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IEEE Open Journal of the Communications Society
Number of Followers: 2  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2644-125X
Published by IEEE Homepage  [228 journals]
  • Terahertz Band: The Last Piece of RF Spectrum Puzzle for Communication

    • Authors: Hadeel Elayan;Osama Amin;Basem Shihada;Raed M. Shubair;Mohamed-Slim Alouini;
      Pages: 1 - 32
      Abstract: Ultra-high bandwidth, negligible latency and seamless communication are envisioned as milestones that will revolutionize the way by which societies create, distribute and consume information. The remarkable expansion of wireless data traffic has advocated the investigation of suitable regimes in the radio spectrum to satisfy users’ escalating requirements and allow the exploitation of massive capacity and massive connectivity. To this end, the Terahertz (THz) frequency band (0.1-10 THz) has received noticeable attention in the research community as an ideal choice for scenarios involving high-speed transmission. As such, in this work, we present an up-to-date review paper to analyze key concepts associated with the THz system architecture. THz generation methods are first addressed by highlighting the recent progress in the devices technology. Moreover, the recently proposed channel models available for propagation at THz band frequencies are introduced. A comprehensive comparison is then presented between the THz wireless communication and its other contenders. In addition, several applications of THz communication are discussed taking into account various scales. Further, we highlight the milestones achieved regarding THz standardization activities. Finally, a future outlook is provided by presenting and envisaging several potential use cases and attempts to guide the deployment of the THz frequency band.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • IEEE Open Journal of the Communications Society Instructions for Authors

    • Pages: 1 - 1
      Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • Robust Beamforming Design for OTFS-NOMA

    • Authors: Zhiguo Ding;
      Pages: 33 - 40
      Abstract: This paper considers the design of beamforming for orthogonal time frequency space modulation assisted non-orthogonal multiple access (OTFS-NOMA) networks, in which a high-mobility user is sharing the spectrum with multiple low-mobility NOMA users. In particular, the beamforming design is formulated as an optimization problem whose objective is to maximize the low-mobility NOMA users' data rates while guaranteeing that the high-mobility user's targeted data rate can be met. Both the cases with and without channel state information errors are considered, where low-complexity solutions are developed by applying successive convex approximation and semidefinite relaxation. Simulation results are also provided to show that the use of the proposed beamforming schemes can yield a significant performance gain over random beamforming.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • Energy Efficient User Association, Power, and Flow Control in Millimeter
           Wave Backhaul Heterogeneous Networks

    • Authors: Sylvester Aboagye;Ahmed Ibrahim;Telex M. N. Ngatched;
      Pages: 41 - 59
      Abstract: This paper studies the problem of energy efficiency (EE) maximization via user association, power, and backhaul (BH) flow control in the downlink of millimeter wave BH heterogeneous networks. This problem is mathematically formulated as a mixed-integer non-linear program, which is non-convex. To get a tractable solution, the initial problem is separated into two sub-problems and optimized sequentially. The first is a joint user association and power control sub-problem for the access network (AN) (AN sub-problem). The second is a joint flow and power control sub-problem for the BH network (BH sub-problem). While the BH sub-problem is a convex optimization problem and hence can be efficiently solved, the AN sub-problem assumes the form of a generalized assignment problem, which is known to be NP-hard. To that end, we utilize Lagrangian decomposition to propose two polynomial time solution techniques that obtain a high-quality solution for the AN sub-problem. The first, referred to as Technique A, uses dynamic programming, the subgradient method, and a heuristic. The second, named Technique B, uses the multiplier adjustment method, the sorting algorithm, and a heuristic. Simulation results are used to demonstrate the effectiveness of the proposed energy efficient user association, power, and BH flow control algorithms as compared with benchmark user association schemes that incorporate the BH sub-problem algorithm, in terms of the total AN power, BH power, and overall network (AN plus BH) EE. The computational complexity and practical implementation of the proposed algorithms are discussed.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • Detection and Classification of UAVs Using RF Fingerprints in the Presence
           of Wi-Fi and Bluetooth Interference

    • Authors: Martins Ezuma;Fatih Erden;Chethan Kumar Anjinappa;Ozgur Ozdemir;Ismail Guvenc;
      Pages: 60 - 76
      Abstract: This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system. The system uses a multistage detector to distinguish signals transmitted by a UAV controller from the background noise and interference signals. First, RF signals from any source are detected using a Markov models-based naïve Bayes decision mechanism. When the receiver operates at a signal-to-noise ratio (SNR) of 10 dB, and the threshold, which defines the states of the models, is set at a level 3.5 times the standard deviation of the preprocessed noise data, a detection accuracy of 99.8% with a false alarm rate of 2.8% is achieved. Second, signals from Wi-Fi and Bluetooth emitters, if present, are detected based on the bandwidth and modulation features of the detected RF signal. Once the input signal is identified as a UAV controller signal, it is classified using machine learning (ML) techniques. Fifteen statistical features extracted from the energy transients of the UAV controller signals are fed to neighborhood component analysis (NCA), and the three most significant features are selected. The performance of the NCA and five different ML classifiers are studied for 15 different types of UAV controllers. A classification accuracy of 98.13% is achieved by k-nearest neighbor classifier at 25 dB SNR. Classification performance is also investigated at different SNR levels and for a set of 17 UAV controllers which includes two pairs from the same UAV controller models.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • Hybrid Beamforming for 5G and Beyond Millimeter-Wave Systems: A Holistic

    • Authors: Jun Zhang;Xianghao Yu;Khaled B. Letaief;
      Pages: 77 - 91
      Abstract: Millimeter-wave (mm-wave) communication is a key technology for future wireless networks. To combat significant path loss and exploit the abundant mm-wave spectrum, effective beamforming is crucial. Nevertheless, conventional fully digital beamforming techniques are inapplicable, as they demand a separate radio frequency (RF) chain for each antenna element, which is costly and consumes too much energy. Hybrid beamforming is a cost-effective alternative, which can significantly reduce the hardware cost and power consumption by employing a small number of RF chains. This paper presents a holistic view on hybrid beamforming for 5G and beyond mm-wave systems, based on a new taxonomy for different hardware structures. We take a pragmatic approach and compare different proposals from three key aspects: 1) hardware efficiency, i.e., the required hardware components; 2) computational efficiency of the associated beamforming algorithm; and 3) achievable spectral efficiency, a main performance indicator. Through systematic comparisons, the interplay and trade-off among these three design aspects are demonstrated, and promising candidates for hybrid beamforming in future wireless networks are identified.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • Multigroup Multicast Precoding for Energy Optimization in SWIPT Systems
           With Heterogeneous Users

    • Authors: Sumit Gautam;Eva Lagunas;Ashok Bandi;Symeon Chatzinotas;Shree Krishna Sharma;Thang X. Vu;Steven Kisseleff;Björn Ottersten;
      Pages: 92 - 108
      Abstract: The key to developing future generations of wireless communication systems lies in the expansion of extant methodologies, which ensures the coexistence of a variety of devices within a system. In this paper, we assume several multicasting (MC) groups comprising three types of heterogeneous users including Information Decoding (ID), Energy Harvesting (EH) and both ID and EH. We present a novel framework to investigate the multi-group (MG) - MC precoder designs for three different scenarios, namely, Separate Multicast and Energy Precoding Design (SMEP), Joint Multicast and Energy Precoding Design (JMEP), and Per-User Information and/or Energy Precoding Design (PIEP). In the considered system, a multi-antenna source transmits the relevant information and/or energy to the groups of corresponding receivers using more than one MC streams. The data processing users employ the conventional ID receiver architectures, the EH users make use of a non-linear EH module for energy acquisition, while the users capable of performing both ID and EH utilize the separated architecture with disparate ID and non-linear EH units. Our contribution is threefold. Firstly, we propose an optimization framework to i) minimize the total transmit power and ii) to maximize the sum harvested energy, the two key performance metrics of MG-MC systems. The proposed framework allows the analysis of the system under arbitrary given quality of service and harvested energy requirements. Secondly, to deal with the non-convexity of the formulated problems, we transform the original problems respectively into equivalent forms, which can be effectively solved by semi-definite relaxation (SDR) and alternating optimization. The convergence of the proposed algorithms is analytically guaranteed. Thirdly, a comparative study between the proposed schemes is conducted via extensive numerical results, wherein the benefits of adopting SMEP over JMEP and PIEP models are discussed.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • Channel Estimation in Massive MIMO Under Hardware Non-Linearities:
           Bayesian Methods Versus Deep Learning

    • Authors: Özlem Tugfe Demir;Emil Björnson;
      Pages: 109 - 124
      Abstract: This paper considers the joint impact of non-linear hardware impairments at the base station (BS) and user equipments (UEs) on the uplink performance of single-cell massive MIMO (multiple-input multiple-output) in practical Rician fading environments. First, Bussgang decomposition-based effective channels and distortion characteristics are analytically derived and the spectral efficiency (SE) achieved by several receivers are explored for third-order non-linearities. Next, two deep feedforward neural networks are designed and trained to estimate the effective channels and the distortion variance at each BS antenna, which are used in signal detection. We compare the performance of the proposed methods with state-of-the-art distortion-aware and -unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise. Using the data generated by the derived effective channels for general order of non-linearities at both the BS and UEs, it is shown that the deep learning-based estimator provides better estimates of the effective channels also for non-linearities more than order three.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • Latency-Constrained Cost-Minimized Request Allocation for Geo-Distributed
           Cloud Services

    • Authors: Xinping Xu;Wenxin Li;Heng Qi;Junxiao Wang;Keqiu Li;
      Pages: 125 - 132
      Abstract: Latency to end-users and regulatory requirements push cloud providers to operate many datacenters all around the globe to host their cloud services. An emerging problem under such geo-distributed architecture is to assign each user request to an appropriate datacenter to benefit both cloud providers (e.g., low bandwidth cost) and end-users (e.g., low latency)—known as request allocation. However, prior request allocation solutions have significant limitations: they either focus only on optimizing the benefits for one entity (e.g., providers or users), or ignore some practical yet indispensable factors (e.g., heterogeneous latency requirements of different users and diverse per unit bandwidth cost among different datacenters) when optimizing benefits for both entities. In this paper, we study the problem of minimizing the total bandwidth cost for cloud service providers while guaranteeing the latency requirement for end-users. Specifically, we formulate an integer programming with consideration of the diversities in both the delay of requests and per unit bandwidth cost of datacenters. To efficiently and practically solve this problem, we first relax the integer programming into a continuous convex optimization and then take the advantages of random sampling to enforce the solution to be a feasible one for the original integer programming. We have conducted rigorous theoretical analysis to prove that our algorithm can provide a considerable good competitive ratio. Extensive simulations demonstrate that our proposed algorithm can reduce the total bandwidth cost by 30% while guaranteeing the latency requirements of all requests, as compared to conventional methods.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • Analysis of UAV Communications in Cell-Free Massive MIMO Systems

    • Authors: Carmen D’Andrea;Adrian Garcia-Rodriguez;Giovanni Geraci;Lorenzo Galati Giordano;Stefano Buzzi;
      Pages: 133 - 147
      Abstract: We study support for unmanned aerial vehicle (UAV) communications through a cell-free massive MIMO architecture, wherein a large number of access points (APs) is deployed in place of large co-located massive MIMO arrays. We consider also a variation of the pure cell-free architecture by applying a user-centric association approach, where each user is served only from a subset of APs in the network. Under the general assumption that the propagation channel between the mobile stations, either UAVs or ground users (GUEs), and the APs follows a Ricean distribution, we derive closed form spectral efficiency lower bounds for uplink and downlink with linear minimum mean square error channel estimation. We consider several power allocation and user scheduling strategies for such a system, and, among these, also minimum-rate maximizing power allocation strategies to improve the system fairness. Our numerical results reveal that cell-free massive MIMO architecture and its low-complexity user-centric alternative may provide better performance than a traditional multi-cell massive MIMO network deployment.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
  • An End-to-End Performance Analysis for Service Chaining in a Virtualized

    • Authors: Emmanouil Fountoulakis;Qi Liao;Nikolaos Pappas;
      Pages: 148 - 163
      Abstract: Future mobile networks supporting Internet of Things are expected to provide both high throughput and low latency to user-specific services. One way to overcome this challenge is to adopt Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC). Besides latency constraints, these services may have strict function chaining requirements. The distribution of network functions over different hosts and more flexible routing caused by service function chaining raise new challenges for end-to-end performance analysis. In this paper, as a first step, we analyze an end-to-end communication system that consists of both MEC servers and a server at the core network hosting different types of virtual network functions. We develop a queueing model for the performance analysis of the system consisting of both processing and transmission flows. We propose a method in order to derive analytical expressions of the performance metrics of interest, i.e., end-to-end delay, system throughput, task drop rate. Then, we show how to apply the similar method to a larger system and derive a stochastic model for such systems. We observe that the simulation and analytical results are very close. By evaluating the system under different scenarios, we provide insights for the decision making on traffic flow control and its impact on critical performance metrics.
      PubDate: 2020
      Issue No: Vol. 1 (2020)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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