Zhenyu Na

Extended Kalman Filter for Real Time Indoor Localization by Fusing WiFi and Smartphone Inertial Sensors

Indoor localization systems using WiFi received signal strength (RSS) or pedestrian dead reckoning (PDR) both have their limitations, such as the RSS fluctuation and the accumulative error of PDR. To exploit their complementary strengths, most existing approaches fuse both systems by a particle filter. However, the particle filter is unsuitable for real time localization on resource-limited smartphones, since it is rather time-consuming and computationally expensive. On the other hand, the light computation fusion approaches including Kalman filter and its variants are inapplicable, since an explicit RSS-location measurement equation and the related noise statistics are unavailable. This paper proposes a novel data fusion framework by using an extended Kalman filter (EKF) to integrate WiFi localization with PDR. To make EKF applicable, we develop a measurement model based on kernel density estimation, which enables accurate WiFi localization and adaptive measurement noise statistics estimation. For the PDR system, we design another EKF based on quaternions for heading estimation by fusing gyroscopes and accelerometers. Experimental results show that the proposed EKF based data fusion approach achieves significant localization accuracy improvement over using WiFi localization or PDR systems alone. Compared with a particle filter, the proposed approach achieves comparable localization accuracy, while it incurs much less computational complexity.

A novel adaptive traffic prediction AQM algorithm

In the Internet, network congestion is becoming an intractable problem. Congestion results in longer delay, drastic jitter and excessive packet losses. As a result, quality of service (QoS) of networks deteriorates, and then the quality of experience (QoE) perceived by end users will not be satisfied. As a powerful supplement of transport layer (i.e. TCP) congestion control, active queue management (AQM) compensates the deficiency of TCP in congestion control. In this paper, a novel adaptive traffic prediction AQM (ATPAQM) algorithm is proposed. ATPAQM operates in two granularities. In coarse granularity, on one hand, it adopts an improved Kalman filtering model to predict traffic; on the other hand, it calculates average packet loss ratio (PLR) every prediction interval. In fine granularity, upon receiving a packet, it regulates packet dropping probability according to the calculated average PLR. Simulation results show that ATPAQM algorithm outperforms other algorithms in queue stability, packet loss ratio and link utilization.

Probabilistic Caching Placement in the Presence of Multiple Eavesdroppers

The wireless caching has attracted a lot of attention in recent years, since it can reduce the backhaul cost significantly and improve the user-perceived experience. The existing works on the wireless caching and transmission mainly focus on the communication scenarios without eavesdroppers. When the eavesdroppers appear, it is of vital importance to investigate the physical-layer security for the wireless caching aided networks. In this paper, a caching network is studied in the presence of multiple eavesdroppers, which can overhear the secure information transmission. We model the locations of eavesdroppers by a homogeneous Poisson Point Process (PPP), and the eavesdroppers jointly receive and decode contents through the maximum ratio combining (MRC) reception which yields the worst case of wiretap. Moreover, the main performance metric is measured by the average probability of successful transmission, which is the probability of finding and successfully transmitting all the requested files within a radius . We study the system secure transmission performance by deriving a single integral result, which is significantly affected by the probability of caching each file. Therefore, we extend to build the optimization problem of the probability of caching each file, in order to optimize the system secure transmission performance. This optimization problem is nonconvex, and we turn to use the genetic algorithm (GA) to solve the problem. Finally, simulation and numerical results are provided to validate the proposed studies.

Subcarrier allocation based Simultaneous Wireless Information and Power Transfer algorithm in 5G cooperative OFDM communication systems

Abstract Self-sustainable communications are highly vital for large amounts of mobile terminals in the Fifth Generation (5G) communication systems. Simultaneous Wireless Information and Power Transfer (SWIPT) makes it possible that terminal transfers information while prolonging battery life by harvesting Radio Frequency (RF) energy. Though the traditional sub-carrier allocation based SWIPT algorithm in OFDM communication systems can optimize resource allocation, the receiver often cannot achieve higher information decoding rate when the channel condition of direct transmission deteriorates. In view of this situation, a sub-carrier allocation based SWIPT algorithm in 5G cooperative OFDM communication systems is proposed in this paper. The amplify-and-forward protocol is adopted by relay node which transmits information from source node to destination node by using a part of its sub-carriers. The remaining sub-carriers are used for energy harvesting. On the premise of minimum threshold of harvested energy, the sub-carrier and power allocations at relay node are optimized by establishing and solving the corresponding optimization model. Simulation results reveal that the proposed algorithm not only achieves the optimal sub-carrier and power allocations, but also improves information decoding rate with fast converging speed.

Spectrum Optimization for Cognitive Satellite Communications With Cournot Game Model

With the availability of high-throughput satellite services at affordable cost, terrestrial network providers make use of satellite links to extend their coverage to areas, where land-based communication infrastructures are prohibitively costly to implement. Due to the ever-increasing demand for bandwidth resulted from the rapid development of data-intensive services in recent years, one of the fundamental challenges for satellite communications is to continuously improve utilization efficiency of the scarce satellite spectrum. Cognitive satellite communications address the problem by providing mechanisms for terrestrial and satellite users to dynamically access idle bands of licensed satellite networks, hence enabling spectrum sharing between two satellite systems or between satellite and terrestrial systems. In this paper, we investigate a distributed technique of spectrum sharing for cognitive satellite networks. In order to cater for situations with incomplete decision information, the proposed scheme is based on the Bayesian equilibrium theory. We first develop a spectrum allocation scheme by studying the action strategy of terrestrial cognitive terminals in a distributed competition. A feasible spectrum allocation scheme that caters for cases of incomplete user information is then developed as an extension of the basic scheme by formulating the problem as a Cournot game model. By identifying the unique equilibrium in this model, optimal spectrum allocation for cognitive satellite networks can be achieved. Essential discussions and proofs for the rationality of this method and uniqueness of the equilibrium are provided. Numerical results are given to justify the claimed advantages.

Caching Efficiency Enhancement at Wireless Edges with Concerns on User’s Quality of Experience

Content caching is a promising approach to enhancing bandwidth utilization and minimizing delivery delay for new-generation Internet applications. The design of content caching is based on the principles that popular contents are cached at appropriate network edges in order to reduce transmission delay and avoid backhaul bottleneck. In this paper, we propose a cooperative caching replacement and efficiency optimization scheme for IP-based wireless networks. Wireless edges are designed to establish a one-hop scope of caching information table for caching replacement in cases when there is not enough cache resource available within its own space. During the course, after receiving the caching request, every caching node should determine the weight of the required contents and provide a response according to the availability of its own caching space. Furthermore, to increase the caching efficiency from a practical perspective, we introduce the concept of quality of user experience (QoE) and try to properly allocate the cache resource of the whole networks to better satisfy user demands. Different caching allocation strategies are devised to be adopted to enhance user QoE in various circumstances. Numerical results are further provided to justify the performance improvement of our proposal from various aspects.

Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space-terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time-varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

Channel Estimation in Next Generation LEO Satellite Communicastion Systems

Low earth orbit (LEO) satellite communication systems are the key parts of Space-Air-Ground networks. In order to deal with the scarcity of spectrum source, generalized frequency division multiplexing (GFDM) becomes a candidate for next generation LEO satellite systems. In LEO satellite communication systems, channel estimation is an indispensable technique to adapt to complex satellite channel environment. Because of the non-orthogonality between GFDM subcarriers, conventional channel estimation techniques can’t achieve the desired performance. We propose a Turbo receiver channel estimation method with threshold control to improve the channel estimation performance by utilizing the feedback information from Turbo decoder. The numerical and analytical results show that the proposed method can achieve better performance over LEO satellite channel.

Privacy Protection for Location Sharing Services in Social Networks

Recently, there is an increase interest in location sharing services in social networks. Behind the convenience brought by location sharing, there comes an indispensable security risk of privacy. Though many efforts have been made to protect user’s privacy for location sharing, they are not suitable for social network. Most importantly, little research so far can support user relationship privacy and identity privacy. Thus, we propose a new privacy protection protocol for location sharing in social networks. Different from previous work, the proposed protocol can provide perfect privacy for location sharing services. Simulation results validate the feasibility and efficiency of the proposed protocol.

Subcarrier Allocation-Based Simultaneous Wireless Information and Power Transfer for Multiuser OFDM Systems

Most of existing works on simultaneous wireless information and power transfer (SWIPT) for OFDM systems are studied based on power splitting or time splitting, which may lead to the time delay and the decreasing of sub-carrier utilization. In this paper, a multiuser orthogonal frequency division multiplexing (OFDM) system is proposed, which divides the sub-carriers into two parts, one for information decoding and the other one for energy harvesting. We investigate the optimization problem for maximizing the sum rate of users under the constraint of energy harvesting through optimizing the channel allocation and power allocation. By using the iterative algorithm, the optimal solution to the optimization problem can be achieved. The simulation results show that the proposed algorithm converges fast and outperforms the conventional algorithm.