Forecast scheduling for mobile users

The Third Generation Partnership Project (3GPP) has been studying dynamic allocation of sub-frames to uplink (UL) or downlink (DL) in Time Division Duplex (TDD), called ‘Dynamic TDD,’ since the Long Term Evolution (LTE) Rel. 11 timeframe. At the same time, 3GPP is also standardizing Enhanced Local Area (eLA) small-cell heterogeneous architectures for inclusion in LTE-B (LTE Rel. 12) as a solution offering high data rate to user terminals (UEs) along with high system capacity through spatial reuse of spectrum. In this paper, we focus on a particular eLA architecture proposed by DOCOMO, called the Phantom Cell architecture, that has the option to support dynamic TDD. For an arbitrarily-located UE in an eLA cell network, we apply results from stochastic geometry to derive expressions for the distribution of DL signal to interference plus noise ratio (SINR) at an arbitrary UE and the distribution of UL SINR at its serving eLA base station (BS). We use these results to study aspects of eLA cell system design, and the sensitivity of SINR to the extent of coordination across eLA cells employing dynamic TDD.

In the current environment of burgeoning demand for mobile broadband, Long Term Evolution (LTE) is the most preferred wireless technology for the mobile operators. Considering the legacy multi-technology networks, operators need to strategically work upon maximizing their revenue while minimizing their cost and satisfying the subscriber’s requirements as well. Operators have a choice between 2 network strategies- LTE overlay and single radio access network (SRAN). A survey carried out proved SRAN to be the optimum LTE deployment strategy considering the total cost of ownership (TCO). This paper discusses the effect on the system performance due to the up gradation of network from second generation (2G) to third generation (3G) to long term evolution (LTE) systems using Nokia Siemens Network Flexi multi-radio base stations. Measurements of RSRP (Reference Signal Received Power), SINR (Signal to Interference plus Noise Ratio) and throughput were carried out by a drive test within 2Km of the deployed site area which indicated that all the 3 parameters were affected as the user equipment moved away from the site area and towards the cell edges. This problem can be addressed by deployment of low power relay nodes, one of the promising techniques, for increasing the coverage of LTE advanced networks (the future of LTE systems).

This paper presents a performance evaluation of the Radio Frequency (RF) fingerprinting framework for positioning using Minimization of Drive Testing (MDT) measurements specified in LTE (Long Term Evolution) Release 10. Methods based on Mahalanobis distance and Kullback-Leibler divergence are used for estimating the geographical locations. Performance evaluation of the framework is verified with system simulator by generating MDT measurement data in intra- and inter-frequency LTE network deployments with rural and urban configurations. The focus of the research is to understand how RF fingerprint positioning accuracy changes when taking into account the prospects and constraints of the RF fingerprints constructed from the MDT measurement data. The results of the study indicate that the performances of Mahalanobis distance and Kullback-Leibler divergence based methods are quite similar in rural deployments. However, in denser network deployments, Kullback-Leibler divergence based method tends to be much better for both intra- and inter-frequency environments, resulting in better than 21 meters and 38 meters positioning accuracies for 68 percent of MDT samples in studied urban scenarios, being well below the E911 emergency positioning requirements.

3GPP Long Term Evolution (LTE) is a standard for wireless communications to achieve high spectral efficiency, high peak data rates, as well as flexibility in frequency and bandwidth. By using frequency reuse of one in LTE networks, high spectral efficiency is achieved. However, the Inter-Cell Interference (ICI) resulted from the frequency reuse of one is a main limitation in these networks. Coordinated Multipoint Transmission (CoMP) has been proposed as an Inter-Cell Interference Coordination (ICIC) technique in Orthogonal Frequency Division Multiple Access (OFDMA) based LTE networks. CoMP forms a cluster of cooperating base stations that jointly transmit data to users within it. This paper focuses on evaluating CoMP method and compare results with the conventional method. A broad comparison among is performed by using a proposed mechanism which depends on Monte Carlo simulations considering performance metrics such as Signal to Interference plus Noise Ratio (SINR), capacity and throughput. Simulation results show that, when CoMP is used, the throughout has generally increased with significant increase at the cell-edge.

Recent advances in cognitive radio have identified the small-cells among the most promising future wireless networking scenarios. Utilizing radio context information, smallcells should perform the most optimal radio resource management (RRM) to maximize performances and minimize inter-cell interference. Radio Environmental Maps (REM) data: empirical propagation models, active transmitters’ locations, upto-date interference levels, statistical channels occupancies, are especially beneficial in these scenarios. The proposed demonstration aims to showcase the benefits of using REM information in the small-cell optimization. The demonstration utilizes a modular/flexible REM prototype, performing a realtime REM data acquisition, processing and inference as input to an enhanced small-cell optimization.

The aim of this paper is to evaluate the impact of a novel method for advanced antenna technologies for the downlink performance of the 3GPP long term evolution (LTE) networks. The network performance impact of 3 times 2 vertical sectorization in different macro-cellular network scenarios for various combinations of antenna parameter configurations have been studied in terms of signal to interference plus noise ratio (SINR) and throughput performance. Simulation studies are performed for uniform UE distribution and full-buffer traffic by using LTE snap-shot simulator and three dimensional propagation modeling.

The knowledge of the statistical characterization of the primary user activity can make more efficient the spectrum selection techniques, which are a key technology to improve the Cognitive Radio (CR) systems performance. In order to provide this kind of primary user information, Radio Environment Maps (REM) data bases has been proposed. This paper presents a REM-based Real Time Testbed designed to validate and exploit the dynamicity of the REM data base to improve the performances of the Radio Resource Management (RRM) algorithms in the context of cognitive radio. The implemented testbed is a flexible HW/SW tool able to use spectrum measurements in licensed bands and validate the Cognitive Radio performance of the secondary users, without causing any interference on these primary users. In particular, the RRM strategy under study in the testbed envisages the optimization of the spectrum selection policy when secondary users, with different service profiles, access opportunistically a set of channels during the inactivity periods of primary users. The selection is done taking as input the statistical characterization of the primary user activity, which will be stored in the REM data base, and the secondary user service profiles.

Cognitive Radio (CR) is considered as a promising solution to improve wireless spectrum utilization. Moreover, through optimization frameworks, Radio Resource Management (RRM) procedures are designed to enhance the efficient utilization of resources in CR networks. To face this issue in a holistic perspective, the RRM solutions should take into account not only the optimization task but also their impact on the design task. Thus, in this paper, a system architecture of a novel RRM is proposed for CR networks. In particular, it is fundamental to construct flexible systems to work across heterogeneous systems and help realize their full potential. Therefore, in the proposed system, heterogeneous Primary Users (PUs) with multiple features and variable CR demands are considered. A Radio Environment Map (REM) is used to obtain the required PU features, which are exploited to improve the adaptability in CR networks and, thus, to design an efficient Cognitive RRM. The functionalities of the network elements are described in detail, along with the message exchange to carry out the resource management.

As a supplement of mobile communication in Fifth generation mobile networks (5G), Unmanned Aerial Vehicles (UAVs) are playing an important role in user coverage. When base stations (BSs) are not able to serve all users in hot spots, deployable UAVs are required to mitigate the burden of BSs and maximize the access number of users. Besides, thanks to the controllable mobility, UAVs can provide better coverage for the mobile users with high velocity or fixed direction. Therefore, it is important to solve the problem of the trajectory planing of multiple UAVs. In this paper, we propose a UAV trajectory optimization strategy to maximize the access number of moving users. Firstly, according to the mobility of UAVs and the Signal to Interference plus Noise Ratio (SINR) requirements of users, we formulate the UAV's trajectory optimization problem, which is an atypical mixed integer linear programming problem (MILP). Then, a convex optimization problem is obtained by correlation relaxation of the previous atypical MILP. Finally, we propose a practicable trajectory optimization algorithm with limited times of iteration. The simulation results show that the proposed algorithm can maximize the access number of users while maintaining enough SINR.

Signal to interference plus noise ratio (SINR) based call admission control (CAC) scheme admits incoming calls as long as the measured SINR is higher than threshold value (SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> ). If a high value of SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> is chosen, the signal quality will improve but this will increase the blocking probability (p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> ), and as a result, will decrease the network utilization. On the other hand, a low value of SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> will reduce p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> , however degrades the signal quality and increases outage probability (p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> ). Hence, how to choose appropriate SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> is very important. In this paper, we focus on the following aspects. Firstly, an accurate upper bound of SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> is derived. Secondly, based on the upper bound of SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> and a lower bound from other literature, we design a fuzzy logic controller (FLC), which can adaptively adjust SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> according to the number of active users (AUs) in a cell and average arriving traffic intensity (AATI) in Erlang per cell. Finally, network performance based on FLC is compared with that based on optimal fixed SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> . Numerical results show that the proposed scheme based on FLC has better performance than the optimal fixed threshold CAC scheme.

Transmission of the multimedia video streaming has been an important topic in wireless communications. As a metric of video quality at the application layer, peak signal-to-noise ratio (PSNR) is affected by the capacity at the physical layer which can be improved by power controlling and precoding. This paper investigates an optimal power allocation scheme which can maximize the sum PSNR of all users in an SLNR-precoding-based multiuser multiple input single output (MISO) system. The power allocation problem can be transformed into a convex optimization problem when the system works in the high signal to interference plus noise ratio (SINR) region. Performance of the proposed power allocation scheme is evaluated through computer simulation. Results show that, when the system works in the high SINR region, performance improvement in terms of sum PSNR through power allocation is neglectable while the effects of different precoding schemes on the sum PSNR performance are obvious.

The unprecedented growth in cellular traffic forced researchers towards extending the Long Term Evolution (LTE) cellular network to unlicensed bands, which is known as U-LTE. However, this extension is by no means straightforward, because centralized control impeded in traditional LTE BSs challenges the performance and radio resources management of today's cellular networks. To cope with the increasing scarcity of today's cellular architecture and radio resources management, for the first time, we propose Break-Even Point (BEP) as a novel approach for solving the radio resources management problem. In this case, the BEP is modelling where the total remaining throughput of Wi-Fi or Small cell Programmable Base Station (SPBS) equals to the total consuming throughput by Users Equipment (UEs). Therefore, we can identify if Wi-Fi and Femtocell or SPBS are working above or below BEP. By using proposed BEP scheme, we guarantee cellular network utilizations with optimal precision, as well as, performance evaluation indicates that optimal spectrum access and fair distribution by using BEP scheme.

The aim of this paper is to present a non‐linear statistical model to fit and forecast the signal‐to‐interference plus noise ratio (SINR) in two‐tier heterogeneous cellular networks which consist of macrocells and femtocells. Since in these networks the number and locations of femtocell base stations (FBS) are variable, SINR forecasting can be useful in some areas such as power control and handover management. So far, linear autoregressive (AR) models have commonly been used in forecasting the received signal strength (rss) in macrocellular networks. However, AR modelling results in high mean square error (MSE) when data are non‐linear. This paper focuses on SINR which takes into account signal strength, interference and noise effects. Moreover, macro‐femto cellular network is considered. The F ‐test results show that the SINR data are non‐linear, leading to use non‐linear models instead of AR model. A non‐linear logistic smooth threshold AR (LSTAR) model is utilised to model and forecast the SINR data. Kolmogorov–Smirnov (K‐S) test demonstrates that LSTAR provides good fitness to the SINR samples. The results indicate that LSTAR model achieves much better performance in modelling and forecasting of SINR data than the AR model.

As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies.

Long Term Evolution (LTE) represents the fourth generation (4G) technology which is capable of providing high data rates as well as support of high speed mobility. The EU FP7 Mobile Cloud Networking (MCN) project integrates the use of cloud computing concepts in LTE mobile networks in order to increase LTE's performance. In this way a shared distributed virtualized LTE mobile network is built that can optimize the utilization of virtualized computing, storage and network resources and minimize communication delays. Two important features that can be used in such a virtualized system to improve its performance are the user mobility and bandwidth prediction. This paper introduces the architecture and challenges that are associated with user mobility and bandwidth prediction approaches in virtualized LTE systems.