Ruoyu Zhang

Relay Selection for Improved Security in Cognitive Relay Networks With Jamming

In this letter, we consider an underlay cognitive radio network in which a secondary transmitter (ST) communicates with a secondary destination (SD) with the aid of multiple cognitive relays, and multiple colluding eavesdroppers attempt to intercept the secondary transmission. Assuming that there is no ST-SD link, one of the cognitive relays is selected to convey the confidential information, while the others cooperate in jamming the eavesdroppers by transmitting artificial noise. Since the eavesdroppers’ channel information state is considered to be absent, two opportunistic relay selection schemes are proposed to enhance the security of the system. We derive novel closed-form expressions for the secrecy outage probability. Simulation results are presented to validate our analysis.

Joint channel estimation algorithm based on structured compressed sensing for FDD multi-user massive MIMO

Accurate channel state information (CSI) at transmitter is of importance to sufficiently exploit the merits of massive multiple input multiple output (MIMO). Because of the large amount of antennas at base station (BS), the pilot overhead becomes unaffordable, especially in frequency-division duplexing (FDD) massive MIMO systems. To alleviate the overwhelming pilot overhead, a novel channel estimation algorithm for multi-user massive MIMO system employing structured compressed sensing (CS) theory is proposed. Firstly, the angular domain channel representation of massive MIMO is analyzed. Then, due to the practical scattering environment, the common sparsity and private sparsity structure of channel matrix exist in multi-user massive MIMO system. Finally, basing on the statistical information of multi-user channel matrix, a structured joint subspace matching pursuit (SJSMP) algorithm is proposed, which is to estimate channels with limited pilot jointly at the BS. Particularly, the common support and private support of multi-user channel matrix are separately estimated to reduce the pilot overhead with improved CSI estimation quality in terms of MSE.

Sparsity-aided codebook for limited feedback in FDD massive MIMO system

Abstract Channel state information at transmitter (CSIT) is vital for massive multiple-input-multiple-output (MIMO) system to achieve unprecedented improvement in capacity and spectral efficiency. Unfortunately, CSIT is acquired via channel feedback in frequency-division duplexing (FDD) system, which suffers from large volume of feedback overhead and size of codebook, since they increase with the number of antennas at transmitter. In this paper, by exploiting the channel sparsity in virtual angular domain, a sparsity-aided codebook is proposed for FDD massive MIMO system to alleviate the channel feedback overhead and codebook size. Specifically, a sparsity aided limited channel feedback model is proposed, where the high dimensional channel is decomposed to the limited linear combination with channel coefficients in virtual angular domain. Then, the high dimensional channel can be quantized by the proposed codebook in a more precise way with significantly small codebook size and fed back in low overhead within virtual coherence time. Furthermore, the upper bound of inter-user interference and the per user rate loss incurred by the proposed sparsity-aided codebook are theoretically analyzed, in which the required quantization bits only increase linearly with the channel sparsity level rather than number of BS antennas when the rate loss is limited to a constant level.

A Low Complexity Correlation Algorithm for Compressive Channel Estimation in Massive MIMO System

Channel state information is essential for base station (BS) to fully exploit the merits of massive multiple input multiple output, which consumes large amount of pilot overhead attributed to tremendous number of BS antennas. Accordingly, huge computational complexity of correlation operation tends to be an obstacle for the implementation of compressive channel estimation algorithms, especially for greedy algorithms. In this paper, pilot overhead problem lightens by exploiting common support property due to the close space of BS antenna array. Furthermore, a low complexity correlation algorithm is proposed for each iteration of greedy algorithm, which exploits the inherent of pilot distribution and sensing matrix composed of pilot sequence. Complexity of proposed algorithm related to pilot distribution is also investigated. Performance analysis and simulation results prove that the proposed algorithm maintains the same performance, while achieves much less computational complexity than the original greedy algorithm.

A new iterative phase correction algorithm in conformal antenna array designing

Compared with ordinary linear antenna arrays or planar antenna arrays, space conformal antenna array has many advantages, such as the flexibility and accuracy of the beam. However, conventional algorithms cannot be applied to the designing of conformal antenna arrays, so the weighted minimum mean square error algorithm is proposed. In this paper, a new iterative phase correction algorithm is presented to improve the weighted minimum mean square error (MMSE) algorithm. By using the new algorithm, the shape of the beam can be controlled more flexibly, and more interferences can be resisted compared with the weighted minimum mean square error algorithm. The simulation results show an improved performance in beamforming.

Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system

Acquisition of accurate channel state information (CSI) at transmitters results in a huge pilot overhead in massive multiple input multiple output (MIMO) systems due to the large number of antennas in the base station (BS). To reduce the overwhelming pilot overhead in such systems, a structured joint channel estimation scheme employing compressed sensing (CS) theory is proposed. Specifically, the channel sparsity in the angular domain due to the practical scattering environment is analyzed, where common sparsity and individual sparsity structures among geographically neighboring users exist in multi-user massive MIMO systems. Then, by equipping each user with multiple antennas, the pilot overhead can be alleviated in the framework of CS and the channel estimation quality can be improved. Moreover, a structured joint matching pursuit (SJMP) algorithm at the BS is proposed to jointly estimate the channel of users with reduced pilot overhead. Furthermore, the probability upper bound of common support recovery and the upper bound of channel estimation quality using the proposed SJMP algorithm are derived. Simulation results demonstrate that the proposed SJMP algorithm can achieve a higher system performance than those of existing algorithms in terms of pilot overhead and achievable rate.

Destination Assisted Secret Transmission in Wireless Relay Networks

To improve physical (PHY) layer security of a wireless relay system in the presence of an eavesdropper, a two-phase cooperative relaying scheme is investigated in this paper. In phase I, the source transmits confidential message, simultaneously, it cooperates with the friendly jammers and destination to create jamming signal at the eavesdropper without affecting the forwarding relay which is preselected. In phase II, the forwarding relay retransmits the decoded signal, meanwhile, the particular relay cooperates with the friendly jammers to create jamming signal at the eavesdropper without affecting the destination. We focus on the investigation of optimal power allocation for maximizing achievable secrecy rate subject to a total power constraint. Optimal relay selection and suboptimal relay selection schemes are also proposed. It is shown that as the number of relays increases, both secrecy rate and the performance of suboptimal relay selection scheme improve significantly. Numerical results are presented to validate the derived analytical results and compare them to existing work

Sparse multi-band signal recovery based on support refining for modulated wideband converter

The continuous-time sparse multi-band signal can be acquired with sub-Nyquist rate by modulated wideband converter (MWC), which is a novel sampling technique based on compressed sensing and alleviates the pressure of high sampling rate. The performance of existing reconstruction algorithm for MWC suffers from low probability of successful recovery, especially when the signal noise ratio (SNR) is not high. In this paper, a sparse multi-band signal recovery algorithm based on support refining (SRSR) is proposed for MWC. Particularly, the support refining process is incorporated into the reconstruction stage of MWC. The initial support of the original multi-band signal is firstly acquired by the existing multi measurement vector (MMV) algorithm. Then, the support refining process refines the initial support to extract the inaccurate support information. Finally, the whole recovery algorithm ends up with recovering the remaining support by the existing MMV algorithm. Simulation results demonstrate that the proposed SRSR algorithm improves the success rate of sparse multi-band signal recovery and enables MWC require fewer channels of sampling architecture and lower hardware complexity.