Book•
4G: LTE/LTE-Advanced for Mobile Broadband
21 Mar 2011-
TL;DR: In this article, the authors focus on LTE with full updates including LTE-Advanced to provide a complete picture of the LTE system, including the physical layer, access procedures, broadcast, relaying, spectrum and RF characteristics, and system performance.
Abstract: Based on the bestseller "3G Evolution - HSPA and LTE for mobile broadband" and reflecting the ongoing success of LTE throughout the world, this book focuses on LTE with full updates including LTE-Advanced to provide a complete picture of the LTE system. Overview and detailed explanations are given for the latest LTE standards for radio interface architecture, the physical layer, access procedures, broadcast, relaying, spectrum and RF characteristics, and system performance. Key technologies presented include multi-carrier transmission, advanced single-carrier transmission, advanced receivers, OFDM, MIMO and adaptive antenna solutions, advanced radio resource management and protocols, and different radio network architectures. Their role and use in the context of mobile broadband access in general is explained. Both a high-level overview and more detailed step-by-step explanations of the LTE/LTE-Advanced implementation are given. An overview of other related systems such as GSM/EDGE, HSPA, CDMA2000, and WIMAX is also provided. This book is a 'must-have' resource for engineers and other professionals in the telecommunications industry, working with cellular or wireless broadband technologies, giving an understanding of how to utilize the new technology in order to stay ahead of the competition. The authors of the book all work at Ericsson Research and have been deeply involved in 3G and 4G development and standardisation since the early days of 3G research. They are leading experts in the field and are today still actively contributing to the standardisation of LTE within 3GPP. Includes full details of the latest additions to the LTE Radio Access standards and technologies up to and including 3GPP Release 10Clear explanations of the role of the underlying technologies for LTE, including OFDM and MIMO Full coverage of LTE-Advanced, including LTE carrier aggregation, extended multi-antenna transmission, relaying functionality and heterogeneous deploymentsLTE radio interface architecture, physical layer, access procedures, MBMS, RF characteristics and system performance covered in detail
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TL;DR: This work proposes adaptive and generic scheduling scheme (AGSS) a generic resource allocation procedure that enables the implementation of state-of-the-art scheduling policies for building a LTE scheduler and proposes its own scheduling policy called opportunistic PDOR aware (OPA) that both optimizes the use of the radio spectrum and provides the quality of service actually expected by applications.
Abstract: Schedulers for multi-carrier wireless networks are a central element of cellular systems and are subject to extensive research. However, state-of-the-art schedulers are hardly implementable in a real system such as long term evolution advanced (LTE-A), which imposes additional constraints on how resources are allocated. To address this problem, we first propose adaptive and generic scheduling scheme (AGSS) a generic resource allocation procedure that enables the implementation of state-of-the-art scheduling policies for building a LTE scheduler. In a second step, we propose our own scheduling policy called opportunistic PDOR aware (OPA) that both optimizes the use of the radio spectrum and provides the quality of service actually expected by applications. We show how to implement this policy using our generic scheduling scheme. We then compare the performance of AGSS to the classic scheme in a LTE environment when used with a given policy. We show that the proposed scheme outperforms the classic scheme whatever the policy. We also establish that OPA offers the best performances in terms of capacity and quality of service compared to state-of-the-art policies.
7 citations
31 Mar 2016
TL;DR: A new system utilizing both OAM and SAM is proposed by using practical dual polarization antenna based UCA architecture to increase the channel capacity and bit error rate(BER) performances are observed for each antenna configuration.
Abstract: Communication exploiting orbital angular momentum(OAM) was proposed recently to improve transmission performance while the method utilizing spin angular momentum(SAM) was researched actively already. A dual polarization antenna is a typical example using SAM. Taking advantage of OAM transmission, multiplexing gain can be achieved by transmitting multi-modes of OAM in line of sight(LOS) environment. To exploit OAM in radio frequency domain, uniform circular array(UCA) architecture assigning signals with different phase to each antenna was proposed to generate OAM carrying beam. However, only single polarization antenna was considered in existing research, so there is a limitation to increase channel capacity. Also, conventional research only considered ideal antennas that radiate isotropically, which are not much practical. In this paper, we propose a new system utilizing both OAM and SAM by using practical dual polarization antenna based UCA architecture to increase the channel capacity. Then, a receive array is located at off-axis to apply practical environment. The channel capacity according to the distance between transmit and receive array called T-R distance is investigated to analyze the performance of proposed system for three antenna configurations that are vertical polarization, horizontal polarization and dual polarization antenna configuration. Furthermore, bit error rate(BER) performances are observed for each antenna configuration.
7 citations
Cites background from "4G: LTE/LTE-Advanced for Mobile Bro..."
...The radiation patterns of dipole antenna laid vertically and horizontally, respectively, are not orthogonal to each other perfectly....
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01 Dec 2018
TL;DR: This work proposes a novel unsupervised framework that first extracts channel features from CSI which characterize large-scale fading effects of the channel, and then uses specialized dimensionality reduction tools to construct the channel chart.
Abstract: Future wireless communication systems will rely on large antenna arrays at the infrastructure base stations (BSs) to serve multiple users with high data rates in a single cell. We demonstrate that the availability of high-dimensional channel state information (CSI) acquired at such multi-antenna BSs enables one to learn a chart of the radio geometry, which captures the spatial geometry of the users so that points close in space are close in the channel chart, using no other information than wireless channels of users. Specifically, we propose a novel unsupervised framework that first extracts channel features from CSI which characterize large-scale fading effects of the channel, and then uses specialized dimensionality reduction tools to construct the channel chart. The channel chart can, for example, be used to perform (relative) user localization, predict cell hand-overs, or guide scheduling tasks, without accessing location information from global navigation satellite systems.
7 citations
07 Jun 2020
TL;DR: This paper aims to design Deep Reinforcement Learning (DRL)-based optimizers with Deep Q-Network (DQN) and Deep Deterministic Policy Gradients (DDPG) for optimizing RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (D Q).
Abstract: Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems, however, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision during the simultaneous massive. Despite that this collision problem has been treated in existing RACH schemes by organizing IoT devices' transmission and retransmission via the central control at the Base Station (BS), these existing RACH schemes are usually fixed over time, thus can hardly adapt to time-varying traffic patterns. In order to optimize the long-term objective in the number of success devices, this paper aims to design Deep Reinforcement Learning (DRL)-based optimizers with Deep Q-Network (DQN) and Deep Deterministic Policy Gradients (DDPG) for optimizing RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (DQ). Specifically, we apply DQN to handle discrete action selection for the BO as well as the DQ schemes, and DDPG to handle continuous action selection for the ACB scheme. Both agents are integrated with Gated recurrent unit Gated Recurrent Unit (GRU) network to approximate their value function/policy, which can improve the optimization performance by capturing temporal traffic correlations. Numerical results showcase that our proposed DRL-based optimizers considerably outperform conventional heuristic solutions in terms of the number of success access devices.
7 citations
Cites background from "4G: LTE/LTE-Advanced for Mobile Bro..."
...The first step, based on framed-ALOHA principle, allows IoT device to transmit a randomly selected preamble, while the latter three, based on BS’s scheduled channel, use for control information exchanges [9]....
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...transmissions from collided devices using the same channel at the same time [9]....
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TL;DR: A multiple input and multiple output (MIMO) antenna that comprises a printed microstrip antenna and a printed double-L sleeve monopole antenna for LTE 1800 wireless application is presented.
Abstract: A multiple input and multiple output (MIMO) antenna that comprises a printed microstrip antenna and a printed double-L sleeve monopole antenna for LTE 1800 wireless application is presented. The printed double-L sleeve monopole antenna is fed by a 50 ohm coplanar waveguide (CPW). A novel T-shaped microstrip feedline printed on the other side of the PCB is used to excite the waveguide's outer shell. Isolation characteristics better than -15 dB can be obtained for the proposed MIMO antenna. The proposed antenna can operate in LTE 1800 (1710 MHz-1880 MHz). This antenna exhibits omnidirectional characteristics. The efficiency of the antenna is greater than 70% and has high gain of 2.18 dBi.
7 citations