Cognitive radio resource management for improving the efficiency of LTE network segments

The target of 3G LTE system is to achieve high data rates with low latency and packet optimized RAT. Orthogonal Frequency Division Multiple Access (OFDM) will be adopted as the access technology for LTE [6]. OFDM provides several techniques for a) spectrum allocations to network segments (one or more cells) and b) intra-cell RRM using Dynamic Subcarrier Assignment (DSA), Adaptive Power Allocation (APA) and Adaptive Modulation (AM) algorithms [6], providing the means for a flexible RRM scheme capable to address the problems of the service area and provide solutions for proper network adaptation. In this paper, we will focus on both techniques and try to exploit the results of them towards proposing a cognitive RRM.
Index Terms—Radio Resource Management, Cognitive, Long Term Evolution, Network adaptation
I. INTRODUCTION In the era of Beyond 3rd Generation (B3G) wireless communications, several different Radio Access Technologies (RATs) will coexist. In this framework, the Long Term Evolution (LTE) system, specified by the 3rd Generation Partnership Project (3GPP) [1], will have to coexist with WiMAX [2], mobile 2G/3G networks, Wireless Local Area Networks (WLANs) [3], etc. Each system will have to satisfy rigid requirements, in order to adequately serve users and applications. At the same time, technology coexistence and user/application requirements yield a set of complex situations that should be also addressed. Thus,
Cognitive Radio Resource Management for Improving the Efficiency of LTE Network
Segments in the Wireless B3G World
SHORT PAPER
Aggelos Saatsakis, Kostas Tsagkaris, Dirk von-Hugo, Matthias Siebert, Manfred Rosenberger, Panagiotis Demestichas
Abstract—In this paper, we provide a framework for enhancing Radio Resource Management (RRM) in 3G Long Term Evolution (LTE) systems with cognitive features. By exploiting the capabilities of Orthogonal Frequency Division Multiplexing (OFDM) LTE access technology, network segments are capable of properly adapting to the environment conditions by applying RRM algorithms for optimized sub-carriers’ assignment, power allocation and adaptive modulation. On the other hand, cognitive features can be used to provide the system with knowledge that derives from past interactions with the environment. As a result, the system will be able to apply already known solutions in timely manner when identifying a problem that has been already addressed in the past. Framed within the above statements, we propose and validate a Cognitive RRM scheme in the context of LTE network segments. Indicative examples and results showcase that the proposed scheme can result in significant efficiency improvement in terms of performance and network adaptation.
Aggelos Saatsakis, Kostas Tsagkaris, Panagiotis Demestichas are with University of Piraeus, Greece, (e-mail: {asaatsak, ktsagk, pdemest} @unipi.gr).
Dirk Von-Hugo, Matthias Siebert, Manfred Rosenberger are with Deuche Telekom, Germany, (e-mail: {M.Siebert, Dirk.Hugo, Manfred.Rosenberger} @).
efficient management platforms and functionality are required in order to address the increased complexity.
A first prominent direction for achieving efficiency is to develop appropriate platforms and functionality that will attribute the managed segment with cognitive network capabilities [4]. Another measure is to develop management platforms that can exploit Radio Resource Management (RRM) schemes, in order to further increase the efficiency of the managed system. In general, cognitive systems determine their behavior, in a reactive or proactive manner, based on the external stimuli (environment aspects), as well as their goals, principles, capabilities, experience and knowledge. In the case of cognitive networks, this definition can be translated as the ability to dynamically select the network’s configuration, through self-management functionality that takes into account the context of operation (environment requirements and characteristics), goals and policies (corresponding to principles), profiles (capabilities), and machine learning [5], (for representing and managing knowledge and experience). The self-management functionalities of cognitive systems may be introduced at the level of the terminal, access point or network segment (set of access points). This paper proposes an enhancement of the RRM mechanism of LTE system with cognitive capabilities (Cognitive RRM). In particular, our work presents an approach for the identification of whether the context currently encountered has also been addressed in the past. This paves the way for proper exploitation of experience and knowledge that can be used to produce wiser RRM decisions and actions.