Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
Abstract: Wireless big data describes a wide range of massive data that is generated, collected and stored in wireless networks by wireless devices and users. While these data share some common properties with traditional big data, they have their own unique characteristics and provide numerous advantages for academic research and practical applications. This article reviews the recent advances and trends in the field of wireless big data. Due to space constraints, this survey is not intended to cover all aspects in this field, but to focus on the data aided transmission, data driven network optimization and novel applications. It is expected that the survey will help the readers to understand this exciting and emerging research field better. Moreover, open issues and promising future directions are also identified.
Abstract: In HetNets (Heterogeneous Networks), each network is allocated with fixed spectrum resource and provides service to its assigned users using specific RAT (Radio Access Technology). Due to the high dynamics of load distribution among different networks, simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices, the consequent upsurge of data traffic, and dynamic user QoE(Quality-of-Experience). The deployment of smart networks, which are supported by SRA (Smart Resource Allocation)among different networks and CUA (Cognitive User Access) among different users, is deemed a promising solution to these challenges. In this paper, we propose a framework to transform HetNets to smart networks by leveraging WBD(Wireless Big Data), CR(Cognitive Radio) and NFV (Network Function Virtualization) techniques. CR and NFV support resource slicing in spectrum, physical layers, and network layers, while WBD is used to design intelligent mechanisms for resource mapping and traffic prediction through powerful AI (Artificial Intelligence) methods. We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks. In particular, the potential of WBD is revealed through high level view on SRA, which intelligently maps radio and network resources to each network for meeting the dynamic traffic demand, as well as CUA, which allows mobile users to access the best available network with manageable cost, yet achieving target QoS(Quality-of-Service)or QoE.
Abstract: Future communication systems will include different types of messages requiring different transmission rates, packet lengths, and service qualities. We address the power-optimization issues of communication systems conveying multiple message types based on finite-delay information theory. Given both the normalized transmission rate and the packet length of a system, the actual residual decoding error rate is a function of the transmission power. We propose a generalized power allocation framework for multiple message types. Two different optimization cost functions are adopted: the number of service-quality violations encountered and the sum log ratio of the residual decoding error rate. We provide the optimal analytical solution for the former cost function and a heuristic solution based on a genetic algorithm for the latter one. Finally, the performance of the proposed solutions are evaluated numerically.
Abstract: Wireless channel modeling has always been one of the most fundamental highlights of the wireless communication research. The performance of new advanced models and technologies heavily depends on the accuracy of the wireless CSI (Channel State Information). This study examined the randomness of the wireless channel parameters based on the characteristics of the radio propagation environment. The diversity of the statistical properties of wireless channel parameters inspired us to introduce the concept of the tomographic channel model. With this model, the static part of the CSI can be extracted from the huge amount of existing CSI data of previous measurements, which can be defined as the wireless channel feature. In the proposed scheme for obtaining CSI with the tomographic channel model, the GMM(Gaussian Mixture Model)is applied to acquire the distribution of the wireless channel parameters, and the CNN(Convolutional Neural Network) is applied to automatically distinguish different wireless channels. The wireless channel feature information can be stored o?ine to guide the design of pilot symbols and save pilot resources. The numerical results based on actual measurements demonstrated the clear diversity of the statistical properties of wireless channel parameters and that the proposed scheme can extract the wireless channel feature automatically with fewer pilot resources. Thus, computing and storage resources can be exchanged for the finite and precious spectrum resource.
Abstract: Wireless big data is attracting extensive attention from operators, vendors and academia, which provides new freedoms in improving the performance from various levels of wireless networks. One possible way to leverage big data analysis is predictive resource allocation, which has been reported to increase spectrum and energy resource utilization efficiency with the predicted user behavior including user mobility. However, few works address how the traffic load prediction can be exploited to optimize the data-driven radio access. We show how to translate the predicted traffic load into the essential information used for resource optimization by taking energy-saving transmission for non-real-time user as an example. By formulating and solving an energy minimizing resource allocation problem with future instantaneous bandwidth information, we not only provide a performance upper bound, but also reveal that only two key parameters are related to the future information. By exploiting the residual bandwidth probability derived from the traffic volume prediction, the two parameters can be estimated accurately when the transmission delay allowed by the user is large, and the closed-form solution of global optimal resource allocation can be obtained when the delay approaches infinity. We provide a heuristic resource allocation policy to guarantee a target transmission completion probability when the delay is no-so-large. Simulation results validate our analysis, show remarkable energy-saving gain of the proposed predictive policy over non-predictive policies, and illustrate that the time granularity in predicting traffic load should be identical to the delay allowed by the user.
Abstract: Atmospheric ducts are horizontal layers that occur under certain weather conditions in the lower atmosphere. Radio signals guided in atmospheric ducts tend to experience less attenuation and spread much farther, i. e, hundreds of kilometers. In a large-scale deployed TD-LTE(Time Division Long Term Evolution) network, atmospheric ducts cause faraway downlink wireless signals to propagate beyond the designed protection distance and interfere with local uplink signals, thus resulting in a large outage probability. In this paper, we analyze the characteristics of ADI atmospheric duct interference (Atmospheric Duct Interference) by the use of real network-side big data from the current operated TD-LTE network owned by China Mobile. The analysis results yield the time varying and directional characteristics of ADI. In addition, we proposed an SVM (Support Vector Machine)-classifier based spacial prediction method of ADI by machine learning over combination of real network-side big data and real meteorological data. Furthermore, an implementation of ADMM (Alternating Direction Methods of Multipliers) framework is proposed to implement a distributed SVM prediction scheme, which reduces data exchange among different regions/cities, maintains similar prediction accuracy and is thus of a more practical use to operators.
Abstract: The past decade has witnessed explosive growth in wireless big data, as well as in various big data analytics technologies. The intelligence mined from these massive datasets can be utilized to optimize wireless system design. Due to the open data policy of the mainstream OSN(Online Social Network)service providers and the pervasiveness of online social services, this paper studies how social big data can be embraced in wireless communication system design. We start with our first hand experience on crawling social big data and the principal of social-aware system design. Then we present five studies on utilizing social intelligence for system optimization, including community-aware social video distribution over cloud content delivery networks, public cloud assisted mobile social video sharing, data driven bitrate adjustment and spectrum allocation for mobile social video sharing, location-aware video streaming, and social video distribution over information-centric networking.
Abstract: The explosive growth in data traffic presents new challenges to the new generation of wireless communication systems, such as computing capabilities, spectrum efficiency and security. In this paper, we use the network structure, which is adaptable for the big data traffic, to improve the security of wireless networks. Specifically, a big-data aided hybrid relay selection scheme is designed and analyzed to enhance physical layer security. First, considering the ideal situation that an eavesdropper’s CSI (Channel State Information) is known to the legal nodes, we propose an optimal hybrid relay selection scheme consisting of the optimal mode selection scheme and the optimal relay selection scheme. In this case, we analyze the upper bound of an eavesdropper’s capacity in FD (Full-Duplex) mode and the secrecy outage probabilities of the optimal HD (Half-Duplex), FD, and hybrid relay selection schemes. Through the analysis of data, it is clear that the mode selection is decided by the self-interference of the FD technique. However, the instantaneous CSI of an eavesdropper is difficult to obtain due to the passive characteristic of eavesdroppers in practice. Therefore, a more practical hybrid relay selection scheme with only the channel distribution information of an eavesdropper is further studied, where a weighting factor is employed to guarantee that the hybrid mode is no worse than either the FD mode or HD mode when the self-interference grows. Finally, the simulation results show the improved security of our proposed scheme.
