Spatially localized and joint access point selection for Wi-Fi indoor positioning

Wi-Fi indoor positioning system has received increasing interest in pervasive computing applications due to its low cost and satisfactory accuracy. To obtain high positioning accuracy based on source limited devices, various AP selection strategies have been proposed to select the most discriminant APs for positioning. In this paper, we propose a spatially localized AP selection method based on joint location information gain. In contrast to traditional AP selection methods which measure the discriminant ability of APs independently, we consider choosing APs jointly. By considering the correlation of the discriminant ability between different APs, more accurate measure of the discriminant ability of APs can be taken. Furthermore, since the optimal AP selection solution varies spatially, we incorporate a location clustering method to localize AP selection and subsequent positioning process. Finally, support vector regression (SVR) algorithm is combined to estimate the location. Experiments are carried in a realistic Wi-Fi indoor environment. Experimental results show that, by using the localized joint AP selection strategy, the proposed positioning method achieves a high-level accuracy while reducing the energy consumption on client devices significantly.

An AP handover scheme using vehicular Wi-Fi connectivity

With the rapid evolution of Wi-Fi technology and the dramatic increase in Wi-Fi coverage, it has become lot more convenient to transfer data by using roadside AP (Access Point). Previous research has proved the feasibility of using vehicular Wi-Fi infrastructure to deliver data packages. However, the issue of optimization of the AP's selection method under different mobility patterns is still open to research. To tackle this issue, this paper proposes an AP selection scheme that maximizes the potential connection time by using Received Signal Strength Indication (RSSI) to predict the duration of future connection between the specific vehicle and APs. Choosing APs with maximum connection duration guarantees the reduction of disconnection rate and packet loss, while improving the stability of the data traffic. The experimental results show that our strategy improves the average connection time and reduces the number of handovers, thereby significantly enhancing communication quality.

A Hybrid Small-Cell and Clustered Cell-Free Massive MIMO System under Mobility Scenarios

Recently,cell-free(CF)massive multipleinput multiple-output(MIMO)becomes a promising architecture for the next generation wireless communication system,where a large number of distributed access points(APs)are deployed to simultaneously serve multiple user equipments(UEs)for improved performance.Meanwhile,a clustered CF system is considered to tackle the backhaul overhead issue in the huge connection network.In this paper,taking into account the more realistic mobility scenarios,we propose a hybrid small-cell(SC)and clustered CF massive MIMO system through classifications of the UEs and APs,and constructing the corresponding pairs to run in SC or CF mode.A joint initial AP selection of this paradigm for all the UEs is firstly proposed,which is based on the statistics of estimated channel.Then,closed-form expressions of the downlink achievable rates for both the static and moving UEs are provided under Ricean fading channel and Doppler shift effect.We also develop a semi-heuristic search algorithm to deal with the AP selection for the moving UEs by maximizing the weight average achievable rate.Numerical results demonstrate the performance gains and effective rates balancing of the proposed system.