exhaustive search based caching strategy. Secondly, in order to further reduce the computational complexity, a mode selection approach is proposed. Numerical results show that this approach can achieve near-optimal performance over a wide range of the weighting factors through a single computation.

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GPI-based Secrecy Rate Maximization Beamforming Scheme for Wireless Transmission with AN-aided Directional Modulation

In a directional modulation network, a general power iterative (GPI) based beamforming scheme is proposed to maximize the secrecy rate (SR), where there are two optimization variables required to be optimized. The first one is the useful precoding vector of transmitting confidential messages to the desired user while the second one is the artificial noise (AN) projection matrix of forcing more AN to eavesdroppers. In such a secure network, the paramount problem is how to design or optimize the two optimization variables by different criteria. To maximize the SR (Max-SR), an alternatively iterative structure (AIS) is established between the AN projection matrix and the precoding vector for confidential messages. To choose a good initial value of iteration process of GPI, the proposed Max-SR method can readily double its convergence speed compared to the random choice of initial value. With only four iterations, it may rapidly converge to its rate ceil. From simulation results, it follows that the SR performance of the proposed AIS of GPI-based Max-SR is much better than those of conventional leakage-based and null-space projection methods in the medium and large signal-to-noise ratio (SNR) regions, and its achievable SR performance gain gradually increases as SNR increases.

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Two High-performance Schemes of Transmit Antenna Selection for Secure Spatial Modulation

In this paper, a secure spatial modulation (SM) system with artificial noise (AN)-aided is investigated. To achieve higher secrecy rate (SR) in such a system, two high-performance schemes of transmit antenna selection (TAS), leakage-based and maximum secrecy rate (Max-SR), are proposed and a generalized Euclidean distance-optimized antenna selection (EDAS) method is designed. From simulation results and analysis, the four TAS schemes have an decreasing order: Max-SR, leakage-based, generalized EDAS, and random (conventional), in terms of SR performance. However, the proposed Max-SR method requires the exhaustive search to achieve the optimal SR performance, thus its complexity is extremely high as the number of antennas tends to medium and large scale. The proposed leakage-based method approaches the Max-SR method with much lower complexity. Thus, it achieves a good balance between complexity and SR performance. In terms of bit error rate (BER), their performances are in an increasing order: random, leakage-based, Max-SR, and generalized EDAS.

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Artificial-noise-aided Secure Multicast Precoding for Directional Modulation Systems

In multi-cast scenario, all desired users are divided into $K$ groups. Each group receives its own individual confidential message stream. Eavesdropper group aims to intercept $K$ confidential-message streams. To achieve a secure transmission, two secure schemes are proposed: maximum group receive power plus null-space (NS) projection (Max-GRP plus NSP) and leakage. The former obtains its precoding vector per group by maximizing its own group receive power subject to the orthogonal constraint, and its AN projection matrix consist of all bases of NS of all desired steering vectors from all groups. The latter attains its desired precoding vector per group by driving the current confidential message power to its group steering space and reducing its power leakage to eavesdropper group and other $K-1$ desired ones by maximizing signal to leakage and noise ratio (Max-SLNR). And its AN projection matrix is designed by forcing AN power into the eavesdropper steering space by viewing AN as a useful signal for eavesdropper group and maximizing AN to leakage-and-noise ratio (Max-ANLNR). Simulation results show that the proposed two methods are better than conventional method in terms of both bit-error-rate (BER) and secrecy sum-rate per group. Also, the leakage scheme performs better than Max-GRP-NSP , especially in the presence of direction measurement errors. However, the latter requires no channel statistical parameters and thus is simpler compared to the former.

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A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing

Conventionally, the resource allocation is formulated as an optimization problem and solved online with instantaneous scenario information. Since most resource allocation problems are not convex, the optimal solutions are very difficult to be obtained in real time. Lagrangian relaxation or greedy methods are then often employed, which results in performance loss. Therefore, the conventional methods of resource allocation are facing great challenges to meet the ever-increasing QoS requirements of users with scarce radio resource. Assisted by cloud computing, a huge amount of historical data on scenarios can be collected for extracting similarities among scenarios using machine learning. Moreover, optimal or near-optimal solutions of historical scenarios can be searched offline and stored in advance. When the measured data of current scenario arrives, the current scenario is compared with historical scenarios to find the most similar one. Then, the optimal or near-optimal solution in the most similar historical scenario is adopted to allocate the radio resources for the current scenario. To facilitate the application of new design philosophy, a machine learning framework is proposed for resource allocation assisted by cloud computing. An example of beam allocation in multi-user massive multiple-input-multiple-output (MIMO) systems shows that the proposed machine-learning based resource allocation outperforms conventional methods.

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User-centric C-RAN Architecture for Ultra-dense 5G Networks: Challenges and Methodologies

Ultra-dense networks (UDN) constitute one of the most promising techniques of supporting the 5G mobile system. By deploying more small cells in a fixed area, the average distance between users and access points can be significantly reduced, hence a dense spatial frequency reuse can be exploited. However, severe interference is the major obstacle in UDN. Most of the contributions deal with the interference by relying on cooperative game theory. This paper advocates the application of dense user-centric C-RAN philosophy to UDN, thanks to the recent development of cloud computing techniques. Under dense C-RAN, centralized signal processing can be invoked for supporting CoMP transmission. We summarize the main challenges in dense user-centric C-RANs. One of the most challenging issues is the requirement of the global CSI for the sake of cooperative transmission. We investigate this requirement by only relying on partial CSI, namely, on inter-cluster large-scale CSI. Furthermore, the estimation of the intra-cluster CSI is considered, including the pilot allocation and robust transmission. Finally, we highlight several promising research directions to make the dense user-centric C-RAN become a reality, with special emphasis on the application of the `big data' techniques.

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Pilot Optimization and Power Allocation for OFDM-based Full-duplex Relay Networks with IQ-imbalances

In OFDM relay networks with IQ imbalances and full-duplex relay station (RS), how to optimize pilot pattern and power allocation using the criterion of minimizing the sum of mean square errors (Sum-MSE) for the frequency-domain least-squares channel estimator has a heavy impact on self-interference cancellation. Firstly, the design problem of pilot pattern is casted as a convex optimization. From the KKT conditions, the optimal analytical expression is derived given the fixed source power and RS power. Subsequently, an optimal power allocation (OPA) strategy is proposed and presented to further alleviate the effect of Sum-MSE under the total transmit power sum constraint of source node and RS. Simulation results show that the proposed OPA performs better than equal power allocation (EPA) in terms of Sum-MSE, and the Sum-MSE performance gain grows with deviating $ρ$ from the value of $ρ^o$ minimizing the Sum-MSE, where $ρ$ is defined as the average ratio of the residual SI channel at RS to the intended channel from source to RS. For example, the OPA achieves about 5dB SNR gain over EPA by shrinking or stretching $ρ$ with a factor $4$. More importantly, as $ρ$ decreases or increases more, the performance gain becomes more significant.

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Secure Precise Wireless Transmission with Random-Subcarrier-Selection-based Directional Modulation Transmit Antenna Array

In this paper, a practical wireless transmission scheme is proposed to transmit confidential messages to the desired user securely and precisely by the joint use of multiple techniques including artificial noise (AN) projection, phase alignment (PA)/beamforming, and random subcarrier selection (RSCS) based on OFDM, and directional modulation (DM), namely RSCS-OFDM-DM. This RSCS-OFDM-DM scheme provides an extremely low-complexity structures for the transmitter and desired receiver and makes the secure and precise wireless transmission realizable in practice. For illegal eavesdroppers, the receive power of confidential messages is so weak that their receivers cannot intercept these confidential messages successfully once it is corrupted by AN. In such a scheme, the design of phase alignment/beamforming vector and AN projection matrix depend intimately on the desired direction angle and distance. It is particularly noted that the use of RSCS leads to a significant outcome that the receive power of confidential messages mainly concentrates on the small neighboring region around the desired receiver and only small fraction of its power leaks out to the remaining large broad regions. This concept is called secure precise transmission. The probability density function of real-time receive signal-to-interference-and-noise ratio (SINR) is derived. Also, the average SINR and its tight upper bound are attained. The approximate closed-form expression for average secrecy rate is derived by analyzing the first-null positions of SINR and clarifying the wiretap region.

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Joint User Selection and Energy Minimization for Ultra-Dense Multi-channel C-RAN with Incomplete CSI

This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fronthaul capacity, heavy computational complexity, unavailability of full channel state information (CSI), etc. Specifically, we aim to jointly optimize the remote radio head (RRH) selection, user equipment (UE)-RRH associations and beam-vectors to minimize the total network power consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI subject to per-RRH power constraints, each UE's total rate requirement, and fronthaul link capacity constraints. This optimization problem is NP-hard. In addition, due to the incomplete CSI, the exact expression of UEs' rate expression is intractable. We first conservatively replace UEs' rate expression with its lower-bound. Then, based on the successive convex approximation (SCA) technique and the relationship between the data rate and the mean square error (MSE), we propose a single-layer iterative algorithm to solve the NPC minimization problem with convergence guarantee. In each iteration of the algorithm, the Lagrange dual decomposition method is used to derive the structure of the optimal beam-vectors, which facilitates the parallel computations at the Baseband unit (BBU) pool. Furthermore, a bisection UE selection algorithm is proposed to guarantee the feasibility of the problem. Simulation results show the benefits of the proposed algorithms and the fact that a limited amount of CSI is sufficient to achieve performance close to that obtained when perfect CSI is possessed.

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