Dongyang Xu

Joint dynamic clustering and user scheduling for downlink cloud radio access network with limited feedback

In limited feedback-based Cloud-RAN (C-RAN) systems, the inter-cluster and intra-cluster interference together with the quantification error can seriously deteriorates the system spectral efficiency. We, in this paper, propose an efficient three-phase framework and corresponding algorithms for dealing with this problem. Firstly, a greedy scheduling algorithm based on the lower bound of the ergodic rate is performed for generating an elementary cluster in the first phase. And then the elementary cluster is divided into many small clusters according to the following proposed algorithms based on the short term instantaneous information in the second phase. In the end, based on the limited feedback two zero-forcing (ZF) precoding strategies are adopted for reducing the intra-cluster interference in the third phase. The provided Monte Carlo simulations show the effectiveness of our proposed algorithms in the respect of system spectral efficiency and average user rate.

Hybrid secure beamforming and vehicle selection using hierarchical agglomerative clustering for C-RAN-based vehicle-to-infrastructure communications in vehicular cyber-physical systems

The architecture of C-RAN-based soft-defined vehicular networks can support increasing demands of various applications in the future vehicular cyber-physical systems, but lacks strong and practical security–enhancing mechanisms in the physical layer. In this article, we propose a hybrid beamforming and vehicle-selection framework for vehicle-to-infrastructure communications to broadcast high-speed confidential messages. A one-dimensional roadway scenario having multiple lanes is considered, and the concept of secure region and interference-selection region is, respectively, developed for each roadside unit. We formulate an optimization problem of maximizing secrecy sumrate with the constraint of orthogonality-based vehicle selection. The original problem is then divided into two subproblems subtly. In terms of the first subproblem, we, by exploiting the orthogonality assumption, obtain a corresponding secure beamformer for each vehicle located in the secure region to reduce the interference and confidential information leakage. Based on secure beamformers, the second subproblem is changed to be a vehicle-selection problem, which is then solved by adopting hierarchical agglomerative clustering method and implemented by two proposed novel algorithms blending vehicle selection with beamformer generation. Simulation results verify the effectiveness of the proposed algorithms in the respect of secrecy sumrate compared with the conventional zero-forcing beamforming using semi-orthogonal user selection algorithms. Furthermore, simulations show how our proposed algorithms resist against eavesdropping from collusive vehicles located in the same secure region by flexibly altering the size of secure region and interference-selection according to the traffic density.

AF-Based CSI Feedback for User Selection in Multi-User MIMO Systems

In this paper, we propose a joint amplify-and-forward (AF)-based channel estimation and user selection (J-ACES) scheme for multi-user MIMO (MU-MIMO) systems. Firstly, without channel state information (CSI) quantization, the transmitter estimates the downlink CSI for each receiver by adopting AF-based CSI feedback mechanism, in which the receiver amplifies and feeds back its own received downlink pilot symbols to the transmitter. An analytic user normalized mean square error (UNMSE) characterization of the uplink channel estimation and a lower bound of UNMSE for the downlink channel estimation are respectively derived and verified by Monte Carlo simulations. We show how the performance of UNMSE is influenced by the uplink and downlink pilot signal to noise ratio (SNR). Secondly, we obtain a corresponding suboptimal beamformer and estimated Signal to Interference Plus Noise Ratio (SINR) for each receiver to minimize the influence of estimation error on the subsequent user selection based on semi-orthogonal user selection (SUS). Finally, a mechanism that each selected receiver is informed of all the selected receiver beamformers to adopt the minimum mean square error (MMSE) detector is performed for further reducing the error caused by the uncertainty of the user selection. Simulation results show that the proposed scheme can improve the system spectral efficiency and has robust spectral efficiency gain even under the condition that the uplink pilot SNR is 20dB lower than the downlink pilot SNR, as compared to Quasi-MMSE Weight (QMW) scheme.

Towards win-win: weighted-Voronoi-diagram based channel quantization for security enhancement in downlink cloud-RAN with limited CSI feedback

Physical layer (PHY) security is recently proved to enable improving the security of wireless communication networks. In downlink frequency division duplex (FDD) cloud radio access network (C-RAN), the performance of PHY security highly relies on the channel state information (CSI) which is usually acquired through the codebook-quantization-based technique at the transceiver. However, the conventional quantization method aggravates the leakage of privacy information in C-RAN under the eavesdropping environment. In this paper, a novel channel quantization method is investigated to improve the secrecy-rate performance of C-RAN by exploiting the high-dimension space geometry. Based on this method, it is proved that when the statistical distribution of the channel matrices of both the legitimate user and the eavesdropper is exploited, a win-win situation can be created where secrecy-rate gains are improved without sacrificing beamforming gains from the point of view of ergodic rate. Particularly, a secrecy-oriented criterion is devised to implement the proposed method for generating codebooks. Then a weighted Voronoi diagram (WVD) is formulated on the complex Grassmann manifold and finally, a vector quantization based algorithm is proposed to build up novel quantization codebooks iteratively. Simulation results further validate the superiority of our proposed codebooks over conventional codebooks in C-RAN systems.

Weighted-Voronoi-Diagram Based Codebook Design against Passive Eavesdropping for MISO Systems

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

ICA-SBDC: A channel estimation and identification mechanism for MISO-OFDM systems under pilot spoofing attack

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

Joint Secure Beamforming and User Selection for Multi-user MISO Systems with Confidential Messages

In this paper, we propose a joint secure beamforming and user selection (J-SBS) scheme to improve the secrecy sum rate in multi-user multiple-input-single-output (MISO) broadcast channels with confidential messages (BCC). The user selection mechanism can bring about multi-user diversity which may be also exploited by eavesdroppers in multi-user MISO sysytem. We show how the system secrecy sum rate under the semi-orthogonal user selection (SUS) algorithm will be limited if users collude with each other. To solve this problem, based on the prediction that selected users have mutual orthogonal beamformers, we characterize a new expression of the secrecy rate. By optimizing the secrecy rate we then derive a corresponding suboptimal secrecy beamformer for each user to reduce the interference and information leakage. Moreover, we apply SUS to select suitable beamformers matching with the prediction. Simulation results show the effectiveness of our proposed method.