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Abstract-This letter proposes a novel graph-based multi-cell scheduling framework to efficiently mitigate downlink inter-cell interference in small cell OFDMA networks. This framework incorporates dynamic clustering combined with channel-aware resource allocation to provide tunable quality of service measures at different levels. Our extensive evaluation study shows that a significant improvement over the state-of-the-art benchmarks can be achieved in terms of user spectral efficiency in return for reuse factor level via tuning the proposed Quality of Service (QoS) measures.

Millimeter wave (mmWave) technologies can enable current mobile communication systems to achieve higher data rates. However, wireless channels at mmWave frequencies experience higher isotropic path loss. Therefore, employing a suitable beamforming algorithm is an indispensable element of any mmWave system. Traditional multiple-input multipleoutput (MIMO) systems employ digital beamforming where each antenna element is equipped with one RF chain. In case of mmWave systems, however, the power consumption, signalling and hardware cost impose the designers to deploy analog or hybrid beamforming strategies. This paper addresses two key problems in beamforming for millimeter wave communication systems. First, an effective codebook is designed, using a genetic algorithm that achieves a near-optimal array gain in all directions. This RF codebook is shown to perform better compared to the state-of-the-art RF codebooks with fewer RF chains and lower resolution phase shifters. Secondly, a low complexity channel estimation scheme is proposed that requires less signalling overhead and is effective with low-resolution phase shifters. Finally, the performance of the proposed RF codebook and channel estimation scheme is thoroughly investigated in terms of spectral efficiency.

Cross-layer scheduling is a promising solution for improving the efficiency of emerging broadband wireless systems. In this tutorial, various cross-layer design approaches are organized into three main categories namely air interface-centric, user-centric and route-centric and the general characteristics of each are discussed. Thereafter, by focusing on the air interfacecentric approach, it is shown that the resource allocation problem can be formulated as an optimization problem with a certain objective function and some particular constraints. This is illustrated with the aid of a customer-provider model from the field of economics. Furthermore, the possible future evolution of scheduling techniques is described based on the characteristics of traffic and air interface in emerging broadband wireless systems. Finally, some further challenges are identified. © 2009 IEEE

Data analytics can be seen as a powerful tool for the fifth-generation (5G) communication system to enable the transformation of the envisioned challenging 5G features into a reality. In the current 5G architecture, some first features toward this direction have been adopted by introducing new functions in core and management domains that can either run analytics on collected communication-related data or can enhance the already supported network functions with statistics collection and prediction capabilities. However, possible further enhancements on 5G architecture may be required, which strongly depend on the requirements as set by vertical customers and the network capabilities as offered by the operator. In addition, the architecture needs to be flexible in order to deal with network changes and service adaptations as requested by verticals. This paper explicitly describes the requirements for deploying data analytics in a 5G system and subsequently presents the current status of standardization activities. The main contribution of this paper is the investigation and design of an integrated data analytics framework as a key enabling technology for the service-based architectures (SBAs). This framework introduces new functional entities for application-level, data network, and access-related analytics to be integrated into the already existing analytics functionalities and examines their interactions in a service-oriented manner. Finally, to demonstrate predictive radio resource management, we showcase a particular implementation for application and radio access network analytics, based on a novel database for collecting and analyzing radio measurements. INDEX TERMS 5G, architecture, data analytics, network slicing. I. INTRODUCTION The fifth generation (5G) mobile communications system is characterized by a wide-range of services grouped under three generic service types, namely, enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (URLLC). The network slicing concept is introduced in 5G to address the various requirements from multiple vertical industries assuming a shared physical network infrastructure. A network slice can be customized according to the needs of vertical industries and services to be supported. Network slicing is a key pillar in 5G networks. The associate editor coordinating the review of this manuscript and approving it for publication was Kostas Psannis. The end-to-end (E2E) nature imposes domain-specific requirements that will span over multiple technical domains, i.e., radio access network (RAN), transport network, and core network (CN). In addition, 5G shall be supported by a management and orchestration (M&O) layer in order to meet defined service-level agreements (SLAs) for network slices of different nature. In 3rd generation partnership project (3GPP), four standard slice/service types (SSTs) have been introduced, namely, eMBB, mMTC, URLLC, and vehicleto-everything (V2X) SSTs, which aim to provide differen...