Hongxi Yin

A new family of 2-D optical orthogonal codes and analysis of its performance in optical CDMA access networks

A new family of two-dimensional optical orthogonal code (2-D OOC), one-coincidence frequency hop code (OCFHC)/OOC, which employs OCFHC and OOC as wavelength hopping and time-spreading patterns, respectively, is proposed in this paper. In contrary to previously constructed 2-D OOCs, OCFHC/OOC provides more choices on the number of available wavelengths and its cardinality achieves the upper bound in theory without sacrificing good auto-and-cross correlation properties, i.e., the correlation properties of the code is still ideal. Meanwhile, we utilize a new method, called effective normalized throughput, to compare the performance of diverse codes applicable to optical code division multiple access (OCDMA) systems besides conventional measure bit error rate, and the results indicate that our code performs better than obtained OCDMA codes and is truly applicable to OCDMA networks as multiaccess codes and will greatly facilitate the implementation of OCDMA access networks.

A Novel Interference Alignment Scheme Based on Sequential Antenna Switching in Wireless Networks

Interference alignment (IA) is a promising technique that can effectively eliminate the interference in wireless networks. However, in traditional IA schemes, the signal to interference plus noise ratio (SINR) may significantly degrade, and the quality of service (QoS) may be unacceptable. In this paper, a novel IA scheme based on antenna switching (AS-IA) is proposed to improve the SINR of the received signal while guaranteeing the QoS in IA wireless networks. In the proposed scheme, some of the antennas are replaced by reconfigurable ones that can switch among preset modes, and the best channel coefficients are selected. Furthermore, to reduce the computational complexity, a sequential antenna switching IA (SAS-IA) scheme is proposed with only one antenna switching in each time slot, and the communication proceeds during the process of searching for the optimal solution. To further improve the performance of the SAS-IA scheme under imperfect channel state information (CSI), a filtering SAS-IA scheme is proposed through averaging the estimated CSI during the iterations of the distributed IA algorithm. Simulation results are presented to show the effectiveness and efficiency of the proposed schemes in improving the QoS of IA wireless networks.

Big Data Analytics in Mobile Cellular Networks

Mobile cellular networks have become both the generators and carriers of massive data. Big data analytics can improve the performance of mobile cellular networks and maximize the revenue of operators. In this paper, we introduce a unified data model based on the random matrix theory and machine learning. Then, we present an architectural framework for applying the big data analytics in the mobile cellular networks. Moreover, we describe several illustrative examples, including big signaling data, big traffic data, big location data, big radio waveforms data, and big heterogeneous data, in mobile cellular networks. Finally, we discuss a number of open research challenges of the big data analytics in the mobile cellular networks.

A new family of 2D variable-weight optical orthogonal codes for OCDMA systems supporting multiple QoS and analysis of its performance

A new family of two-dimensional variable-weight and constant-length optical orthogonal codes (2D VWOOCs) is proposed, and the code cardinality and BER performance for the corresponding OCDMA system are analyzed in this article. It is shown that the cardinality of 2D VWOOC is larger than that of constant-weight 2D OOC and close to the upper bound in theory. In an OCDMA network, the users employing 2D VWOOC codewords with larger Hamming weight outperform the users using 2D VWOOC codewords with smaller Hamming weight in bit-error-rate performance. Therefore, the OCDMA network employing 2D VWOOC can support diverse quality-of-services (QoS) classes and multimedia services, and make the better use of bandwidth resources in optical networks.

Interference alignment with delayed channel state information and dynamic AR-model channel prediction in wireless networks

Interference alignment (IA) is a promising technique that can effectively eliminate the interferences in multiuser wireless networks. However, it requires highly accurate channel state information (CSI) of the whole network at all the transmitters and receivers. In practical wireless systems, it is difficult to obtain the perfect knowledge of a dynamic channel. Particularly, the CSI at transmitters used in IA is usually delayed through feedback, which will dramatically affect the performance of IA. In this paper, the performance of IA with delayed CSI is studied. The expressions of the average signal to interference plus noise ratio and sum rate of IA networks with delayed CSI are established. To alleviate the influence of delayed CSI, an IA scheme based on dynamic autoregressive (AR)-model channel prediction is proposed, in which the parameters of AR mode are updated frequently. The CSI of the next time instant is predicted using the present and past CSI in the proposed scheme to improve the performance of IA networks. Two key factors of the scheme, window length and refresh rate are analyzed in detail. Simulation results are presented to show that the proposed IA scheme based on channel prediction can significantly improve its performance with delayed CSI.

Interference alignment based on channel prediction with delayed channel state information

Interference alignment (IA) is a promising technique that can eliminate the interference in multi-user communication networks effectively. However, it requires highly accurate and real-time channel state information (CSI) at both transmitters and receivers. In practical systems, it is difficult to obtain the perfect knowledge of a dynamic channel due to channel estimation errors, communication latency and capacity constraints. Particularly, transmitters in IA systems usually get imperfect CSI fed back from receivers with a delay, which will greatly affect the performance of IA. In this paper, the performance of IA with delayed CSI is studied, and the decrease of the total network capacity due to the delayed CSI is analyzed. To mitigate the influence of the delayed CSI, an IA scheme based on channel prediction is proposed using two easy-to-implement and practical channel predictors, minimum mean square estimate (MMSE) and weighted least squares error (WLSE) predictors. The CSI of the next time instant is predicted using the present and past CSI. Simulation results are presented to show the effectiveness of the channel prediction IA schemes with the delayed channel knowledge.

Long-haul dense wavelength division multiplexing between a chaotic optical secure channel and a conventional fiber-optic channel

The purpose of this paper is to numerically investigate dense wavelength division multiplexing (DWDM) transmission between a chaotic optical secure channel and a conventional fiber-optic channel. A 2.5 Gbits/s secure message masked by the chaotic optical secure channel and a 10 Gbits/s message sequence carried by the conventional fiber-optic channel can be realized simultaneously when the channel spacing is 0.8 nm. The results show that the Q-factors of the recovered messages can be increased significantly when the launched optical power is reduced appropriately. The deterioration of the quality of communication caused by fiber dispersion can be compensated noticeably on the condition that the symmetrical dispersion compensation scheme is adopted. In addition, the secure message is masked by chaos shift keying in the chaotic optical secure channel. The multiplexing distance between the chaotic optical secure channel and the conventional fiber-optic channel is up to 500 km.

Experimental demonstration of polarization-division multiplexing of chaotic laser secure communications

Optical polarization-division multiplexing (PDM) can double the capacity of a communication system. In this paper, PDM between a conventional fiber-optic channel and a chaos-encrypted channel, and between two chaos-encrypted channels, is proposed and experimentally investigated. The bit rate for each channel is 1.25 Gb/s, while the transmission in the standard single-mode fiber can be up to 22.54 km. The effect of the mutual power leakages on the receiver quality is experimentally explored, which is induced by the variation in polarization direction during the propagating process. In addition, the effect of optical launched power at the transmitter side on the Q-factor is tested and analyzed.

Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach

The developments of connected vehicles are heavily influenced by information and communications technologies, which have fueled a plethora of innovations in various areas, including networking, caching, and computing. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem, where the gains of not only networking but also caching and computing are taken into consideration in the proposed framework. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.

Optimization of cache-enabled opportunistic interference alignment wireless networks: A big data deep reinforcement learning approach

Both caching and interference alignment (IA) are promising techniques for future wireless networks. Nevertheless, most of existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, we propose a novel big data reinforcement learning approach in this paper. Deep reinforcement learning is an advanced reinforcement learning algorithm that uses deep Q network to approximate the Q value-action function. Deep reinforcement learning is used in this paper to obtain the optimal lA user selection policy in cache-enabled opportunistic lA wireless networks. Simulation results are presented to show the effectiveness of the proposed scheme.