Chaowei Wang

An Advanced Dynamic Framed-Slotted ALOHA Algorithm Based on Bayesian Estimation and Probability Response

This paper proposes an advanced dynamic framed-slotted ALOHA algorithm based on Bayesian estimation and probability response (BE-PDFSA) to improve the performance of radio frequency identification (RFID) system. The Bayesian estimation is introduced to improve the accuracy of the estimation algorithm for lacking a large number of observations in one query. The probability response is used to adjust responsive probability of the unrecognized tags to make the responsive tag number equal to the frame length. In this way, we can solve the problem of high collision rate with the increase of tag number and improve the throughput of the whole system. From the simulation results, we can see that the algorithm we proposed can greatly improve the stability of RFID system compared with DFSA and other commonly used algorithms.

Partial Interference Alignment for Multi-Cell and Multi-User MIMO Downlink Transmission

As a promising technology to effectively mitigate interference and improve the performance of a wireless communication network, interference alignment (IA) has been attracted extensive concern. In order to solve the problem that perfect IA needs a large number of antennas and facing the cell selection, this paper proposes a partial interference alignment scheme based on user grouping for a network with multi-cell multi-user multiple- input and multiple-output (MIMO) under downlink transmission scenario. In the proposed scheme, the interference at different directions does not need to be aligned to the same subspace. Furthermore, each BS eliminates inter-user interference within its cell and the dominant interference to users in its neighboring cells. Analysis and simulation results show that the proposed approach outperforms the extension of grouping method in terms of antenna number and sum capacity.

Energy Saving Dynamic Relaying Scheme in Wireless Cooperative Networks Using Markov Decision Process

Energy saving becomes one of the most important design considerations in wireless cooperative networks which are composed of nodes typically powered by batteries that can supply only a finite amount of energy. In this paper, we propose a dynamic relaying scheme based on relay selection and physical-layer power control with the objective of minimizing the energy consumption for data transmission. We first develop a mathematical model for the cooperative relaying and analyze the total battery energy consumption to forward a symbol. Based on the analytic results and Markov channel model, we formulate the optimization problem that minimizes total average energy consumption as a Markov decision process, with which we can decide an optimal relay and the transmission power distributely. In the proposed scheme, the relay selection process and cooperation mode starts only when the direct transmission between source and destination node failed, which is energy efficient from a network sense. Numerical simulation show that the proposed scheme achieves significant energy savings.

A machine-to-machine application: Body posture recognition on smartphones for intelligent remote control

The protection for individual privacy especially in personal computers has drawn sharply increasing attentions all over the world. Owing to the concept of machine-to-machine (M2M) communication that plays a significant role in the Internet of things (IoT), Sit&Stand, an android application is ergonomically developed to help people with smartphones prevent privacy disclosure through locking or unlocking their computers intelligently and automatically according to their body postures such as stand-up or sit-down. This paper first introduces the M2M communication, and describes system model and the principle to recognize body postures intelligently. And then, the application implementation is stated in detail. Finally a few tests are made to verify the availability of the application and further enhance its performance.

A Public Transport Bus as a Flexible Mobile Smart Environment Sensing Platform for IoT

In this paper we present the requirements, design and pre-deployment testing of a transportation bus as a Mobile Enterprise Sensor Bus (M-ESB) service in China that supports two main requirements: to monitor the urban physical environment, and to monitor road conditions. Although, several such projects have been proposed previously, integrating both environment and road condition monitoring and using a data exchange interface to feed a data cloud computing system, is a novel approach. We present the architecture for M-ESB and in addition propose a new management model for the bus company to act as a Virtual Mobile Service Operator. Pre-deployment testing was undertaken to validate our system.

An Enhanced Coarse Synchronization Scheme with Low Complexity for 3GPP LTE

A new coarse synchronization scheme for 3rd Generation Partnership Project Long Term Evolution (3GPP-LTE) is proposed. Existing coarse synchronization schemes based on central symmetry utilize either primary synchronization signal (PSS) and secondary synchronization signal (SSS) to fulfill initial timing synchronization. Though these two schemes perform well with relative low complexity, they can be further enhanced. Therefore a new scheme is proposed in this paper, which combines PSS with SSS and utilizes hierarchical computing to implement initial timing synchronization. Simulation results show that the new scheme can acquire higher detection accuracy rate without increasing computational complexity.

Hierarchical spectrum sharing for cognitive radio networks based on microeconomic theory

In this paper, we consider the problem of hierarchical bandwidth sharing in cognitive radio (CR) environment. In the system model under consideration, a primary service provider (PSP) can sell its available spectrum bandwidth to a secondary service provider (SSP). In contrast to the conventional schemes, the SUs served by the SSP can also share the unsold bands of the PSP through the approach of opportunistic spectrum access (OSA), which will introduce interference to the PUs and make the PSP suffer a profit loss due to the sensing imperfections in practical networks. Based on microeconomic theory, this problem of bandwidth allocation is formulated as a market model, and an equilibrium where both the PSP and SSP are satisfied with the amount of allocated bandwidth and the price is derived by using the concept of demand and supply functions. Moreover, an iterative algorithm for distributed implementation of our scheme is also proposed. Furthermore, simulation results and comparisons are presented to illustrate the performance of the proposed scheme.

On Critical Density for Coverage and Connectivity in Directional Sensor Network Using Continuum Percolation

Sensing coverage in wireless sensor network is one of the vital performance metrics which can arise in all design stages and usually considered together with some other performance metrics, such as connectivity or energy consumption. Whatever the metrics, the fundamental problem is to know at least how many sensors are needed to maintain both sensing coverage and network connectivity. In this paper, we proposed a percolation-based coverage & connectivity combined model (PCCC) to obtain the critical density at which the network abruptly becomes covered/connected. The PCCC is based on directional sensor network in which sensors are assigned a determined sense direction with the angular interval varies from 0 to 2π. Besides, we also discussed the coverage and connectivity together as a whole under the model proposed. It is worth mentioning that the theoretical analysis as well as simulation results of relationship between critical density and transmitting gives insights into the practical design of directional sensor network.

A Fast Adaptive Receive Antenna Selection Method in MIMO System

Antenna selection has been regarded as an effective method to acquire the diversity benefits of multiple antennas while potentially reduce hardware costs. This paper focuses on receive antenna selection. According to the proportion between the numbers of total receive antennas and selected antennas and the influence of each antenna on system capacity, we propose a fast adaptive antenna selection algorithm for wireless multiple-input multiple-output (MIMO) systems. Mathematical analysis and numerical results show that our algorithm significantly reduces the computational complexity and memory requirement and achieves considerable system capacity gain compared with the optimal selection technique in the same time.

A Simplified Estimate-and-Forward Scheme for Relay Networks

Relay aided wireless transmission realizes extended coverage and increases capacity for wireless communication networks, especially for the TDD/FDD LTE-A system. The relay node processes the received signal from source under certain rules and forwards the signal to destination. Amplify-and-Forward (AF) and Decode-and-Forward (DF) are two of the conventional relay schemes commonly used and cited. Estimate-and-Forward is a well-know but much less studied approach. The basic idea is to forward an estimate version of original signal without hard decision. A soft symbol estimating approach by solving linear equation set structured with received symbol probabilities. This algorithm makes a least squares fit of the estimated symbol probabilities to a single shot noisy symbol a posteri probability (APP) model. In the simulation, we take frame error rate (FER) to demonstrate the proposed scheme outperforms existing relay schemes.