Reproducible LTE uplink performance analysis using precomputed interference signals

The evolving small-cell (originally femtocell) networks are envisioned to provide improved capacity and coverage in next generation cellular systems. Small-cell deployments pose many challenges among which radio resource management (i.e., interference management, resource allocation, and power control) is the most significant one. The aim of this talk is to provide an overview of the radio resource management problem in small-cell networks considering OFDMA (e.g., LTE, WiMAX) femtocells, and the ongoing research on this topic. After a brief overview of the small-cell networks, we outline the major challenges in successful deployment of small-cells in next generation cellular wireless systems. In particular, the challenges related to resource allocation, co-tier/cross-tier interference management and power control are discussed. Then we provide a comprehensive overview of the different proposed techniques for resource allocation and interference management in small-cell networks. In this context, two different approaches are described: one based on clustering and semi-centralized computations, and one based on cooperative game theory. The requirements for strategic interference management via power control and resource allocation mechanisms are then discussed.

The opportunistic beamforming (OB) technique in multicellular OFDMA networks is investigated in this paper. For this reason, three cross-layer radio resource management (RRM) algorithms for OFDMA operational scenarios that are based on typical resource allocation targets such as minimization of transmit power, maximization of throughput and interference averaging are considered. The OB concept is combined with the considered RRM algorithms and a comparative performance analysis between the two types of resulted networks (omni and OB) is performed in terms of throughput, blocking probability and fairness. The simulation results suggest that OB is preferred for OFDMA systems with opportunistic rate adaptation rather than power controlled systems which offer QoS provision. According to the presented results, OB provides to OFDMA systems with opportunistic rate adaptation a ~13% throughput gain and ~75% gain in terms of blocking probability. In addition, it is shown that the combination of OB with interference averaging RRM algorithms has a minor beneficial impact only on the system fairness.

Network quality of experience (QoE) metrics are proposed in order to capture the overall performance of radio resource management (RRM) algorithms in terms of video quality perceived by the end users. Metrics corresponding to average, geometric mean, and minimum QoE in the network are measured when Max C/I, proportional fair, and Max-Min RRM algorithms are implemented in the network. The objective is to ensure a fair QoE for all users in the network. In our study, we investigate both the uplink (UL) and downlink (DL) directions, and we consider the use of distributed antenna systems (DASs) to enhance the performance. The performance of the various RRM methods in terms of the proposed network QoE metrics is studied in scenarios with and without DAS deployments. Results show that a combination of DAS and fair RRM algorithms can lead to significant and fair QoE enhancements for all the users in the network.

In this paper, radio resource management (RRM) in LTE femtocell networks is investigated in an integrated wired/wireless environment. In such a scenario, femtocell access points (FAPs) are assumed connected via wired links to a central controller within a certain vicinity (e.g. building, compound, hotel, campus, etc.). Consequently, it becomes possible to perform RRM in a centralized and controlled way in order to enhance the quality of service (QoS) performance for the users in the network. A utility maximization framework is presented, and an RRM algorithm that can be used to maximize various utility functions is proposed. The performance tradeoffs between various utility functions are studied and assessed. Furthermore, the joint wired/wireless RRM approach is compared to the distributed RRM case, where each FAP acts as an independent wireless network without central control. Simulation results show that the integrated wired/wireless approach leads to significant gains compared to the wireless only case.KeywordsRadio resource managementfemtocellLTEproportional fairwired/wireless networks

Initially deployed cellular systems were analog and constituted the first generation cellular systems. Growth of voice subscribers led to high capacity digital systems called second generation (2G) systems. Examples of 2G systems are narrowband CDMA-based IS-95 systems and TDMA-based GSM systems. Demand for increased voice capacity and expected growth of data subscribers are now driving the development of emerging third generation (3G) and third-and-a-half generation (3.5G) systems. Two dominant 3G cellular standards are Interim Standard-2000 (IS-2000) and UMTS (Universal Mobile Telephone System). Radio interface related features of these standards along with RRM aspects of these systems are the focus of this chapter. A standard to specifically address the wireless data market has been developed and is called 1xEvolution-Data Only (1xEV-DO). The 1xEV-DO standard (also referred to as IS-856) is considered a 3.5G system and represents the first phase in the evolution of IS-2000. The second phase of the evolution of IS-2000 is 1xEvolution-Data and Voice (1xEV-DV). Prominent features and distinct RRM requirements of the 1xEV-DO are highlighted. Methodology for fuzzy logic based and neural network based handoff algorithms developed in this book is viewed from the perspective of RRM.

Next generation wireless network (NGWN) will be heterogeneous where different radio access technologies (RATs) coexist. In NGWN, efficient radio resource management and QoS provisioning are major challenges. We propose an optimal joint radio resource management algorithm to minimize call blocking probability in NGWN. The algorithm makes call admission decisions such that overall call blocking probability is minimized in the heterogeneous wireless network. We develop an analytical model for the radio resource management algorithm using a multi-dimensional Markov chain. Based on linear programming optimization technique, we determine the optimal splitting of arriving calls among available RATs. Results show that the proposed algorithm outperforms other algorithm in the heterogeneous wireless network.

In the fifth generation (5G) of mobile broadband systems, radio resource management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture consists of a learner (or a few), which learns RRM policies directly from the data gathered in the network using a single general-purpose learning framework, and a set of distributed actors, which execute RRM policies issued by the learner and repeatedly generate samples of experience. Thus, the complexity of RRM is shifted to the design of the learning framework, while the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios, and future directions on applications of machine learning to RRM are discussed. Although we focus on a mobile broadband context, the concepts proposed hereafter extend to any radio access network technology where one can conceive the idea of a central learning unit gathering data from distributed actors.

We propose and evaluate the performance of an integrated quality-of-service (QoS) aware radio resource management (RRM) framework for LTE uplink access. Unlike related work, our integrated RRM framework jointly takes into account 1) the class of service requirements of the different connections, 2) the constraints on the contiguity in resource block allocation due to the single-carrier frequency division multiple access (SC-FDMA) mechanism adopted for the LTE uplink, and 3) the selection of the appropriate modulation and coding scheme with full integration with the LTE uplink closed-loop fractional power control for proper power allocation. The proposed RRM framework uses closed loop power control and interference limit per cell to provide an autonomous inter-cell interference coordination scheme that is applied locally without the need of exchanging interference related information between neighboring cells. Simulation results show the ability of the proposed framework to meet the QoS of a highly loaded system with four classes of service. The interference-aware scheme also manages to limit interference and provides fair resource sharing between cell-edge and cell-center users.

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.

In this paper an innovative Radio Resource Management (RRM) algorithm is proposed with the purpose of increasing High Speed Packet Access (HSPA) performances, in terms of system capacity and service quality, when the Multimedia Broadcast Multicast Services (MBMS) is supplied. The proposed RRM algorithm exploits channel quality indications to set up point-to-multipoint connections to subgroups of multicast users and to select the proper modulation and coding schemes on the downlink. The number of subgroups is determined through an optimization technique that also takes into account the user satisfaction. An exhaustive simulation campaign is conducted to compare the proposed algorithm with the most promising approaches in the literature. Comparisons aim to assess the capability of the proposed RRM algorithm to efficiently manage group oriented services by providing an increment in terms of user satisfaction.

To a wireless user, dropped calls, blocked calls, and calls riddled with static are unacceptable. Users expect to remain connected while traveling through cells of a service area. Users also expect to be able to make calls at any time and to have good, clear connections. These user demands are addressed through the management of radio resources. Handoff is an important aspect of the radio resource management. Handoff makes a continuous connection possible by transferring a mobile user from one cell to another. Handoff also determines how many calls can be served in a given area and the quality perceived by users. Thus, efficient handoff algorithms are essential for preserving the capacity and quality of service of wireless communication systems.

All cellular radio systems have radio resource management algorithms which rely on some sort of resource quantity. In the uplink of a WCDMA system, a natural choice of such a quantity is the uplink noise rise, i.e., total received power over noise power. Unfortunately this quantity is hard to measure. In this paper, we propose and evaluate four different noise rise estimates. The best performing estimate provides an average error of less than 1 dB for practical load levels. Due to low standard deviation of single estimates it is possible to apply a simple error correction algorithm.

The technological evolution of the wireless cellular systems has been very rapid in last two decades. In the coming decade of “converging wireless networks/systems/ecosystems”, there is an increasing demand on achieving very high data rates ubiquitously even with high mobile speeds as if we connected to a wired ADSL! Radio Resource Management (RRM) for the emerging wireless systems will be the key mechanism for achieving such high data rates. Indeed, RRM has already been a hot research area in both academia and industry for decades. And due to the complexity of the emerging wireless systems, an interdisciplinary approach and/or methodology is needed to tackle the new RRM challenges. In this paper, we provide a control engineering view onto some of the RRM challenges in emerging wireless networks, with a special emphasis on distributed power control. For example, we establish a link between power control design and dynamic neural networks, two different areas whose scope of interest, motivations and settings are completely different. Here, we emphasize the importance and the need of interdisciplinary approach. Some subjects to be addressed within the paper shall include future-generation cellular/“noncellular” systems, radio resource management challenges, energy efficiency and distributed power control algorithms, variable-structure-systems based power control, channel/frequency allocation, spectral-clustering based channel allocation, Hopfield neural networks.

The interface management and resource allocation in backhaul (BH)/access networks for the cloud/centralized radio access networks (C-RAN) are two of the largest challenges to enable the C-RAN architecture deployment successfully in cellular wireless systems. From the PHY perspectives, one major advantage of C-RAN is the ease of implementation of multicell coordination mechanisms to manage the interference and improve the system spectral efficiency (SE). Theoretically, a large number of cooperative cells leads to a higher SE; however, it may also cause significant delay due to extra channel state information feedback and joint processing computational needs at the cloud data center, which is likely to result in performance degradation. In order to investigate the delay impact on the throughput gains, we divide the network into multiple clusters of cooperative small cells and formulate a throughput optimization problem. We model various delay factors and the sum-rate of the network as a function of cluster size, treating it as the main optimization variable. For our analysis, we consider both base stations' as well as users' geometric locations as random variables for both linear and planar network deployments. The output signal-to-interference-plus-noise ratio and ergodic sum-rate are derived based on the homogenous Poisson-point-processing model. The sum-rate optimization problem in terms of the cluster size is formulated and solved. From the radio resource management (RRM) perspective, we consider the problem of joint BH and access links optimization in dense small-cell networks with special focus on time division duplexing mode of operation in BH and access links transmission. Here, we propose a framework for joint RRM where we systematically decompose the problem in BH and access links. To simplify the analysis, the procedure is tackled in two stages. At the first stage, the joint optimization problem is formulated for a point-to-point scenario where each small cell is simply associated to a single user. In the second stage, the problem is generalized for multiaccess small cells. In addition, the chapter addressed thejoint routing and BH scheduling in a dense small-cell networks using 60 GHz multihop BH, coordinated by a local C-RAN central unit. The problem is formulated as a generalized vehicle routing problem and decoupled into two subproblems, channel-aware path selection and queue-aware link scheduling.

Wireless local area networks (WLANs) based on IEEE 802.11 standard have been rapidly growing. With limitations of the standard and rapid increase in wireless application demand, the air interface acts as bottleneck even in high-speed WLAN standards such as IEEE 802.11a and 802.11g with expected data transmission rates of up to 54 Mbps. To improve the overall performance of IEEE 802.11 WLAN under large deployment and heavy application demand environment, radio resource management (RRM) algorithms based on power control have been investigated and tested through simulation. The results show that controlling the wireless terminal's (WT) transmitter power to an optimum power level is important. This results into an increased data throughput in a WLAN network and when the transmitter power level of a WT is increased beyond the optimum power level, the throughputs drop drastically no matter how high the WT's transmitter power may be increased.