Ting Jiang

Joint Optimization of User Grouping and Transmitter Connection on Multi-Cell SNR Blind Interference Alignment

Blind interference alignment (BIA) can greatly improve the degree of freedom with the infinite signal-to-noise ratio (SNR) assumption. Under the finite SNR condition, noise accumulation can have a significantly negative impact on SNR, inducing severe performance deterioration. In particular, in multi-cell networks, the transmitter to which a user connects can further affect its received SNR and the BIA design. To address such problem, we present a user grouping scheme for reducing noise accumulation in a single cell and analyze the impact of transmitter connections on the user grouping scheme. SNR BIA in a multi-cell network is further proposed, which jointly optimizes the transmitter connection and the user grouping scheme. Extensive simulations demonstrate that the achievable sum rate of SNR BIA is 1.36 times, 1.66 times, and 2.68 times that of data shared BIA, standard BIA, and extended BIA reported in the literature, respectively, and SNR BIA is more robust to user mobility.

Safeguarding multiuser communication using full-duplex jamming and Q-learning algorithm

In this study, the authors consider secure communications in multiuser wireless networks where full-duplex (FD) jamming operates to enhance physical layer security. The considered multiuser system is equipped with FD legitimate receivers in contrast to conventional frameworks where a half-duplex (HD) receiver is at hand. This study investigates an alternative solution in which the authors take advantage of FD capability of the receivers to send jamming signals against the eavesdropper. Under these assumptions, the impact of self-interference and channel interference on physical layer security is investigated. They derive the expressions of secrecy outage probability and ergodic secrecy rate in the proposed system. Two FD jammer selection schemes are proposed to further improve the security. In addition, they apply a reinforcement learning technology, called Q-learning, to model the interaction between the source and multiple jamming users. The preliminary results show that the application of FD jamming and user selection scheme leads to a significant improvement in the wireless network security.

SNR aware heterogeneous blind interference alignment in MISO broadcasting channel

The standard Blind Interference Alignment (BIA) accumulates noise levels and causes great Signal Noise Ratio (SNR) deteriorations in the condition that the received SNR is not infinite. To address such problem, we take SNR feedback into consideration. We first define SNR reduced factor, reflecting SNR reduction between the received SNR and the final SNR for decoding data streams. A heterogeneous BIA (hBIA) is then presented where the users form different groups, and each group further consists of several sub-groups. Within one group, the users in one sub-group perform BIA with other sub-groups users, while the users in different groups have different SNR reduced factors. To maximize the overall effective degree of freedom (DoF) with SNR guarantee, a greedy algorithm is proposed for allocating the users into different groups and subgroups. Evaluations demonstrate that hBIA achieves about 375% and 540% effective DoF improvement on average compared with the standard BIA and the grouping based BIA, respectively.

Channel sparse representation based authentication for reconciliation of key generation

We propose a novel authentication method based on sparse representation for the reconciliation in physical layer key Generation. In order to protect the legitimation of the channel, sparse representation is considered to enhance wireless security by using the unique channel location information. In contrast to traditional approaches, in this paper, we consider that this reconciliation channel is unauthentic, and we formulate this problem as one of improved sparse representation. In this scheme, the position of the primary decomposition coefficients is adopted to ascertain whether the signal comes from legitimate devices or not. The effectiveness of this new process model is validated through experiments and simulations. These results shown that the proposed scheme can achieve desired identification performance even at lower signal-to-noise ratio (SNR).

A fusion method for object identification in rainy weather based on PSO-SFLA and UWB

Ultra-wideband (UWB) is a short-range wireless communication technology with strong resolution, precious detection and anti-jamming. UWB technology can complete the target recognition in the communication process. Whats more, UWB has many great advantages in the aspect of transmission rate, power consumption and cost, so it is used in the personnel orientation, object detection and obstacle recognition widely. In this paper, the target recognition based on UWB is realized in rainy weather firstly with support vector machine (SVM). For the feature extraction, the method of principal component analysis (PCA) is used to reduce the dimension which can shorten the running time of the algorithm. In order to improve the recognition rate, the parameter of SVM should be optimized. Based on shuffled frog leaping algorithm (SFLA) and particle swarm optimization (PSO), this paper proposed PSO-SFLA which has better performance in global optimal ability and convergence speed.

Paired-user blind interference alignment for improving power efficiency in a two-cell network

Blind Interference Alignment (BIA) can greatly improve channel capacity through simultaneous transmitting multiple encoded data streams (EDSs), but can also result in low power efficiency, especially in the multi-network scenario. To address such problem, this paper takes the first step in this direction and presents paired-user BIA in a two-cell network, where users form pairs performing BIA improving transmission power on each EDS and overall degree of freedom. The achievable sum rate is derived and simulation experiment demonstrates that the achievable sum rate of paired-user BIA is 40% higher than that of network BIA with the same transmission power.

SNR aware SBIA in multiple-input–single-output broadcast channel

Blind interference alignment (BIA) can eliminate users’ interference without channel state information and greatly improve the channel capacity, which has been regarded as a promising technique in next generation wireless systems. However, current BIA mechanisms neglect noise accumulations, resulting in final signal-to-noise ratio (fSNR) deterioration and reduced transmission rates. Motivated by ameliorating such negative impact induced by noise accumulations, this study establishes an SNR aware semi-BIA (SNR SBIA) framework in the multi-input–single-output broadcast channel, where the users form different groups performing BIA for different SNR reductions. An SNR SBIA algorithm is further proposed based on tabu search for maximising the overall performance ensuring all users’ fSNRs higher than an SNR threshold. Extensive evaluations demonstrate that SNR SBIA can adapt to the users’ diverse received SNRs and the corresponding achievable sum rate is about 1.38 times and 1.92 times that in standard BIA and grouped hierarchical BIA, respectively.

PPBSP circular correlation based channel estimation for CP-OFDM systems

A perfect punctured binary sequence pairs (PPBSP) circular correlation based channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) is proposed. The original sequence of PPBSP with Cyclic Prefix (CP) is inserted between OFDM symbols as training sequence. The cross-correlation is calculated between the received sequence with CP removed and the punctured sequence of PPBSP. Channel Impulse Response (CIR) is decided with good autocorrelation property of PPBSP. Both theoretical analysis and simulation result show that, compared with the traditional method, an impressive performance with less system overhead and lower computation burden is obtained with the proposed method.