Showing papers on "Channel state information published in 2017"

Cell-Free Massive MIMO Versus Small Cells

Abstract: A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs), which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max–min power control algorithms. Max–min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly fivefold improvement in 95%-likely per-user throughput over the small-cell scheme, and tenfold improvement when shadow fading is correlated.

Hybrid Beamforming for Massive MIMO: A Survey

Abstract: Hybrid multiple-antenna transceivers, which combine large-dimensional analog pre/postprocessing with lower-dimensional digital processing, are the most promising approach for reducing the hardware cost and training overhead in massive MIMO systems. This article provides a comprehensive survey of the various incarnations of such structures that have been proposed in the literature. We provide a taxonomy in terms of the required channel state information, that is, whether the processing adapts to the instantaneous or average (second-order) channel state information; while the former provides somewhat better signal- to-noise and interference ratio, the latter has much lower overhead for CSI acquisition. We furthermore distinguish hardware structures of different complexities. Finally, we point out the special design aspects for operation at millimeter-wave frequencies.

Throughput Analysis of Massive MIMO Uplink With Low-Resolution ADCs

Abstract: We investigate the uplink throughput achievable by a multiple-user (MU) massive multiple-input multiple-output (MIMO) system, in which the base station is equipped with a large number of low-resolution analog-to-digital converters (ADCs). Our focus is on the case where neither the transmitter nor the receiver have any a priori channel state information. This implies that the fading realizations have to be learned through pilot transmission followed by channel estimation at the receiver, based on coarsely quantized observations. We propose a novel channel estimator, based on Bussgang’s decomposition, and a novel approximation to the rate achievable with finite-resolution ADCs, both for the case of finite-cardinality constellations and of Gaussian inputs, that is accurate for a broad range of system parameters. Through numerical results, we illustrate that, for the 1-bit quantized case, pilot-based channel estimation together with maximal-ratio combing, or zero-forcing detection enables reliable multi-user communication with high-order constellations, in spite of the severe nonlinearity introduced by the ADCs. Furthermore, we show that the rate achievable in the infinite-resolution (no quantization) case can be approached using ADCs with only a few bits of resolution. We finally investigate the robustness of low-ADC-resolution MU-MIMO uplink against receive power imbalances between the different users, caused for example by imperfect power control.

Robust Resource Allocation for MIMO Wireless Powered Communication Networks Based on a Non-Linear EH Model

Abstract: In this paper, we consider a multiple-input multiple-output wireless powered communication network, where multiple users harvest energy from a dedicated power station in order to be able to transmit their information signals to an information receiving station. Employing a practical non-linear energy harvesting (EH) model, we propose a joint time allocation and power control scheme, which takes into account the uncertainty regarding the channel state information (CSI) and provides robustness against imperfect CSI knowledge. In particular, we formulate two non-convex optimization problems for different objectives, namely system sum throughput maximization and the maximization of the minimum individual throughput across all wireless powered users. To overcome the non-convexity, we apply several transformations along with a one-dimensional search to obtain an efficient resource allocation algorithm. Numerical results reveal that a significant performance gain can be achieved when the resource allocation is designed based on the adopted non-linear EH model instead of the conventional linear EH model. Besides, unlike a non-robust baseline scheme designed for perfect CSI, the proposed resource allocation schemes are shown to be robust against imperfect CSI knowledge.

A Survey on Behavior Recognition Using WiFi Channel State Information

Abstract: In this article, we present a survey of recent advances in passive human behavior recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. The movement of the human body parts cause changes in the wireless signal reflections, which result in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behavior can be recognized. This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers. The techniques from the literature that are presented herein have great performance; however, instead of the machine learning techniques employed in these works, we propose to use deep learning techniques such as long-short term memory (LSTM) recurrent neural networking (RNN) and show the improved performance. We also discuss different challenges such as environment change, frame rate selection, and the multi-user scenario; and finally suggest possible directions for future work.

Joint User Scheduling and Power Allocation Optimization for Energy-Efficient NOMA Systems With Imperfect CSI

Abstract: Non-orthogonal multiple access (NOMA) exploits successive interference cancellation technique at the receivers to improve the spectral efficiency. By using this technique, multiple users can be multiplexed on the same subchannel to achieve high sum rate. Most previous research works on NOMA systems assume perfect channel state information (CSI). However, in this paper, we investigate energy efficiency improvement for a downlink NOMA single-cell network by considering imperfect CSI. The energy efficient resource scheduling problem is formulated as a non-convex optimization problem with the constraints of outage probability limit, the maximum power of the system, the minimum user data rate, and the maximum number of multiplexed users sharing the same subchannel. Different from previous works, the maximum number of multiplexed users can be greater than two, and the imperfect CSI is first studied for resource allocation in NOMA. To efficiently solve this problem, the probabilistic mixed problem is first transformed into a non-probabilistic problem. An iterative algorithm for user scheduling and power allocation is proposed to maximize the system energy efficiency. The optimal user scheduling based on exhaustive search serves as a system performance benchmark, but it has high computational complexity. To balance the system performance and the computational complexity, a new suboptimal user scheduling scheme is proposed to schedule users on different subchannels. Based on the user scheduling scheme, the optimal power allocation expression is derived by the Lagrange approach. By transforming the fractional-form problem into an equivalent subtractive-form optimization problem, an iterative power allocation algorithm is proposed to maximize the system energy efficiency. Simulation results demonstrate that the proposed user scheduling algorithm closely attains the optimal performance.

Noncoherent Detections for Ambient Backscatter System

Abstract: Ambient backscatter, an emerging communication mechanism where battery-free devices communicate with each other via backscattering ambient radio frequency (RF) signals, has achieved much attention recently because of its desirable application prospects in the Internet of Things. In this paper, we formulate a practical transmission model for an ambient backscatter system, where a tag wishes to send some low-rate messages to a reader with the help of an ambient RF signal source, and then provide fundamental studies of noncoherent symbol detection when all channel state information of the system is unknown. For the first time, a maximum likelihood detector is derived based on the joint probability density function of received signal vectors. In order to ease availability of prior knowledge of the ambient RF signal and reduce computational complexity of the algorithm, we design a joint-energy detector and derive its corresponding detection threshold. The analytical bit error rate (BER) and BER-based outage probability are also obtained in a closed form, which helps with designing system parameters. An estimation method to obtain detection-required parameters and comparison of computational complexity of the detectors are presented as complementary discussions. Simulation results are provided to corroborate theoretical studies.

NOMA-Based Random Access With Multichannel ALOHA

Abstract: In nonorthogonal multiple access (NOMA), the power difference of multiple signals is exploited for multiple access and successive interference cancellation is employed at a receiver to mitigate co-channel interference. Thus, NOMA is usually employed for coordinated transmissions and mostly applied to downlink transmissions where a base station performs coordination for downlink transmissions with full channel state information. In this paper, however, we show that NOMA can also be employed for non-coordinated transmissions such as random access for uplink transmissions. We apply a NOMA scheme to multichannel ALOHA and show that the throughput can be improved. In particular, the resulting scheme is suitable for random access when the number of subchannels is limited since NOMA can effectively increase the number of subchannels without any bandwidth expansion.

Optimal Resource Allocation for Power-Efficient MC-NOMA With Imperfect Channel State Information

Abstract: In this paper, we study power-efficient resource allocation for multicarrier non-orthogonal multiple access systems. The resource allocation algorithm design is formulated as a non-convex optimization problem which jointly designs the power allocation, rate allocation, user scheduling, and successive interference cancellation (SIC) decoding policy for minimizing the total transmit power. The proposed framework takes into account the imperfection of channel state information at transmitter and quality of service requirements of users. To facilitate the design of optimal SIC decoding policy on each subcarrier, we define a channel-to-noise ratio outage threshold . Subsequently, the considered non-convex optimization problem is recast as a generalized linear multiplicative programming problem, for which a globally optimal solution is obtained via employing the branch-and-bound approach. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. To strike a balance between system performance and computational complexity, we propose a suboptimal iterative resource allocation algorithm based on difference of convex programming. Simulation results demonstrate that the suboptimal scheme achieves a close-to-optimal performance. Also, both proposed schemes provide significant transmit power savings than that of conventional orthogonal multiple access schemes.

Design of Cooperative Non-Orthogonal Multicast Cognitive Multiple Access for 5G Systems: User Scheduling and Performance Analysis

Abstract: Non-orthogonal multiple access (NOMA) is emerging as a promising, yet challenging, multiple access technology to improve spectrum utilization for the fifth generation (5G) wireless networks. In this paper, the application of NOMA to multicast cognitive radio networks (termed as MCR-NOMA) is investigated. A dynamic cooperative MCR-NOMA scheme is proposed, where the multicast secondary users serve as relays to improve the performance of both primary and secondary networks. Based on the available channel state information (CSI), three different secondary user scheduling strategies for the cooperative MCR-NOMA scheme are presented. To evaluate the system performance, we derive the closed-form expressions of the outage probability and diversity order for both networks. Furthermore, we introduce a new metric, referred to as mutual outage probability to characterize the cooperation benefit compared to non-cooperative MCR-NOMA scheme. Simulation results demonstrate significant performance gains are obtained for both networks, thanks to the use of our proposed cooperative MCR-NOMA scheme. It is also demonstrated that higher spatial diversity order can be achieved by opportunistically utilizing the CSI available for the secondary user scheduling.

Deep Learning Based MIMO Communications.

Abstract: We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder. This method extends prior work on the joint optimization of physical layer representation and encoding and decoding processes as a single end-to-end task by expanding transmitter and receivers to the multi-antenna case. We introduce a widely used domain appropriate wireless channel impairment model (Rayleigh fading channel), into the autoencoder optimization problem in order to directly learn a system which optimizes for it. We considered both spatial diversity and spatial multiplexing techniques in our implementation. Our deep learning-based approach demonstrates significant potential for learning schemes which approach and exceed the performance of the methods which are widely used in existing wireless MIMO systems. We discuss how the proposed scheme can be easily adapted for open-loop and closed-loop operation in spatial diversity and multiplexing modes and extended use with only compact binary channel state information (CSI) as feedback.

Rate-Splitting Multiple Access for Downlink Communication Systems: Bridging, Generalizing and Outperforming SDMA and NOMA

Abstract: Space-Division Multiple Access (SDMA) utilizes linear precoding to separate users in the spatial domain and relies on fully treating any residual multi-user interference as noise Non-Orthogonal Multiple Access (NOMA) uses linearly precoded superposition coding with successive interference cancellation (SIC) and relies on user grouping and ordering to enforce some users to fully decode and cancel interference created by other users In this paper, we argue that to efficiently cope with the high throughput, heterogeneity of Quality-of-Service (QoS), and massive connectivity requirements of future multi-antenna wireless networks, multiple access design needs to depart from SDMA and NOMA We develop a novel multiple access framework, called Rate-Splitting Multiple Access (RSMA) RSMA is a more general and powerful multiple access for downlink multi-antenna systems that contains SDMA and NOMA as special cases RSMA relies on linearly precoded rate-splitting with SIC to decode part of the interference and treat the remaining part of the interference as noise This capability of RSMA to partially decode interference and partially treat interference as noise enables to softly bridge the two extremes of fully decoding interference and treating interference as noise, and provide room for rate and QoS enhancements, and complexity reduction The three multiple access schemes are compared and extensive numerical results show that RSMA provides a smooth transition between SDMA and NOMA and outperforms them both in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel directions, channel strengths and qualities of Channel State Information at the Transmitter) Moreover, RSMA provides rate and QoS enhancements over NOMA at a lower computational complexity for the transmit scheduler and the receivers (number of SIC layers)

On the Total Energy Efficiency of Cell-Free Massive MIMO

Abstract: We consider the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-antenna users in the same time-frequency resource. A simple (distributed) conjugate beamforming scheme is applied at each AP via the use of local channel state information (CSI). This CSI is acquired through time-division duplex operation and the reception of uplink training signals transmitted by the users. We derive a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control. This closed-form result enables us to analyze the effects of backhaul power consumption, the number of APs, and the number of antennas per AP on the total energy efficiency, as well as, to design an optimal power allocation algorithm. The optimal power allocation algorithm aims at maximizing the total energy efficiency, subject to a per-user spectral efficiency constraint and a per-AP power constraint. Compared with the equal power control, our proposed power allocation scheme can double the total energy efficiency. Furthermore, we propose AP selections schemes, in which each user chooses a subset of APs, to reduce the power consumption caused by the backhaul links. With our proposed AP selection schemes, the total energy efficiency increases significantly, especially for large numbers of APs. Moreover, under a requirement of good quality-of-service for all users, cell-free massive MIMO outperforms the colocated counterpart in terms of energy efficiency.

CSI-Based Device-Free Wireless Localization and Activity Recognition Using Radio Image Features

Abstract: Device-free wireless localization and activity recognition is an emerging technique, which could estimate the location and activity of a person without equipping him/her with any device. It deduces the state of a person by analyzing his/her influence on surrounding wireless signals. Therefore, how to characterize the influence of human behaviors is the key question. In this paper, we explore and exploit a radio image processing approach to better characterize the influence of human behaviors on Wi-Fi signals. Traditional methods deal with channel state information (CSI) measurements on each channel independently. However, CSI measurements on different channels are correlated, and thus lots of useful information involved with channel correlation may be lost. This motivates us to look on CSI measurements from multiple channels as a radio image and deal with it from the two-dimensional perspective. Specifically, we transform CSI measurements from multiple channels into a radio image, extract color and texture features from the radio image, adopt a deep learning network to learn optimized deep features from image features, and estimate the location and activity of a person using a machine learning approach. Benefits from the informative and discriminative deep image features and experimental results in two clutter laboratories confirm the excellent performance of the proposed system.

Random Beamforming in Millimeter-Wave NOMA Networks

Abstract: This paper investigates the coexistence between two key enabling technologies for fifth generation (5G) mobile networks, non-orthogonal multiple access (NOMA), and millimeter-wave (mmWave) communications. Particularly, the application of random beamforming to mmWave-NOMA systems is considered in order to avoid the requirement that the base station know all the users’ channel state information. Stochastic geometry is used to characterize the performance of the proposed mmWave-NOMA transmission scheme by using key features of mmWave systems, i.e., that mmWave transmission is highly directional and potential blockages will thin the user distribution. Two random beamforming approaches that can further reduce the system overhead are also proposed, and their performance is studied analytically in terms of sum rates and outage probabilities. Simulation results are also provided to demonstrate the performance of the proposed schemes and verify the accuracy of the developed analytical results.

Codebook-Based Hybrid Precoding for Millimeter Wave Multiuser Systems

Abstract: In millimeter-wave (mmWave) systems, antenna architecture limitations make it difficult to apply conventional fully digital precoding techniques but call for low-cost analog radio frequency (RF) and digital baseband hybrid precoding methods. This paper investigates joint RF-baseband hybrid precoding for the downlink of multiuser multiantenna mmWave systems with a limited number of RF chains. Two performance measures, maximizing the spectral efficiency and the energy efficiency of the system, are considered. We propose a codebook-based RF precoding design and obtain the channel state information via a beam sweep procedure. Via the codebook-based design, the original system is transformed into a virtual multiuser downlink system with the RF chain constraint. Consequently, we are able to simplify the complicated hybrid precoding optimization problems to joint codeword selection and precoder design (JWSPD) problems. Then, we propose efficient methods to address the JWSPD problems and jointly optimize the RF and baseband precoders under the two performance measures. Finally, extensive numerical results are provided to validate the effectiveness of the proposed hybrid precoders.

TensorBeat: Tensor Decomposition for Monitoring Multiperson Breathing Beats with Commodity WiFi

Abstract: Breathing signal monitoring can provide important clues for health problems. Compared to existing techniques that require wearable devices and special equipment, a more desirable approach is to provide contact-free and long-term breathing rate monitoring by exploiting wireless signals. In this article, we propose TensorBeat, a system to employ channel state information (CSI) phase difference data to intelligently estimate breathing rates for multiple persons with commodity WiFi devices. The main idea is to leverage the tensor decomposition technique to handle the CSI phase difference data. The proposed TensorBeat scheme first obtains CSI phase difference data between pairs of antennas at the WiFi receiver to create CSI tensors. Then canonical polyadic (CP) decomposition is applied to obtain the desired breathing signals. A stable signal matching algorithm is developed to identify the decomposed signal pairs, and a peak detection method is applied to estimate the breathing rates for multiple persons. Our experimental study shows that TensorBeat can achieve high accuracy under different environments for multiperson breathing rate monitoring.

Optimal Resource Allocation for Power-Efficient MC-NOMA with Imperfect Channel State Information

Abstract: In this paper, we study power-efficient resource allocation for multicarrier non-orthogonal multiple access (MC-NOMA) systems The resource allocation algorithm design is formulated as a non-convex optimization problem which jointly designs the power allocation, rate allocation, user scheduling, and successive interference cancellation (SIC) decoding policy for minimizing the total transmit power The proposed framework takes into account the imperfection of channel state information at transmitter (CSIT) and quality of service (QoS) requirements of users To facilitate the design of optimal SIC decoding policy on each subcarrier, we define a channel-to-noise ratio outage threshold Subsequently, the considered non-convex optimization problem is recast as a generalized linear multiplicative programming problem, for which a globally optimal solution is obtained via employing the branch-and-bound approach The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity To strike a balance between system performance and computational complexity, we propose a suboptimal iterative resource allocation algorithm based on difference of convex programming Simulation results demonstrate that the suboptimal scheme achieves a close-to-optimal performance Also, both proposed schemes provide significant transmit power savings than that of conventional orthogonal multiple access (OMA) schemes

Millimeter-Wave V2V Communications: Distributed Association and Beam Alignment

Abstract: Recently, millimeter-wave (mmWave) bands have been postulated as a means to accommodate the foreseen extreme bandwidth demands in vehicular communications, which result from the dissemination of sensory data to nearby vehicles for enhanced environmental awareness and improved safety level. However, the literature is particularly scarce in regards to principled resource allocation schemes that deal with the challenging radio conditions posed by the high mobility of vehicular scenarios. In this paper, we propose a novel framework that blends together matching theory and swarm intelligence to dynamically and efficiently pair vehicles and optimize both transmission and reception beamwidths. This is done by jointly considering channel state information and queue state information when establishing vehicle-to-vehicle (V2V) links. To validate the proposed framework, simulation results are presented and discussed, where the throughput performance as well as the latency/reliability tradeoffs of the proposed approach are assessed and compared with several baseline approaches recently proposed in the literature. The results obtained in this paper show performance gains of 25% in reliability and delay for ultra-dense vehicular scenarios with 50% more active V2V links than the baselines. These results shed light on the operational limits and practical feasibility of mmWave bands, as a viable radio access solution for future high-rate V2V communications.

On the Performance of Visible Light Communication Systems With Non-Orthogonal Multiple Access

Abstract: Visible light communication (VLC) has been proposed as a promising and efficient solution to indoor ubiquitous broadband connectivity. In this paper, non-orthogonal multiple access, which has been recently introduced as an effective scheme for fifth generation (5G) wireless networks, is considered in the context of VLC systems under different channel uncertainty models. To this end, we first derive a novel closed-form expression for the bit-error-rate (BER) under perfect channel state information (CSI). Capitalizing on this, we then quantify the effect of noisy and outdated CSI by deriving a simple and accurate approximation for the former and a tight upper bound for the latter. The offered results are corroborated by respective results from extensive Monte Carlo simulations and assist in developing useful insights on the effect of imperfect CSI knowledge on the overall system performance. Furthermore, it was shown that while noisy CSI leads to slight degradation in the BER performance, outdated CSI can cause considerable performance degradation, if the order of the users’ channel gains change due to the involved mobility.

On Secure NOMA Systems With Transmit Antenna Selection Schemes

Abstract: This paper investigates the secrecy performance of a two-user downlink non-orthogonal multiple access systems. Both single-input and single-output and multiple-input and single-output systems with different transmit antenna selection (TAS) strategies are considered. Depending on whether the base station has the global channel state information of both the main and wiretap channels, the exact closed-form expressions for the secrecy outage probability (SOP) with suboptimal antenna selection and optimal antenna selection schemes are obtained and compared with the traditional space-time transmission scheme. To obtain further insights, the asymptotic analysis of the SOP in high average channel power gains regime is presented and it is found that the secrecy diversity order for all the TAS schemes with fixed power allocation is zero. Furthermore, an effective power allocation scheme is proposed to obtain the non-zero diversity order with all the TAS schemes. Monte Carlo simulations are performed to verify the proposed analytical results.

Proactive Eavesdropping via Cognitive Jamming in Fading Channels

Abstract: To enhance the national security, there is a growing need for authorized parties to legitimately monitor suspicious communication links for preventing intended crimes and terror attacks. In this paper, we propose a new wireless information surveillance paradigm by investigating a scenario, where a legitimate monitor aims to intercept a suspicious wireless link over fading channels. The legitimate monitor can successfully eavesdrop (decode) the information of the suspicious link at each fading state only when its achievable data rate is no smaller than that at the suspicious receiver. We propose a new approach, namely, proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver in a full-duplex mode so as to change the suspicious communication (e.g., to a smaller data rate) for overhearing more efficiently. By assuming perfect self-interference cancelation (SIC) and global channel state information (CSI) at the legitimate monitor, we characterize the fundamental information-theoretic limits of proactive eavesdropping. We consider both delay-sensitive and delay-tolerant applications for the suspicious communication, under which the legitimate monitor maximizes the eavesdropping non-outage probability (for event-based monitoring) and the relative eavesdropping rate (for content analysis), respectively, by optimizing the jamming power allocation over different fading states subject to an average power constraint. Numerical results show that the proposed proactive eavesdropping via cognitive jamming approach greatly outperforms other benchmark schemes. Furthermore, by extending to a more practical scenario with residual SI and local CSI, we design an efficient online cognitive jamming scheme inspired by the optimal cognitive jamming with perfect SIC and global CSI.

Wireless Energy Harvesting Using Signals From Multiple Fading Channels

Abstract: In this paper, we study the average, the probability density function, and the cumulative distribution function of the harvested power. The signals are transmitted from multiple sources. The channels are assumed to be either Rician fading or Gamma-shadowed Rician fading. The received signals are then harvested by using either a single harvester for simultaneous transmissions or multiple harvesters for transmissions at different frequencies, antennas or time slots. Both linear and nonlinear models for the energy harvester at the receiver are examined. Numerical results are presented to show that, when a large amount of harvested power is required, a single harvester or the linear range of a practical nonlinear harvester are more efficient, to avoid power outage. Further, the power transfer strategy can be optimized for fixed total power. Specifically, for Rayleigh fading, the optimal strategy is to put the total power at the source with the best channel condition and switch off all other sources, while for general Rician fading, the optimum magnitudes and phases of the transmitting waveforms depend on the channel parameters.

Spectrum and Power Allocation for Vehicular Communications With Delayed CSI Feedback

Abstract: Channel state information (CSI) at the base station (BS) is critical to resource allocation design for wireless networks, but it is hard to obtain accurate CSI in a high mobility vehicular environment. In this letter, we study the spectrum and power allocation problem in device-to-device-enabled vehicular networks, where CSI of vehicular links is only reported to the BS periodically. We maximize the sum throughput of all vehicle-to-infrastructure (V2I) links while guaranteeing the reliability of each vehicle-to-vehicle (V2V) link with the delayed CSI feedback. We propose a low-complexity algorithm to find the optimal spectrum sharing strategy among V2I and V2V links and properly adjust their transmit powers. Its desirable performance is confirmed by computer simulation.

Massive MIMO Performance With Imperfect Channel Reciprocity and Channel Estimation Error

Abstract: Channel reciprocity in time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems can be exploited to reduce the overhead required for the acquisition of channel state information (CSI). However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. This can result in a significant degradation in the performance of linear precoding schemes, which are sensitive to the accuracy of the CSI. In this paper, we model and analyse the impact of RF mismatches on the performance of linear precoding in a TDD multi-user massive MIMO system, by taking the channel estimation error into considerations. We use the truncated Gaussian distribution to model the RF mismatch, and derive closed-form expressions of the output signal-to-interference-plus-noise ratio for maximum ratio transmission and zero forcing precoders. We further investigate the asymptotic performance of the derived expressions, to provide valuable insights into the practical system designs, including useful guidelines for the selection of the effective precoding schemes. Simulation results are presented to demonstrate the validity and accuracy of the proposed analytical results.

Secrecy Outage Performance of Transmit Antenna Selection for MIMO Underlay Cognitive Radio Systems Over Nakagami- $m$ Channels

Abstract: This paper considers a multiple-input–multiple-output (MIMO) cognitive wiretap system over Nakagami- $m$ channels with generalized selection combining (GSC), where confidential messages transmitted from a multiple-antenna transmitter to a multiple-antenna legitimate receiver are overheard by a multiple-antenna eavesdropper. Depending on whether the source node has the global channel state information (CSI) of both the main and wiretap channels, we investigate the secrecy outage performances of optimal antenna selection (OAS) and suboptimal antenna selection (SAS) schemes for MIMO underlay cognitive radio systems over Nakagami- $m$ channels, and we compare them with the space-time transmission (STT) scheme. The closed-form expressions for the exact and asymptotic secrecy outage probability (SOP) for various schemes are derived. Simulations are conducted to validate the accuracy of our derived analytical results.

The Fluctuating Two-Ray Fading Model: Statistical Characterization and Performance Analysis

Abstract: We introduce the fluctuating two-ray (FTR) fading model, a new statistical channel model that consists of two fluctuating specular components with random phases plus a diffuse component. The FTR model arises as the natural generalization of the two-wave with diffuse power (TWDP) fading model; this generalization allows its two specular components to exhibit a random amplitude fluctuation. Unlike the TWDP model, all the chief probability functions of the FTR fading model (PDF, CDF, and MGF) are expressed in closed-form, having a functional form similar to other state-of-the-art fading models. We also provide approximate closed-form expressions for the PDF and CDF in terms of a finite number of elementary functions, which allow for a simple evaluation of these statistics to an arbitrary level of precision. We show that the FTR fading model provides a much better fit than Rician fading for recent small-scale fading measurements in 28 GHz outdoor mm-wave channels. Finally, the performance of wireless communication systems over FTR fading is evaluated in terms of the bit error rate and the outage capacity, and the interplay between the FTR fading model parameters and the system performance is discussed. Monte Carlo simulations have been carried out in order to validate the obtained theoretical expressions.

High-Efficiency Device Positioning and Location-Aware Communications in Dense 5G Networks

Abstract: In this article, the prospects and enabling technologies for high-efficiency device positioning and location-aware communications in emerging 5G networks are reviewed. We will first describe some key technical enablers and demonstrate by means of realistic ray-tracing and map based evaluations that positioning accuracies below one meter can be achieved by properly fusing direction and delay related measurements on the network side, even when tracking moving devices. We will then discuss the possibilities and opportunities that such high-efficiency positioning capabilities can offer, not only for location-based services in general, but also for the radio access network itself. In particular, we will demonstrate that geometric location-based beamforming schemes become technically feasible, which can offer substantially reduced reference symbol overhead compared to classic full channel state information (CSI)-based beamforming. At the same time, substantial power savings can be realized in future wideband 5G networks where acquiring full CSI calls for wideband reference signals while location estimation and tracking can, in turn, be accomplished with narrowband pilots.

Exploiting Multiple-Antenna Techniques for Non-Orthogonal Multiple Access

Abstract: This paper aims to provide a comprehensive solution for the design, analysis, and optimization of a multiple-antenna non-orthogonal multiple access (NOMA) system for multiuser downlink communication with both time duplex division and frequency duplex division modes. First, we design a new framework for multiple-antenna NOMA, including user clustering, channel state information (CSI) acquisition, superposition coding, transmit beamforming, and successive interference cancellation. Then, we analyze the performance of the considered system, and derive exact closed-form expressions for average transmission rates in terms of transmit power, CSI accuracy, transmission mode, and channel conditions. For further enhancing the system performance, we optimize three key parameters, i.e., transmit power, feedback bits, and transmission mode. Especially, we propose a low-complexity joint optimization scheme, so as to fully exploit the potential of multiple-antenna techniques in NOMA. Moreover, through asymptotic analysis, we reveal the impact of system parameters on average transmission rates, and hence present some guidelines on the design of multiple-antenna NOMA. Finally, simulation results validate our theoretical analysis, and show that a substantial performance gain can be obtained over traditional orthogonal multiple access technology under practical conditions.

On Secure Underlay MIMO Cognitive Radio Networks With Energy Harvesting and Transmit Antenna Selection

Abstract: In this paper, we consider an underlay multiple-input-multiple-output (MIMO) cognitive radio network (CRN) including a pair of primary nodes, a couple of secondary nodes, and an eavesdropper, where the secondary transmitter is powered by the renewable energy harvested from the primary transmitter in order to improve both energy efficiency and spectral efficiency. Based on whether the channel state information of wiretap links are available or not, the secrecy outage performance of the optimal antenna selection (OAS) scheme and suboptimal antenna selection (SAS) scheme for underlay MIMO CRN with energy harvesting are investigated and compared with traditional space-time transmission scheme. The closed-form expressions for exact and asymptotic secrecy outage probability are derived. Monte-Carlo simulations are conducted to testify the accuracy of the analytical results. The analysis illustrates that the OAS scheme outperforms SAS scheme. Furthermore, the asymptotic result shows that no matter which scheme is considered, the OAS and SAS schemes can achieve the same secrecy diversity order.