Integrated sensing and communication (ISAC) is expected to play a prominent role in 6G. Avoiding full duplex transceivers, bi-static sensing is free from self-interference and able to leverage ubiquitous network devices, thus considered an indispensable scenario of ISAC. However, bi-static sensing must resolve the non-ideal synchronization of the transceiver nodes. Such timing offset (TO) remains a challenging issue, especially in the non-line-of-sight (NLOS) condition with rich multipaths. This article makes the best use of all multipaths, deriving the cyclic shift relation between the delay spectrums measured at the two sides of transceivers to estimate the TO. Based on this theoretical analysis, an algorithm called enhanced round-trip measurement (eRTM) is developed to mitigate the TO. Specifically, the cyclic cross-correlation between the amplitudes of the two normalized delay spectrums measured at the two sides of the transceivers respectively is conducted to estimate the TO, which is then used for TO mitigation. Extensive simulations have verified the advantages of the eRTM algorithm, especially in NLOS conditions and with low signal-to-noise ratios (SNRs). In addition, field tests conducted with a bi-static ISAC prototype system and eRTM algorithm, have achievedcentimeter-level positioning accuracy even in NLOS conditions with rich multipaths, confirming the validities of our theoretical analysis as well as the proposed algorithm.

Offloading computationally intensive tasks to edge nodes reduces latency and improves user experience. Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) can effectively address the limitations of the fixed deployment of traditional edge nodes, but the dynamic nature of UAVs brings challenges to the optimization of computation offloading. Methods based on deep reinforcement learning (DRL) can efficiently learn edge network dynamics to optimize computation offloading in UAV-assisted systems. In the system model, different edge nodes such as UAVs, roadside units, and user equipments (UEs) are treated as agents in the reinforcement learning network, so that the optimization of the edge computation offloading strategy can be transformed into a multi-agent optimization problem. In addition, meta-reinforcement learning is introduced into the multi-agent deep deterministic policy gradient (MADDPG) algorithm to cope with the dynamics and uncertainty of the UAV-assisted edge computing network environment. Simulation results show that the computation offloading strategy based on meta-reinforcement learning can not only quickly adapt to the dynamic changes of the network environment but also outperform other benchmark algorithms in terms of network energy efficiency.

Foreseen as a killer application in next-generation wireless networks, integrated sensing and communication (ISAC) has gained tremendous developments in recent years, and will contribute to realize Internet of Everything (IoE). Particularly, by enabling separable sensing transceivers, bi-static sensing is free from self-interference and able to leverage ubiquitous network devices, and thus has become an indispensable scenario of ISAC. However, bi-static sensing suffers from transceiver asynchronization, which induces timing offset (TO), timing drift (TD) and carrier frequency offset (CFO). In this paper, we first give the theoretical analyses on how TO, TD and CFO impact the sensing signal, under the practical configuration following the new radio (NR) protocol. Based on this, we systematically reveal the mechanism of TD and the correspondingly resulted delay-Doppler spectrum dispersion. Specifically, the delay spectrum shifts and the phase drifts induced by TD are analyzed, which are the two main factors eventually leading to the delay-Doppler spectrum dispersion and consequent severe errors in signal detection and parameter estimation. Based on the revealed mechanisms, we develop an asynchronous delay-Doppler (ADD) algorithm for bi-static sensing, including delay spectrum alignment and phase compensation, respectively to suppress the delay spectrum shifts and phase drifts. Thanks to the revealed mechanism, the ADD algorithm does not rely on specific prerequisites. Simulation results have confirmed the revealed mechanisms and verified the effectiveness of the ADD algorithm. Particularly, field tests are conducted on an ISAC prototype, and achieve a centimeter-level positioning accuracy, which further confirms the revealed mechanisms and validates the ADD algorithm.

This paper presents a new multiple-input multiple-output (MIMO) communication system. The system, termed as “pseudo MIMO”, facilitates the transmission of more parallel data streams than the number of receiving radio frequency (RF) chains. The key principle involves connecting multiple antenna elements to each receiving RF chain and employing multiple analog combining patterns for signal reception within the orthogonal frequency division multiplexing (OFDM) sampling period. Through detailing the entire transmission and signal processing procedures by matrix manipulation, the equivalent channel matrix for each OFDM subcarrier is derived in a unified matrix form, which can be further represented by the product of the analog combining matrix and the frequency domain wireless channel matrix. As per the equivalent channel matrix, an optimization problem is formulated and solved to maximize the spectral efficiency by jointly designing the digital precoding and analog combining matrices. Numerical results demonstrate that the performance of a pseudo MIMO system can closely approach that of a fully digital (FD) MIMO system with the same antenna configuration but more RF chains. It is also revealed that a low-precision discrete analog combining scheme is sufficient to achieve nearly optimal performance. Further, the effectiveness of pseudo MIMO technology is validated through prototype testing.

With the development of intelligent transportation systems, vehicular edge computing (VEC) has played a pivotal role by integrating computation, storage, and analytics closer to the vehicles. VEC represents a paradigm shift towards real-time data processing and intelligent decision-making, overcoming challenges associated with latency and resource constraints. In VEC scenarios, the efficient scheduling and allocation of computing resources are fundamental research areas, enabling real-time processing of vehicular tasks and intelligent decision-making. This paper provides a comprehensive review of the latest research in Deep Reinforcement Learning (DRL)-based task scheduling and resource allocation in VEC environments. Firstly, the paper outlines the development of VEC and introduces the core concepts of DRL, shedding light on their growing importance in the dynamic VEC landscape. Secondly, the state-of-the-art research in DRL-based task scheduling and resource allocation is categorized, reviewed, and discussed. Finally, the paper discusses current challenges in the field, offering insights into the promising future of VEC applications within the realm of intelligent transportation systems.

Unmanned-aerial-vehicle (UAV)-assisted multiaccess edge computing (MEC) networks can effectively broaden the application scope of the Internet of Things (IoT) in complex scenarios, such as maritime operations, military communications, and emergency commands. However, uncertain factors, such as weather changes and temporary airspace control, pose great challenges to UAV flight safety. Obstacles resulting from these uncertain factors may intersect with UAVs with preplanned flight paths, leading to accidents. Therefore, generating the optimal flight trajectory to avoid these obstacles is key in the successful operation of this fuzzy system. In this article, we present a heuristic trajectory generation scheme for complex offshore environments that can generate optimal trajectories according to complex terrain conditions and avoid uncertain obstacles. First, we build a complex terrain model based on a 3-D offshore environment to simulate the conditions in UAV-assisted MEC networks. Second, we propose a network performance optimization objective function that is based on UAV characteristics. Third, we improve the existing ant colony optimization (ACO) algorithm by introducing chaotic mapping, polarizing the pheromone recording rule, and implementing a simulated annealing screening mechanism to efficiently generate trajectories. Finally, we design an efficient obstacle avoidance algorithm for different combinations of obstacle regions. The simulation results show that our proposed trajectory generation scheme can efficiently avoid obstacles and significantly improve the total trajectory loss rate compared with that of baseline schemes.

Entering the 5G era, the mobile network operators (MNO) are facing greater challenges in providing services cost effectively than any other previous generations. The potential solutions to this are lying on the emerging trend of deep convergence of information technology (IT), communication technology (CT) and data technology (DT). In particular, the O-RAN technology, the representation of such ICDT convergence and proposed by the O-RAN ALLIANCE in 2018, is transforming Radio Access Networks towards a new paradigm featuring openness, cloudification and intelligence. O-RAN has gained huge attention from both industry and academia since its inception. In this paper, we presented the recent endeavors from China Mobile, including our deployment scenarios, various test results from open fronthaul, cloud platform to the intelligent controller. Our rich and comprehensive tests have demonstrated the viability and superiority of current O-RAN technologies. Furthermore, we also provide our deep thinking on the O-RAN future evolution in order to better serve the emerging applications such as Metaverse, cloud extended-reality (XR), extensive enterprise private 5G verticals and so on.

Real-time healthcare indicates monitoring people's health status in a timely manner. In this regard, wireless techniques can be used to provide immediate access to bio-sensing information, facilitating monitoring and instant communication between healthcare providers [1]. Such a scheme aims to realize real-time decision-making and intervention, improving patient outcomes and efficiency in healthcare delivery.

Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly notifying the utilized NOMA signatures causes large signaling cost, blind signature classification naturally becomes a low-cost option. To accomplish signature classification for NOMA, we study both likelihood- and feature-based methods. A likelihood-based method is firstly proposed and showed to be optimal in the asymptotic limit of the observations, despite high computational complexity. While feature-based classification methods promise low complexity, efficient features are non-trivial to be manually designed. To this end, we resort to artificial intelligence (AI) for deep learning-based automatic feature extraction. Specifically, our proposed deep neural network for signature classification, namely DeepClassifier, establishes on the insights gained from the likelihood-based method, which contains two stages to respectively deal with a single observation and aggregate the classification results of an observation sequence. The first stage utilizes an iterative structure where each layer employs a memory-extended network to explicitly exploit the knowledge of signature pool. The second stage incorporates the straight-through channels within a deep recurrent structure to avoid information loss of previous observations. Experiments show that DeepClassifier approaches the optimal likelihood-based method with a reduction of 90% complexity.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In the typical indoor office environments, the existing site locations resource on ceiling for visible light communications (VLC) access points (AP) is relatively limited due to the originally illumination & fire-fighting oriented ceiling planning. For reducing the capital expenditure of VLC networking in most situations, the straight forward solution is making full use of the limited linear location of luminaries. In this work, for the first time, the non-circular symmetric optical beam is introduced to the cells planning of VLC networks. The coverage performance is compared between the typical noncircular symmetric optical beam pattern i.e. BBE LED pattern and the well investigated circular symmetric optical beam pattern i.e. Lambertian pattern. It is sufficiently demonstrated that the satisfying matching capability of this non-circular symmetric optical beam to indoor scenario with just 3 linear site locations. By appropriately placing the BBE LED pattern, more than 12 dB the signal-to-interference-plus-noise ratio (SINR) enhancement is provided for more than 70% receiver positions while the total optical transmitted power of all VLC AP remains unchanged. Moreover, the superior performance uniformity of this optical beam pattern is separately identified in cellular cells configuration case and in electrical frequency reuse case.

Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the required high pilot overhead. In this paper, we propose a 3D structured orthogonal matching pursuit (3D-SOMP) algorithm based channel estimation technique. First, we show that the OTFS MIMO channel exhibits 3D structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D structured channel sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed 3D-SOMP algorithm can achieve accurate channel state information with low pilot overhead.

Mobile communication continues to play an important role in the modern economy, including consumer, health, education, logistics, and other major industries. At the same time, the current Internet has created a key infrastructure component for our modern world, having an impact on almost every aspect of our daily lives. The Internet democratized access to information and has enabled emerging economies to participate in the modern global economy. We are now approaching the next big wave of the Internet innovation: the Tactile Internet. The widely used term “Tactile Internet” was coined and defined by the IEEE P1918.1 as: “A network or network of networks for remotely accessing, perceiving, manipulating or controlling real or virtual objects or processes in perceived real time by humans or machines.”

Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.

Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user. Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.

Millimeter-wave (mm-wave) massive MIMO with lens antenna array can considerably reduce the number of required radio-frequency (RF) chains by beam selection. However, beam selection requires the base station to acquire the accurate information of beamspace channel. This is a challenging task as the size of beamspace channel is large, while the number of RF chains is limited. In this paper, we investigate the beamspace channel estimation problem in mm-wave massive MIMO systems with lens antenna array. Specifically, we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beamspace channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beamspace channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beamspace channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beamspace channel can be exploited.

In this paper, we analyze the spectral efficiency (SE) and energy efficiency (EE) of multi-user (MU) multiple-input and multiple-output (MIMO) in multi-cell downlink networks. We first analyze the achievable sum-rate, i.e., the SE, of MU-MIMO systems with maximal ratio transmission (MRT) and zero-forcing (ZF) precoders in downlink cellular networks. Different from the conventional analysis, we derive the achievable sum-rate under the assumption that the number of the BS antennas is huge but limited. Based on the analytical results, we obtain the system EE and further analyze the effect of the number of BS antennas and scheduled users on system EE. The computer simulation results show that the analytical results is accurate and there exists an optimal relationship between the BS antennas and the scheduled users.

In this paper, we address the problem of power consumption minimization in Heterogeneous networks (HetNets) of China Mobile by implementing dynamic base stations (BSs) switching operation. Particularly, considering the minimal rate requirements of users as well as the realistic power consumption model of BSs, we formulate the problem as an integer linear programming which is NP-complete. Then, to efficiently solve this problem, the Power Efficient Base Station Operation with User Association scheme (PEBUA) and Adaptive Multi-cell Coordination Algorithm for Energy Saving (AMCES) have been developed, which can be efficiently adopted in different network conditions, respectively. Simulation results verify the validity of our analysis and additionally, compared with the existing method in practice network operation, show the effectiveness of the proposed schemes. It should be noted that both of the proposed algorithms can be applied in real HetNets to save the overall power consumption without decreasing users' traffic rates, which makes our study more feasible.

The amorphous multi-connected networks have e-merged as a new trend to meet the fast increasing wireless capacity and coverage demands of future 5G networks. To realize such amorphous networks, ultra dense network (UDN) technology is expected as a promising candidate for its flexible network architecture and abundant radio resources. Yet, the multi-connection optimization for UDN is still left as an open problem nowadays. This motivates us to explore how many and which access points (APs) should be accessed in multi-connected amorphous UDNs. To solve this problem, we introduce a new definition of the maximum number of accessed APs for help, and propose two algorithms to identify this number and handle the AP selection respectively. Monte Carlo simulations testify the validity of our scheme and prove that our strategy can guarantee the soft handover and overcome the “ping-pong effect” efficiently in various kinds of networks with high sum-rate performance.

After about a decade of intense research, spurred by both economic and operational considerations, and by environmental concerns, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, with millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribution to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding.

5G network is anticipated to meet the challenging requirements of mobile traffic in the 2020's, which are characterized by super high data rate, low latency, high mobility, high energy efficiency, and high traffic density. This paper provides an overview of China Mobile's 5G vision and potential solutions. Targeting a paradigm shift to user-centric network operation from the traditional cell-centric operation, 5G radio access network (RAN) design considerations are presented, including RAN restructure, Turbo charged edge, core network (CN) and RAN function repartition, and network slice as a service. Adaptive multiple connections in the user-centric operation is further investigated, where the decoupled downlink and uplink, decoupled control and data, and adaptive multiple connections provide sufficient means to achieve a 5G network with “no more cells.” Software-defined air interface (SDAI) is presented under a unified framework, in which the frame structure, waveform, multiple access, duplex mode, and antenna configuration can be adaptively configured. New paradigm of 5G network featuring user-centric network (UCN) and SDAI is needed to meet the diverse yet extremely stringent requirements across the broad scope of 5G scenarios.

The rapid growth in the energy consumption of mobile networks has become a major concern for mobile operators. Today's mobile networks' usage is dominated by over-the-top (OTT) applications, and operators are keen to determine the network energy consumed by these OTT applications. With a recent shift in user behavior toward a preference for instant messaging (IM) applications over conventional mobile services, operators are interested in exploring what impact OTT IM applications such as WeChat will have on the energy consumption of a network when compared with a corresponding conventional mobile service. Here, we present for the first time energy assessment models for mobile services based on real network and service measurements to address this need. Using WeChat as an OTT IM application example, our results show that WeChat consumes more network energy than conventional mobile services for both light users and heavy text users due to the network signaling energy overhead. In comparison, for heavy voice users, WeChat consumes less network energy since voice messages are first recorded and then sent in packet bursts. Our findings provide a quantitative analysis of the energy consumption of mobile services, which should be valuable for mobile operators and OTT application developers to improve the energy-efficiency of mobile applications and services.

Deploying ultra dense networks (UDNs) can boost the spectral efficiency (SE) and energy efficiency (EE) of cellular networks, where the density of base stations (BSs) may exceed the density of users. For UDNs, inter-cell interference is a limiting factor on improving both the SE and EE, and complicated interference coordination is highly undesirable. In this paper, we investigate energy and spectral efficient frequency reuse factors in randomly deployed cells, where BS sleeping is allowed for the cells without active users. Toward this goal, we find the frequency reuse factor that maximizes the SE or EE upper bound of the network with given ratio of BS density to user density, and quantify the SE and EE gains of universal frequency reuse over partial frequency reuse in UDNs. Our analytical results show that the universal frequency reuse is SE-optimal for the networks with arbitrary BS/user density ratios, but is EE-optimal only when the ratio exceeds a threshold, which highly depends on the entire bandwidth of the network and the number of antennas at each BS. Both the normalized SE and EE gains in UDNs increase with the BS/user density ratio. The simulation results are provided to validate our analysis.

Deploying ultra dense networks (UDNs) is a major trend in the evolution of cellular networks, where the number of base stations (BSs) may exceed the number of users. In this paper, we investigate energy and spectral efficient frequency reuse strategies in hexagonally deployed cells, where BS sleeping is allowed for a cell without active users. Toward this goal, we derive the closed form expression of the energy efficiency (EE)and spectral efficiency (SE), and find the optimal frequency reuse strategies that maximize the EE and SE, respectively. Our analyses show that full frequency reuse is SE-optimal for cellular networks with all ratios of BS to user density, but is EE-optimal only when the BS-user density ratio exceeds a threshold (i.e. in UDNs), while using orthogonal frequency band among adjacent cells is EE-optimal when the ratio of density is less than the threshold. The normalized EE gain of full frequency reuse in UDNs increases with the number of BSs. Simulation results validate our analysis and illustrate the EE gain with typical BS-user density ratios.

Millimeter wave (mmWave) massive MIMO can achieve orders of magnitude increase in spectral and energy efficiency, and it usually exploits the hybrid analog and digital precoding to overcome the serious signal attenuation induced by mmWave frequencies. However, most of hybrid precoding schemes focus on the full-array structure, which involves a high complexity. In this paper, we propose a near-optimal iterative hybrid precoding scheme based on the more realistic subarray structure with low complexity. We first decompose the complicated capacity optimization problem into a series of ones easier to be handled by considering each antenna array one by one. Then we optimize the achievable capacity of each antenna array from the first one to the last one by utilizing the idea of successive interference cancelation (SIC), which is realized in an iterative procedure that is easy to be parallelized. It is shown that the proposed hybrid precoding scheme can achieve better performance than other recently proposed hybrid precoding schemes, while it also enjoys an acceptable computational complexity.

Linear precoding techniques, such as zero forcing precoding, can achieve the near-optimal capacity due to the favorable channel propagation in large-scale MIMO systems, but involve complicated matrix inversion of large size. In this paper, we propose a low-complexity linear precoding scheme based on the Gauss-Seidel (GS) method. The proposed scheme can achieve the capacity-approaching performance of the classical linear precoding schemes in an iterative way without complicated matrix inversion, which can reduce the overall complexity by one order of magnitude. We also prove that the proposed GSbased precoding scheme has a faster convergence rate than the recently proposed Neumann-based precoding scheme. Simulation results demonstrate that the proposed scheme can achieve the exact capacity-approaching performance of the classical linear precoding schemes with only a small number of iterations.

Hybrid beamforming has drawn attention with the increasing concern on high frequency band communication and the appearance of flexible structures of base stations. This paper considers the practical transmitter structure that each antenna is only connected to a unique RF chain and optimizes the analog and digital beamforming matrices to maximize the achievable rate with transmit power constraint. Following alternating optimization principle, closed-form relationships between analog and digital precoders are obtained when both amplitude and phase or only phase can be adjusted to form analog beams. Simulation results demonstrate the advantage of our proposed methods over the existing beam steering method in terms of achievable rate with different scale antenna array. This reveals that the method can be applied to both low and high frequency band communications. When increasing the number of propagation paths, contrast to the traditional beam steering method, the performance of proposed methods becomes better while the complexity almost keeps at an acceptable constant level.

Non-orthogonal multiple access (NOMA) is expected to be a promising technique for future wireless networks due to its superior spectral efficiency. In this paper, the sum rate optimization problem for multiple-input multiple-output (MIMO) NOMA systems is studied with the total transmit power constraint and the minimum rate constraint of weak user. We first derive a channel state information (CSI) condition in which MIMO NOMA systems can achieve full rate transmission, i.e. the transmission rate of the weak user equals to the channel capacity of weak user. Based on the CSI condition, we propose an optimal power allocation scheme for MIMO NOMA systems, which can achieve the capacity region of MIMO Broadcast channel as dirty paper coding. A low complexity suboptimal scheme is proposed as well for all CSI channel conditions. Numerical results show that the proposed NOMA schemes significantly outperform the traditional time division based single user MIMO scheme and the multi-user MIMO scheme.

Mobile traffic is projected to increase 1000 times from 2010 to 2020. This poses significant challenges on the 5thgeneration (5G) wireless communication system design, including network structure, air interface, key transmission schemes, multiple access, and duplexing schemes. In this paper, full duplex (FD) is discussed, aiming to highlight some insights on various issues including deployment scenarios, frame structures, reference signals (RS), interference mitigation, transceiver structures/calibration, and extension of time division duplex (TDD)/frequency division duplex (FDD) to full duplex. It is anticipated that with future standardization and deployment of FD systems, TDD and FDD will be harmoniously integrated, supporting all the existing half duplex mobile phones efficiently, and leading to a much enhanced 5G system performance.

In this paper, by jointly considering subchannels, power, and Modulation and Coding Scheme (MCS) allocation, we address the sum-rate maximization problem in OFDMA downlink systems. We formulate the problem as an integer linear programming (ILP), which maximizes the system sum-rate subject to the minimum rate requirements of users and total transmit power constraint of base station. To solve the formulation with low complexity, we propose a two-level iterative Subchannels, Power, and MCS allocation Algorithm (SPMA) by exploiting Tabu Search (TS). At each iteration, the SPMA firstly assigns MCS to users and then allocates subchannels and power based on a SubChannels and Power allocation Algorithm (SCPA). Particularly, the SCPA maximizes the system sum-rate by first satisfying the minimum rate requirements with the least transmit power. Simulation results show that the SPMA outperforms the existing algorithms in terms of sum-rate and average rate per user, as well as demonstrate that the sum-rate is distributed flexibly among users in instantaneous channel conditions with the SPMA.

In this paper, we address the energy conservation problem with the quality of service (QoS) requirements taken into account for the physical downlink shared channel (PDSCH) in LTE/LTE-Advanced networks. By jointly allocating the modulation and coding schemes (MCS), resource blocks (RB), and power, we first propose a standards-compliant QoS-oriented power control algorithm (SQPC) for realistic systems to save energy. Specifically, with an appropriate MCS allocation, the proposed algorithm can tailor the power to match the QoS requirements. However, the SQPC saves energy at the cost of RB utilization. To this end, we further devise an Enhanced SQPC algorithm (ESQPC) to strike a balance between RB allocation and energy consumption. Finally, simulation results show that the proposed algorithms have the advantage of reducing nearly half of energy consumption compared to the existing algorithm, as well as demonstrate that the ESQPC can improve RB utilization against the SQPC.

Massive multiple-input and multiple-output (MIMO) systems have draw a lot of attention, due to its outstanding performance in energy saving and spectrum efficiency [1]. In massive MIMO systems, the intra-cell interference, uncorrelated noise and fast fading nearly vanish. Further study reveals that for multi-cell massive MIMO systems the performance will be limited by so-called pilot contamination (inter-cell interference) [2], where the transmitted CSI at one BS includes the channel coefficients from all the users using the same pilot. However, the analysis in [2] ignores a fact that the spatial/temporal characters of channel coefficients of different users are different such that they are distinguishable in certain extends. In practice, utterly isolating the user channels is extremely hard or even not feasible in certain cases. This paper firstly presents a practical temporal domain based method against pilot contamination without coordination, which just remains the strongest channel impulse responses (CIR) as the effective channel information. Simulation results show the outperformance of the proposed method with low complexity.

Energy efficiency (EE) has been recognized as another important metric as well as spectral efficiency (SE). This paper investigates the fundamental properties of the EE-SE relationship. The point-to-point MIMO transmission link is considered with different channel state information (CSI) conditions. Our analysis proves that the slope of EE-SE curves only depends on the circuit power and bandwidth when the SE approaches zero while it depends on the multiplexing gain when the SE approaches infinity. Reducing circuit power is helpful to improve EE in the low SE region while introducing more antennas benefits the EE in the high SE region. We also study the properties of EE-optimal points. It is found that reducing circuit power brings exponential EE increasing with linear SE loss at the EE-optimal points. More multiplexing gain will alleviate the speed of EE reduction with the increase of SE. More diversity gain brings a fixed EE gain.

This paper investigates effective SINR mapping (ESM) methods for link abstraction (or channel quality evaluation) used in system level performance simulation. Among all existing ESM methods, the mean mutual information per-bit (MMIB) ESM has been shown to be superior in terms of high accurate block error rate (BLER) prediction and supporting multiple modulation schemes within one data block. However, complicated multi-Gaussian distribution approximation is needed in MMIB-ESM when high modulation schemes such as 16QAM and 64QAM are considered. This paper proposes a new generalized bit-wise E-ESM (BE-ESM), which is shown to have similar performance as MMIB-ESM but with a very simple structure. Furthermore, it is also shown that BE-ESM and MMIB-ESM can be equivalent to each other in certain extents.

A new MIMO channel model based on angular-domain, double-directional channel model is proposed. Angular domain is a more appropriate coordinate to describe the MIMO channel, since the incoming rays and outgoing rays appear in clusters in the real world. This model is developed based on linear space and sampling theory and is applicable to any practical antenna geometry and includes the actual radiation pattern of the antenna array. It is compared with other existing models, including i.i.d., Kronecker, virtual channel representation and eigenbeam model, using channel estimation based model validation. Both simulation and experimental results show that it outperforms other angular-domain based models in terms of channel estimation error. A more accurate channel model not only facilitates better performance analysis, but also channel estimation in practical communication system.

The performance degradation in convolutionally coded V-BLAST system is analyzed in this paper. Compared with coded linear filtering approach, it is shown that the coded V-BLAST suffers from serious degradation due to the effect of error propagation. Considering the constraint of the system, a performance improvement method is proposed. It adjusts the weighting of soft bits, where the weighting for one stream is found based on the equivalent error rate of that stream. Additional computation needed by the proposed method is neglectable. Both theoretical analysis and simulation verify the effectiveness of the proposed method.

Based on multi-carrier techniques, the orthogonal frequency division multiplexing access (OFDMA) scheme can support multiple user access through sub-carrier allocation. The performance of OFDMA depends on the frequency selectivity of the wireless fading channel. In order to increase the frequency selectivity, a cyclic delay diversity scheme is introduced, where multiple antennas are used to create an artificial fading channel. Using a predetermined fixed cyclic delay, the original scheme is not ensured to be optimal in various circumstances. This paper presents a multi-degree cyclic delay diversity scheme where the delay values can be tailored by each individual user with the aid of feedback channel. With sub-channel-wise channel dependent user scheduling, each sub-channel will be occupied by the user who can achieve the best performance with its reported cyclic delay values. In addition, for further performance enhancement, a new transmitter structure is proposed to provide high accuracy delay control. Computer simulation results verify the effectiveness of the proposed method.

A critical factor in the deployment of spatial multiplexing multiple-input multiple-output (MIMO) systems is the cost of multiple analog transmit/receive chains at the mobile terminal side. To mitigate this problem, antenna subset selection at the transmitter/receive has been proposed. With antenna selection, a small number of analog chains are multiplexed between a larger number of transmit/receive antenna elements. In this paper, we derive simple and robust criteria to antenna selection for V-BLAST error rate performance maximization. Both cases, where channel state information (CSI) at the transmitter is available and unavailable, are studied. Simulation results show that the proposed algorithm provides a very close performance to the optimum exhaustive search performance.

Most researches into multiple-input multiple-output (MIMO) antenna systems have aimed to determine the capacity-achieving input covariance given certain degree of channel state information (CSI) at the transmitter side. In practice, however, it is much preferred to find the input signal covariance that requires the least average transmit power to support a given rate based on only long-term channel information. In this paper, we analyze the characteristics of the optimal (in the sense of power-saving) input covariance for spatially correlated MIMO channels where only the long-term (slow-varying) channel spatial covariance information is available at the transmitter. Sufficient and necessary conditions of the optimal input covariance are derived. By considering the large-system regimes, we devise an efficient iterative algorithm to compute the asymptotic optimal power-saving input covariance. Remarkably, simulation results will demonstrate that the asymptotic solution is very effective in that it gives promising results even for MIMO systems with only a few antennas at the transmitter and the receiver.

This paper presents a novel self-organizing distributed algorithm for finding a broadcasting schedule in a packet radio network via only local collaborative interactions among neighboring network stations. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, we transform the broadcast scheduling problem (BSP) into an LDPC-like problem through a factor graph. In the proposed algorithm, the constraint rules of the BSP are divided into many simple local rules, each of which is enforced by a local processing unit in the factor graph. The soft-information, describing the probability that each station will transmit a data packet, is then efficiently exchanged among the local processing units by using the sum-product algorithm to iteratively optimize the broadcasting schedule. Simulation results indicate that the proposed algorithm performs better than the other existing central-processing algorithms in terms of the channel utilization and the average packet delay. This is true especially when the network scenario is very complex. Furthermore, the proposed algorithm is both low in complexity and completely distributed, which makes it suitable for implementation in practical network applications.

In this paper, we derive an analytical bit-error-rate (BER) formula for evaluating the system performance of MSTS (modified space-time spreading) code with space-time diversity for cellular CDMA systems. The BER analysis for the MSTS is carried out for a multiple transmit and receive antenna system (also known as a MIMO system) in Ricean channels. For MIMO Ricean channels, the analytical BER is derived as function of angle spread, Doppler spread, and the Ricean K-factor. The BER results show that MSTS exploits spatial diversity and temporal diversity in time-varying MIMO channels and is compared to existing STS (space-time spreading) scheme. The results also show that the MSTS outperforms STS in a high mobility cellular environment.

The IS-95 downlink channel incorporates a pilot signal to aid the mobile receiver in obtaining coherent channel estimates for use in the RAKE receiver. We propose a simple, recursive multipath noise cancellation (MNC) detector which uses an on/off switch mechanism for controlling the noise cancellation for each RAKE finger. We derive the switch mechanism which minimizes the mean-squared error (MSE) at the RAKE output. This switched-MNC detector, when compared with the conventional RAKE receiver, demonstrates significant performance gains, increasing the downlink capacity of a typical IS-95 system by up to 10%, while requiring minimal additional complexity when implemented using a simple recursive structure for the MNC function. Furthermore, we observe that almost all of the performance gains of the MMSE switched-MNC detector can be achieved using the simpler, ad-hoc, "always-on" MNC detector, in which pilot multipath noise cancellation always occurs at each finger input without regard to minimizing the MSE.

Future microcellular personal communications systems (PCSs) will be characterized by high user density and high mobility. It is expected that registrations will incur a large amount of the radio link signaling traffic. A profile-based strategy (PBS) is proposed to reduce the signaling traffic on the radio link by increasing the intelligence within the fixed network. The system maintains a sequential list of the most likely places where each user is located. The list is ranked from the most to the least likely place where a user is found. When a call arrives for a mobile, it is paged sequentially in each location within the list. When a user moves between location areas in this list, no location update is required. The list may be provided by the user or may be based on each user's past calling history. The method for doing this is outside the scope of this work. This work focuses on the potential performance improvements that can result from maintaining such a list. This paper compares the performance of the proposed strategy to the typical geographic-based location-tracking schemes being implemented in evolving digital cellular and cordless standards. Key performance measures for the comparison are radio bandwidth, fixed network SS7 traffic, and call setup delay. We investigate the conditions under which the PBS performs better than the traditional scheme. Results indicate that over a wide range of parameters, it may be possible to reduce both the radio bandwidth and fixed network signaling load for a modest increase in call setup delay.

The increased demand for wireless mobile communications coupled with the finite available spectrum has motivated investigation into alternative methods of tracking users and delivering calls. We present a new scheme for delivering mobile terminated calls named reverse virtual call setup (RVC). Only a few new fixed network SS7 signaling messages are needed to implement this protocol; we specify them here. RVC can function within the existing cellular paging network or with an integrated overlaid paging network. The relative performance of RVC for both scenarios is investigated.

Future microcellular personal communications systems (PCS) will be characterized by high user density and high mobility. It is expected that registrations will incur a large amount of the radio link signaling traffic. A profile-based strategy (PBS) is proposed to reduce the signaling traffic on the radio link by increasing the intelligence within the fixed network. The fixed network performance of the PBS was investigated previously. This paper compares its radio bandwidth and call setup delay performance to the typical geographic based location tracking schemes in evolving digital cellular and cordless standards. We investigate the conditions under which the PBS performs better than the traditional scheme. Results of this work together with the previous work indicate that over a wide range of parameters it may be possible to reduce both the radio bandwidth and fixed network signaling load for a modest increase in call setup delay.

Dynamic channel allocation (DCA) has become an important subject of research and development for wireless/cellular networks. Various DCA schemes have been proposed and numerous studies on their performance have been made. However, the DCA investigations so far have all been done under the constraints imposed by co-channel interference only. Another important constraint which is crucial in practical systems, namely, the channel usage limited by adjacent channel interference (ACI), has mostly been overlooked in the literature. The authors analyze the ACI constraints under various spatial relationships between the mobile and base stations. They also make the first attempt to investigate the impact of this constraint on DCA by devising a modified version of a novel and practical distributed DCA algorithm, the local packing DDCA (LP-DDCA), incoporating the ACI constraint. Then, the performance improvement offered by the modified LP-DDCA is re-evaluated. The results show that although the ACI constraint may significantly reduce the traffic adaptation capability of DCA in general, the LP-DDCA is quite robust with respect to this additional constraint.

Future high density wireless communication systems will place large demands upon the signaling channel between the mobile terminals and the fixed network infrastructure. One such signaling channel protocol is resource auction multiple access (RAMA). This paper evaluates the performance of a low complexity extension to RAMA called tree-search RAMA (TRAMA). TRAMA employs a close variant of a classic tree search mechanism to more effectively utilize a given bandwidth. We derive bounds on the delay-throughput characteristics of TRAMA. Under heavy traffic conditions where RAMA is unusable TRAMA is able to operate with finite average delay. For light to moderate traffic the average delay performance is only slightly better for TRAMA. However TRAMA operates with a lower variance of delay. Furthermore, the variance decreases when the average offered traffic increases. We show that this implies that TRAMA is less sensitive to the spatial and temporal variability of a mobile wireless environment.>

pg 100 diversity coding, as introduced in Ayanoglu et al. (1990), is a method of protection against failures in a communication network or a storage system, which is based on introducing a digital error-correcting code across independent links. This technique makes efficient use of the extra network capacity needed for coding and has the additional advantages of being nearly instantaneous, not requiring a feedback channel, rerouting, or resynchronization. In high-speed (multi Gbps) networks, digital coding will be difficult to implement, and the purpose of the present paper is to demonstrate how diversity coding may be implemented in the analog domain using the discrete fourier transform (DFT). In particular, the authors show that the DFT is a continuous-amplitude maximum-distance separable code over the field of complex numbers when the transform kernel is a prime root of unity. This code can be used to generate self-healing or fault-tolerant communication networks for continuous- or discrete-amplitude signals, as long as continuous-amplitude parity channels are available. The authors describe electrical and optoelectronic implementations, and a signal estimation approach to combat channel noise and thereby improve the performance of the analog diversity coding system. The most important advantage of this technique is in greatly simplifying the encoders and decoders of diversity coding systems for high-speed networks, such as fiber-optic wavelength division multiplexed networks. Application of analog diversity coding to systems with analog sources, such as telemetry systems is also possible.>

We determine the call blocking performance of channel-allocation algorithms where every channel is available for use in every cell and where decisions are made by mobiles/portables based only on local observations. Using a novel Erlang-B approximation method, together with simulation, we demonstrate that even the simplest algorithm, the timid, compares favorably with impractical, centrally administered fixed channel allocation. Our results suggest that an aggressive algorithm, that is, one requiring call reconfigurations, could provide a substantially reduced blocking probability. We also present some algorithms which take major steps toward achieving the excellent blocking performance of the hypothetical aggressive algorithm but having the stability of the timid algorithm.>

Th authors explore the use of a wireless network having a two-tier architecture to serve both conventional mobile subscribers and quasi-stationary (e.g., PCN (personal communications network)) subscribers. The latter are served by microcells which are embedded within macrocells that serve the mobile users. This provides a balance between maximizing the number of users per unit area (which favors small cells) and minimizing the network control associated with handoff (which favors large cells). Four approaches to sharing the spectrum between the two tiers, using per-cell capacity as the measure, are evaluated. The first two feature spread-spectrum sharing, i.e., they use TDMA (time-division multiple access) among microcell users and CDMA (code-division multiple access) among macrocell users (System I), or vice versa (System II). The other two approaches feature orthogonal sharing, i.e., they use TDMA in both tiers, with time slots (System III) or frequency channels (System IV) partitioned so there is no overlap between tiers. Analysis shows that the capacity tradeoffs are poor for Systems I and II because of the large amounts of cross-tier interference: and that System IV gives the best capacity tradeoffs.>

A channel coding approach called diversity coding is introduced for self-healing and fault-tolerance in digital communication networks for nearly instantaneous recovery from link failures. To achieve this goal, the problem of link failures is treated as an erasure channel problem. Implementation details of this technique in existing and future communication networks are discussed.>

The performance of a growable architecture for broadband asynchronous transfer mode (ATM) switching consisting of a memoryless self-routing interconnect fabric and modest-size packet switch modules is examined. The cell loss probability is the focus because the architecture attains the best possible delay-throughput performance if the packet switch modules use output queuing. There are two sources of cell loss in the switch. First, cells are dropped if too many simultaneous arrivals are destined to a group of output ports. Second, because a simple, distributed path-assignment controller is used for speed and efficiency, cells are dropped when the controller cannot schedule a path through the switch. The authors compute an upper bound on arrivals, possibly including isochronous circuit connections, and show that both sources of cell loss can be made negligibly small.>

Techniques are presented that can cancel ghosts in received analog TV (for IDTV, EDTV, and HDTV) signals. The authors utilize the fact that there are short periods of time without the analog signal (the horizontal flyback interval between the lines) to periodically cleanse a finite impulse response (FIR) or an infinite impulse response (IIR) equalizer. This line-by-line processing (cleansing) overcomes the limitations of standard equalizers to allow adequate suppression of ghosts, even with nulls in the spectrum.>

Techniques that can cancel ghosts in received analog TV (for improved-definition TV, extended-definition TV, and high-definition TV) signals are presented. The fact that there are short periods of time without the analog signal (the horizontal flyback interval between the lines) is utilized to periodically cleanse a finite impulse response (FIR) or an infinite impulse response (IIR) equalizer. This line-by-line processing (cleansing) overcomes the limitation of standard equalizers to allow for 40-50 dB of suppression of ghosts, even with nulls in the spectrum, as long as the ghost delay is less than the period of time without the analog signal. Furthermore, by using time inversion in combination with line-by-line processing, the stability problem of the conventional IIR equalizer can be eliminated. It is shown that it may be possible to implement this IIR equalizer on a single digital integrated circuit. Alternatively, an FIR equalizer can be used; although it requires multiple chips (i.e. more taps), it can acquire and adapt to the ghosted channel more rapidly than an IIR equalizer.>

An error-control-based approach, called diversity coding, that provides nearly instantaneous self-healing digital communication networks is presented. This is achieved by constructing an error-correcting code across logically independent channels and by treating link failures within the framework of an erasure channel model. Diversity coding is more efficient than previous approaches to self-healing communication networks since it is nearly instantaneous, is transparent to the end user, minimizes the required extra capacity, and does not need rerouting, resynchronization, or a feedback channel. It is applicable to both circuit-switched and packet-switched networks and to a wide variety of network topologies. Diversity coding can be extended to provide protection from short-duration environmental disruptions, such as multipath fading in radio networks and polarization dispersion in fiber-optic networks, or, in conjunction with previous error detection schemes, to provide forward error correction for random and burst errors.>