In this paper 1 1 The work was done when the authors were with Futurewei Technologies, Bridgewater, NJ., we present a state-of-the-art method to estimate block error rate (BLER) for coded modulation with practical channel codes, e.g., turbo or low-density parity-check (LDPC) codes. The method is based on the theoretic breakthrough on the finite block coding and a novel rate combining model. With tuning on a very limited number of parameters, the estimates from the proposed method match very well with the simulation results. The proposed method can have wide ranges of applications for wireless communications.

Mobile/wireless virtual reality (VR) services, especially immersive 360° VR videos, have advanced unprecedentedly in recent years. However, the high bandwidth requirement of VR services has compounded the burden on wireless networks. Multicast is a high potential technique for alleviating the bandwidth requirement of 360° VR video streaming, but the multicast capacity is still constrained by the users with poor channel conditions, and it vanishes when the number of users increases while the number of the base station (BS) antennas is fixed. To overcome the drawbacks of multicast, sidelink, which is an adaptation of the core LTE standard that allows the device-to-device (D2D) communications without going through a BS, can be utilized. In this article, two sidelink-aided multicast scenarios (i.e., independent decoding and joint decoding) are studied for multiquality tiled 360° VR video transmission. We propose a utility model for each scenario, and quality level selection, sidelink sender/receiver selection, and transmission resource allocation are optimized to maximize the total utility of all users under the bandwidth constraints as well as the quality smoothness constraints for multiquality tiles. We then develop an iterative two-stage algorithm to obtain suboptimal solutions to the formulated mixed-integer nonlinear programming (MINLP) problems. Simulation results demonstrate the advantage of the proposed solutions over several baseline schemes.

In this paper, we survey state-of-the-art research outcomes in the burgeoning field of Reconfigurable Intelligent Surface (RIS), given its potential for significant performance enhancement of next-generation wireless communication networks by means of adapting a propagation environment. Emphasis has been placed on several aspects gating the commercial viability of future network deployment. Comprehensive summaries are provided for practical hardware design considerations and broad implications of artificial intelligence techniques, as are in-depth outlooks on the salient aspects of system models, use cases, and physical layer optimization techniques.

In this paper, a new rate combining model for coded modulation with practical channel codes is proposed, resulting in a novel utility function. The mathematical properties of the new utility function, such as convexity, are studied. This utility function generalizes the harmonic mean function and demon-strates great potential in various applications in communication and learning problems. The proposed rate combining model is applied to the power allocation problem for parallel Gaussian channels with coded modulation inputs. Our results show that the optimal power allocation based on the new rate combining model outperforms other well known schemes such as mercury water-filling in parallel channels or multiple-input and multiple-output (MIMO) channels.

In this paper, we design efficient methods for the mean and variance estimations of QAM symbols with applications to iterative receivers. The proposed methods for optimal estimations enable scalable hardware implementations for any Gray mapped PAM or QAM with less circuitries. For variance estimations, the proposed method reduces the complexity from $O((\log_{2}N)^{2})$ in the existing method to $O(\log_{2}N)$ for an N-QAM. Two suboptimal methods are also proposed to avoid the multiplications in the hardware implementations. The presented approximation approaches provide similar or better performance than the existing methods but with simpler implementation and less logical circuitries. In addition, based on the proposed architecture, we present novel unit module designs with disassembled estimation components and the schematics to virtualize the estimation hardware. With efficient design of unit module and control unit, maximized parallelization can be achieved.

In this paper, we first define a high dimensional (HiDi) channel characteristics, i.e., space-frequency covariance, for wideband MIMO-OFDM systems. We then design the conversion of the HiDi covariance in frequency domain from one carrier frequency to another, e.g., for FDD systems. Specifically, we apply the projection method in a Hilbert space to estimate the power angle delay spectrum and form the frequency domain conversion of the space-frequency covariance. We also obtain the asymptotic solutions when considering the infinite delay spread, which significantly reduces the complexity. Moreover, we generalize the conversions of space-frequency covariance in both spatial and frequency domains with two exemplary multi-panel scenarios. We then apply the general solutions to a specific antenna array configuration, i.e., uniform linear array (ULA), and obtain the explicit expressions of conversions. Numerical simulations demonstrate the efficiency of the designed conversions.

In this paper, we consider an adaptive grouped physical layer multicasting for large-scale multi-antenna wireless systems in which a set of users are divided into several groups and each user group are assigned with a unique beamforming vector for multicast transmissions on the orthogonal resources. Based on the adaptive grouped multicast framework, we consider the joint design of user grouping and multicast beamforming adapted to the user channels. Two design objectives are studied, i.e., the average-rate maximization and the max-min fairness. We propose an iterative user grouping and beamforming design method for both optimization objectives. For iterative user grouping, we present a method for selecting better initial grouping centers. Moreover, to overcome the issue of converging to a local optimum for the iterative approach, we propose a novel enhancement scheme via user grouping perturbation, which performs very close to the exhaustive grouping search. Simulation results demonstrate the efficacy of the proposed designs.

Cloud-assisted wireless networks are emerging solutions that unite wireless networks and cloud-computing to provide cloud services at the edge of the network in order to support the foreseen massive demands from data and computation hungry mobile users. In this chapter, we first provide an overview of the two emerging cloud-assisted wireless network paradigms, namely, cloud radio access network (C-RAN), where the functionalities at the base stations (BSs) are centralized, and mobile-edge computing (MEC), recently renamed multi-access edge computing, which aims at providing the RAN with computing and storage resources. We then leverage the C-RAN and MEC paradigms to design novel cooperative caching frameworks that explore the synergies of the in-network computing and storage resources. Specifically, a novel cooperative hierarchical caching framework is designed in C-RAN, where caching is performed both at the distributed BSs and at the center processing unit (CPU) that bridges the gap between the traditional edge-based and core-based caching schemes. Furthermore, a joint cooperative caching and processing framework is designed in an MEC network, where the MEC servers perform both cache storage and video transcoding to support adaptive bitrate (ABR) video streaming. Numerical simulations are performed using real-world video requests on YouTube and synthetic content requests. It is shown that important gains can be achieved in terms of content access delay, cache-hit ratio, and backhaul traffic load using the envisioned cooperative caching frameworks.

In this paper, we consider the design of multiuser (MU) massive multiple-input and multiple-output (MIMO) employing hybrid beamforming, i.e., an analog radio frequency (RF) beamforming at the front end concatenated with a digital baseband precoding. We present several RF beam feedback protocols with small overhead, i.e. the RF beam indices with or without channel quality index (CQI). Based on the beam feedback schemes, we design novel algorithms to jointly select the transmit RF beams and schedule the users to improve the system throughput. Specifically, when the users feed back multiple candidate RF beams, the new algorithms resolve the conflicts when different users feedback some same RF beams. The simulation results demonstrate the efficacy of the proposed algorithms.

In this paper, we consider the finite blocklength analysis for the single-user block fading channel with discrete input constellations. In particular, we derive novel achievability results on block error rate (BLER) for a given code rate and blocklength. We first consider the scalar block fading channel case for which we adopt linear precoding as a method to increase the spectral efficiency associated with full temporal diversity. Then, we extend our analysis to the multiple-input multiple-output block fading channels under space-time linear precoding. Numerical evaluations demonstrate the impact of finite blocklength and diversity in block fading channels as well as the effectiveness of linear precoding in minimizing the achievable BLER under practical modulation schemes, such as QPSK.

In cellular communications, deploying a larger number of antennas at the base station, also called massive multiple-input multiple-output (MIMO), can offer a significant improvement in system throughput. In this paper, we exploit the spatial fading correlations in massive MIMO to reduce the downlink training and the corresponding feedback overhead in frequency division duplexing systems. We first study the user clustering, where the users with similar spatial channel correlations are clustered together. In the study, we provide the optimal metric and prove the convergence of the user clustering. Then, we propose an efficient eigenspace training and precoding (EETP) framework, where two different prebeamforming matrices are designed to minimize the channel estimation error and to manage the inter-user interference, respectively. In the results, we show that the channel estimation error for EETP decreases monotonically when either the number of prebeamforming vectors or the number of clusters increases. The spectral efficiency of the new algorithms is evaluated extensively with different user distributions, errors in channel correlations, different numbers of clusters, and different coherence block lengths, as well as with dynamic user scheduling for a large number of users. The new EETP not only achieves significant savings in the downlink training and the corresponding feedback, but also offers significantly higher system throughput compared with the existing schemes in the literature.

In this paper, a new approach to the wireless link adaptation based on theoretical results from the finite blocklength analysis is proposed. Theoretical bounds on parallel complex AWGN channels with coded modulation inputs are studied. A model based on these theoretical bounds is provided for the application of link adaptation. The proposed approach to link adaption only requires a significantly small number of simulations to tune the model and is able to predict the performance of parallel channels with arbitrary combinations of SNRs without referring to any lookup table as in the traditional approach. The model is tested with turbo and polar codes. The result demonstrates significant improvement over the traditional methods in link adaptation in terms of accuracy and the amount of computation.

In this paper, we consider the feedback and precoding designs for massive multiple-input multiple-output (MIMO) systems. Based on a recently introduced two-stage beamforming framework, termed as joint spatial division and multiplexing (JSDM), we propose a generalized JSDM approach to deal with the practical issue that the user’s channel usually has multiple scattering clusters for mobile broadband which may incur significant performance degradation with original JSDM. In particular, we propose a new feedback approach where the users feed back additional channel measurements, referred to as inter-group channel state information (CSI) feedback. To utilize the inter-group CSI, we first provide the joint group precoding (JGP) approaches, then propose a novel virtual user based low-complexity per-group precoding (PGP) method with interference avoidance. Finally, we explore a general PGP approach with power allocations for interference management. Simulation results show that the spectral efficiency is significantly improved with proposed designs.

We consider the problem of downlink multicast transmission of user data in massive MIMO systems. Due to the nature of multicast transmission, the common data rate in a multicast group is constrained by that of the user with the worst Signal-to-Noise-Ratio (SNR). As a consequence, serving a large number of users in a single multicast group might degrade the system performance. To overcome this drawback, utilizing spatial degrees of freedom offered by a large number of transmit antennas, we propose to dynamically divide the set of serving users into multiple multicast groups and jointly design the user grouping pattern and co-channel beamforming vectors of these groups. Given the NP-hardness of the considered problem, we decompose it into a multi-group multicast beamforming subproblem and a user grouping subproblem. We proposed several low-complexity methods to iteratively solve these subproblems in order to obtain a suboptimal solution to the original problem. Simulation results show that our proposed GRouping And Beamforming (GRAB) scheme achieve significantly higher average sum-rate performance compared to that of the existing multicast schemes.

This paper considers the design of network coding schemes for reliable wireless broadcast and multicast transmissions, in which the same packet is broadcast to a group of receivers. Network coding across multiple broadcasted packets is employed to generate redundant packets for the broadcast retransmissions so that the lost packets can be recovered. It is assumed that optimal decoders are employed at the receivers and the focus is on the design of short block codes with small numbers of redundant bits. To this end, use if first made of the residual graph representation to calculate the error probability of the optimal decoder. Then two code design schemes are proposed to minimize the error probability, including a low-complexity deterministic greedy code design algorithm as well as a stochastic code construction algorithm inspired by the simulated annealing technique. Extensive simulation studies have been carried out to assess the performance of the proposed schemes. It is seen that for a given number of retransmissions, the proposed network coding schemes can considerably increase the average number of recovered packages per user at the receivers and thereby improve the spectral efficiency over traditional coding methods.

In this article, we propose a novel cooperative hierarchical caching framework in a Cloud Radio Access Network (C-RAN), in which a new cloud-cache at Cloud Processing Unit (CPU) is envisioned to bridge the storage-capacity/delay-performance gap between the traditional edge-based and core-based caching paradigms. A delay-cost model is introduced and the cache placement problem is formulated that aims at minimizing the average delay-cost of content delivery in the network. Given the NP-completeness of the cache placement problem, we propose a low-complexity heuristic cache-management strategy comprising of a proactive cache-distribution algorithm and a reactive cache-replacement algorithm. Furthermore, a Cache-Aware Request Scheduling (CARS) algorithm is devised in order to optimize online the tradeoff between content download rate and content access delay. Via extensive numerical simulations-carried out using both real-world YouTube video requests and synthetic content requests-it is demonstrated that the proposed cache-management strategy outperforms traditional caching strategies in terms of cache hit ratio, average content access delay, and backhaul traffic load. Additionally, it is shown that the proposed CARS algorithm achieves superior tradeoff performance over traditional approaches that optimize either users' rate or access delay alone.

research-article Share on MIMO Technologies IN 5G NEW RADIO Authors: Guosen Yue Huawei R&D USA, Bridgewater, NJ Huawei R&D USA, Bridgewater, NJView Profile , Lingjia Liu Electrical Engineering and Computer Science Dept., University of Kansas, Lawrence, KS Electrical Engineering and Computer Science Dept., University of Kansas, Lawrence, KSView Profile , Yongxing Zhou Wireless Research and Development Dept. Huawei Co. Ltd., Beijing, China Wireless Research and Development Dept. Huawei Co. Ltd., Beijing, ChinaView Profile , Jianzhong (Charlie) Zhang Samsung Research America, Richardson, TX Samsung Research America, Richardson, TXView Profile Authors Info & Claims GetMobile: Mobile Computing and CommunicationsVolume 21Issue 1March 2017 pp 19–24https://doi.org/10.1145/3103535.3103543Published:31 May 2017Publication History 2citation640DownloadsMetricsTotal Citations2Total Downloads640Last 12 Months122Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

FIELD: radio engineering, communication.SUBSTANCE: method is provided for receiving a rank indication (RI) performed in the base station, comprising the steps of: receiving, from the user equipment, the RI, the first precoding matrix indicator (PMI) and the second PMI, determining the index iof the codebook based on the RI and the second PMI. Values 0-15 are assigned to the second PMI (I) for the RI=1, and the values 0-3 are assigned to the second PMI (I) for each of the RI=2, RI=3 and RI=4. The index iof the codebook contains Ifor RI=1, and index iof the codebook contains {0, 1, 4, 5} for RI=2.EFFECT: providing a decision regarding transmission, from the user equipment to the base station, feedback information for the codebook.26 cl, 7 dwg, 22 tbl

Un metodo realizado en una estacion base utilizada en un sistema de comunicaciones inalambricas, que comprende: disponer de un libro de codigos que incluye una pluralidad de matrices precodificadoras; precodificar datos con una de la pluralidad de matrices precodificadoras; y transmitir, a un equipo de usuario, los datos precodificados, en el que cada matriz precodificadora W satisface W >= W(1)W(2), en el que la primera matriz W(1) se elige de un primer libro de codigos , y la segunda matriz W(2) se elige de un segundo libro de codigos, que se caracteriza por**Formula** en donde indica un producto Hadamard, Q1 es un entero positivo,**Formula** J, K y L son enteros positivos,**Formula** y N es el numero de antenas transmisoras**Formula** m es un entero positivo y 1 <= m <= J .

In this paper, we consider the pilot design based on the mutual incoherence property (MIP) for sparse channel estimation in orthogonal frequency-division multiplexing (OFDM) systems. With respect to the length of channel impulse response (CIR), we first derive a sufficient condition for the optimal pilot pattern generated from the cyclic different set (CDS). Since the CDS does not exist for most practical OFDM systems, we propose three pilot design schemes to obtain a near-optimal pilot pattern. The first two schemes, including stochastic sequential search (SSS) and stochastic parallel search (SPS), are based on the stochastic search. The third scheme called iterative group shrinkage (IGS) employs a tree-based searching structure and removes rows in a group instead of removing a single row at each step. We later extend our work to multiple-input-multiple-output (MIMO) systems and propose two schemes, i.e., sequential design scheme and joint design scheme. We also combine them to design the multiple orthogonal pilot patterns, i.e., using the sequential scheme for the first several transmit antennas and using the joint scheme to design the pilot pattern for the remaining transmit antennas. Simulation results show that the proposed SSS, SPS, and IGS converge much faster than the cross-entropy optimization and the exhaustive search and are thus more efficient. Moreover, SSS and SPS outperform IGS in terms of channel estimation performance.