Indoor 3D Dynamic Reconstruction Fingerprint Matching Algorithm in 5G Ultra-Dense Network

For an arbitrarily-located user terminal (UE) in a multi-tier heterogeneous cellular wireless network, the joint distribution of the downlink SINR at the UE from the candidate serving base stations (BSs) in the accessible tiers of the network has been derived in closed form for the cases where the candidate serving BS from each accessible tier is chosen as either the one nearest to the UE [1] or the one that is received strongest (equivalently, with maximum SINR) at the UE [2], when the locations of the BSs in the tiers are modeled by independent Poisson Point Processes, and the fading on all links is assumed independent identically distributed (iid) and Rayleigh. The actual serving BS for the UE is chosen as the nearest/strongest/max-SINR candidate serving BS after imposing selection bias across the tiers. The above joint distributions can be used to yield the distribution of the actual SINR at the UE (i.e., when receiving from this serving BS) when no selection bias exists across tiers. However, for the practically important case of selection bias, analytical calculation of the distribution of the actual SINR presents significant challenges. This work derives and summarizes the distribution of actual downlink SINR for all the above criteria for selection of the serving BS accounting for selection bias. We then explore some implications of these results for design and operation of a heterogeneous network.

Handover optimization is problematic in ultra-dense 5G networks because of the extremely high number of users and base stations. Unlike other methods, classical optimization approaches are not suitable for such technologies due to their complexity. In this context, a Machine Learning (ML)-based architecture using Deep Neural Networks (DNN) and Reinforcement Learning is proposed for handover and network optimization. The DNN model considers the temporal changes in the state of the network, and reinforcement learning is responsible for policy-based decision-making for proactive intervention, adaptive-driven interference suppression, and spectral efficiency improvement. These findings support the feasibility of the proposed resource allocation technique using ML in wireless communication systems.

Market-based Short-Term Allocations in Small Cell Wireless Networks

Device positioning has generally been recognized as an important service of LTE Advanced pro and the upcoming fifth generation (5G) mobile communication. The Observed Time Difference of Arrival (OTDoA) technique that utilizes cellular signals for positioning is considered as a promising candidate technology capable of meeting users' growing demands for high-precision and ubiquitous device positioning. The application of ultra-dense network (UDN) in 5G systems brings new opportunities and challenges to device positioning. However, existing researches mainly focus on the communication characteristics of UDN, while its positioning characteristics have not been analyzed thoroughly. In this study, a software simulation platform of OTDoA positioning in 5G UDN is first established based on the current 5G standards. Then, the performance of OTDoA technique in UDN and its influencing factors are comprehensively analyzed by heat map method over our simulation platform. The simulation results demonstrate that in 5G UDN, the unsatisfactory geometric distribution quality of base stations used for positioning and the non-line-of-sight (NLoS) propagation of cellular signals are the main factors that cause the performance degradation of OTDoA positioning. Moreover, based on the above analysis, we also provide some meaningful guidance for the development or improvement of OTDoA positioning technique used in 5G UDN.

Cooperative Multi-Point is a technology for improved spectrum utilization where a group of neighboring base stations coordinate their transmissions. However, the performance gain is severely degraded due to out-of-group interference. We propose simple schemes for null space learning and tracking of the interference channel between a terminal and an interfering base station group, whose only required network information is the interference levels at the terminal. In the proposed schemes, the interfering base station group transmits a sequence of learning signals which inflict low interference on a terminal of a neighboring group. The schemes utilize the fact that each terminal normally measures its interference levels and feeds them back to its serving base stations. Then, the serving group distributes this information to the interfering group, from which it learns the null space of the interfering channel. The proposed algorithms do not affect the transmission protocol between the terminal and the serving base stations, and do not add any signaling to the control channel between them. Each terminal communicates only with its serving group and treats the learning signals of the interfering group as noise.

Indoor target tracking is a crucial application of indoor localization systems. Wi-Fi fingerprint-based target tracking/positioning has been extensively studied due to its deployability under pervasive indoor wireless local area networks. However, the majority of fingerprint-based schemes adopted the static radio map and did not make full use of continuous motion information of the target. Therefore, we build a ubiquitous indoor tracking/localization system that can maintain a dynamic fingerprint database at low cost in this paper. First, we propose an optimal target tracking algorithm exploiting the historical data of the target location information to help improve the accuracy, which adopts a modified particle filter algorithm to reduce the number of samples and computing overhead. Second, we present a database self-update method according to trajectory continuity, which is employed to update fingerprint database dynamically for keeping the system robust. Finally, we tested the optimal target tracking based on dynamic fingerprint algorithm (OTTDF) in a complex laboratory area with diverse target motion conditions and various obstacles, our experimental results indicate that the OTTDF scheme successfully handles complex indoor structure, including different target motion state, signal fingerprint changes caused by obstacles, simultaneously provides better performance in localization cost, and localization/tracking accuracy in indoor wireless network.

In 5G ultra-dense networks, a large number of mobile users (MUs) request a huge amount of high data rate video traffic causing a peak congestion situation at the macro base station (MBS) and small-cell base stations. This situation certainly reduces the total video capacity delivered to the MUs. In this paper, we exploit the available spectrum and storage resources of the MUs as well as the wireless broadcast nature of device-to-device (D2D) communications to propose a joint downlink resource sharing and caching helper selection (DRS-CHS) control to maximize the multicast video delivery capacity in dense D2D 5G networks. We assume that the MUs are divided into different clusters in which they can communicate with each other by D2D communications. There are two types of MUs in each cluster including the requesting users (RUs) that request the video and the caching helpers (CHs) that have cached the video. In addition, there are some sharing users (SUs) that can share their downlink resources with the CHs and the RUs for D2D multicast communications. A DRS-CHS optimization problem is then formulated and solved for an optimal control process of how to select a CH in each cluster and how to assign an SU to share its downlink resource with the selected CH such that the total video delivery capacity multicasted from the CHs to the RUs in all clusters is maximized. Simulation results demonstrate the benefits of the proposed DRS-CHS control solution compared to other conventional benchmarks.

Performing recognition tasks using latent fingerprint samples is often challenging for automated identification systems due to poor quality, distortion, and partially missing information from the input samples. We propose a direct latentfingerprint reconstruction model based on conditional generative adversarial networks $(cGANs)$.Two modifications are applied to the cGANto adapt itfor the task of latent fingerprint reconstruction. First, the model is forced to generate three additional maps to the ridge map to ensure that the orientation and frequency information are considered in the generation process, and prevent the model from filling large missing areas and generating erroneous minutiae. Second, a perceptual IDpreservation approach is developed to force the generator to preserve the IDinformation during the reconstruction process. Using a synthetically generated database of latent fingerprints, the deep network learns to predict missing information from the input latent samples. We evaluate the proposed method in combination with two different fingerprint matching algorithms on several publicly available latent fingerprint datasets. Weachieved rank-10 accuracy of 88.02% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50accuracy of 70.89% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching. Experimental results of matching reconstructed samples in both latent-to-sensor and latent-to-latent frameworks indicate that the proposed method significantly increases the matching accuracy of the fingerprint recognition systems for the latent samples.

Based on analyzing the traditional image matching algorithms, a fast image matching method, which integrates improved sequential similarity detection algorithm (SSDA) and wavelet transformation, is presented in this paper. Firstly, some basic principles of SSDA image matching and wavelet transformation are introduced. Secondly, according to the shortcoming of traditional SSDA algorithm, a new SSDA algorithm is studied for improving the determining velocity of the threshold in SSDA searching match. By integrating wavelet transformation, an integrated matching algorithm is given out. Finally, the new method is compared with the traditional matching algorithms in experiments. The experimental results show that the new method has higher efficiency and accuracy in image matching and is suitable for the realization of real-time underwater navigation.

Efficient packet scheduling in CDMA cellular networks is a challenging problem due to the time variant and stochastic nature of the channel fading process. Selection diversity is one of the most effective techniques utilizing random and independent variations of diverse channels to improve the performance of communication over fading channels. In this paper, we propose two packet scheduling schemes exploiting base station selection diversity in the downlink of CDMA cellular networks. The proposed schemes rely on the limited instantaneous channel state information (CSI) to select the best user from the best serving base station at each time slot. This technique increases the system throughput by increasing multiuser diversity gain and reducing the effective interference among adjacent base stations. Results of Monte Carlo simulations are given to demonstrate the improvement of system throughput using the proposed scheduling schemes. In addition, we investigate fairness issue of wireless scheduling schemes. Due to different characteristics of wireless scheduling schemes, the existing fairness indexes may result in misleading comparison among different schemes. We propose a new fairness index to compare the overall satisfaction of the network users for different scheduling schemes. Copyright © 2006 John Wiley & Sons, Ltd.

Cellular radio systems are often limited due to the presence of cochannel interference. Basically, radio systems may be operated by utilizing asynchronous or synchronous downlink transmission from all base stations in the system, where synchronization between terminals and their serving base station is mandatory in both concepts. We provide a comparison between the theoretical achievable spectral efficiency in an orthogonal frequency division multiple access systems using different linear equalizers and their resulting performance taking estimation errors into account. It is shown, that the choice of the appropriate receiver depends on the degree of synchronization in the system. We demonstrate that a 3G Long Term Evolution radio system may achieve higher spectral efficiency thanks to interference suppression if fully synchronized data transmission from all base stations is introduced.

In this paper, we evaluate the performance of the IEEE 802.16e sleep mode mechanism in wireless access networks. This mechanism reduces the energy consumption of a mobile station (MS) by allowing it to turn off its radio interface (sleep mode) when there is no traffic present at its serving base station (BS). After a sleep period expires, the MS briefly checks the BS for data packets and switches off for the duration of another sleep period if none are available. Specifically for IEEE 802.16e, each additional sleep period doubles in length, up to a certain maximum. Clearly, the sleep mode mechanism can extend the battery life of the MS considerably, but also increases the delay at the BS buffer. For the performance analysis, we use a discrete-time queueing model with general service times and multiple server vacations. The vacations represent the sleep periods and have a length depending on the number of preceding vacations. Unlike previous studies, we take the (short-range) traffic correlation into account by assuming a D-BMAP arrival process, i.e. the distribution of the number of packet arrivals per slot is modulated by the transitions in a Markov chain with N background states. As results, we obtain the distribution of the number of packets in the queue at various sets of time epochs, the distribution of the packet delay and the antenna activity rate. We apply these results to the IEEE 802.16e sleep mode mechanism with correlated downlink traffic. By means of some examples, we show the influence of both the configuration parameters and the traffic correlation on the delay and the energy consumption.

In this paper, we propose a cumulative distribution function (CDF)-based scheduling for multi-cell uplink networks in order to exploit multi-user diversity, while satisfying fair resource sharing among users. In the proposed scheduling, each user adjusts its transmit power to reduce the amount of generating interference to other cells, based on a pre-determined threshold. Then, each user calculates CDF of an uplink signal-to-noise ratio with the adjusted transmit power, and feeds the CDF value back to its serving base station (BS). In each time slot, the BS selects the user having the largest CDF value. The proposed scheduling operates with a distributed manner even though it effectively copes with inter-cell interference. As a main result, we prove that the proposed scheduling achieves the double-logarithmic growth of normalized user throughput which is defined as the ratio of user throughput to the probability of the user being selected. Moreover, we observe that a fixed threshold is enough to accommodate diverse network scenarios with different population sizes and user locations in the proposed scheduling. ?? 2014 IEEE.

In this letter, we propose a spectrum resource allocation algorithm in non-orthogonal multiple access heterogeneous network (NOMA-HetNet). We consider a dense two-tier HetNet consisting multiple macro base station (MBS) and multiple femto base stations (FBSs), which are very close to each other. All the MBS and FBSs are working in NOMA mode. Our hypergraph spectral clustering based method is proposed to deal with the serious cumulative interference. We aim at improving the system throughput with the prerequisite that all users' fairness in the same base station is guaranteed. We analyze the computation complexity of the algorithm. The simulation result shows that our algorithm can improve the system throughput with low computational complexity.

In order to realize high precision comparison between the time of remote user and UTC (NTSC), the National Time Service Center has built a set of high accuracy remote time common-view system based on the satellite common view time transfer method, the underlying system comprised with a base station and some calibration stations and a data processing center. Among them, the base station provides a reference measurement value for the whole system, namely the bias of UTC (NTSC) and the satellite clock. The calibration stations are used to provide a measurement value of the measured stations, namely the bias of local time of calibration station and the satellite clock. The data processing center is responsible for completing the time deviation between the local time of calibration stations and UTC (NTSC). In view of the importance of base station in the system, this paper has designed two kinds of configuration scheme, the single base station and multiple base stations. According to this a large number of tests has been analyzed. The results of experiment describe the single base station is easy to use, its timing accuracy is about 10ns, multi-base stations with higher availability and reliability, and its timing accuracy is 3 ∼ 5ns.