Channel State Information Acquisition Research Articles

Multiple-Beam Selection With Limited Feedback for Hybrid Beamforming in Massive MIMO Systems

Hybrid multiple-input multiple-output (MIMO) systems have been thought as a promising technology in future 5G. Compared with conventional digital MIMO systems, such a structure is equipped with fewer RF chains, which would reduce the computational complexity and hardware cost, and meanwhile additional analog beamforming is introduced to maintain the performance. However, scarce RF chains make channel state information acquisition difficult for analog beamforming. In this paper, we consider a practical hybrid beamforming, which includes zero-forcing (ZF) precoding in digital beamforming, and beam selection for analog beamforming. First, the statistic information of users (e.g., angle and distance) is utilized to construct an approximate channel for each user. Second, users individually evaluate the codebook and feedback the results, by which base station (BS) makes optimization and selects the final beams for analog beamforming. Finally, BS performs the digital baseband ZF precoding with the equivalent channel. In the process, we give two limited feedback methods for users, and two corresponding beam selection methods for BS. These methods are evaluated in the Rayleigh fading channels and mmWave channels. Simulation results show that our hybrid beamforming could approach performance of conventional digital precoding, and more RF chains could provide better performance. Moreover, proposed methods only require once feedback and effectively reduce the delay, and two feedback methods achieve a good tradeoff between performance and feedback cost.

Sequential Beamforming for Multiuser MIMO With Full-Duplex Training

Multiple transmitting antennas can considerably increase the downlink spectral efficiency by beamforming to multiple users at the same time. However, multiuser beamforming requires channel state information (CSI) at the transmitter, which leads to training overhead and reduces overall achievable spectral efficiency. In this paper, we propose and analyze a sequential beamforming strategy that utilizes full-duplex base station to implement downlink data transmission concurrently with CSI acquisition via in-band closed or open loop training. Our results demonstrate that full-duplex capability can improve the spectral efficiency of uni-directional traffic, by leveraging it to reduce the control overhead of CSI estimation. In moderate SNR regimes, we analytically derive tight approximations for the optimal training duration and characterize the associated respective spectral efficiency. We further characterize the enhanced multiplexing gain performance in the high SNR regime. In both regimes, the performance of the proposed full-duplex strategy is compared to the half-duplex counterpart to quantify spectral efficiency improvement. With experimental data [1] and 3D channel model [2] from 3GPP, in a 1.4 MHz 8X8 system LTE system with the block length of 500 symbols, the proposed strategy attains a spectral efficiency improvement of 130% and 8% with closed and open loop training, respectively.

Massive MIMO: survey and future research topics

Massive multiple-input multiple-output technology has been considered a breakthrough in wireless communication systems. It consists of equipping a base station with a large number of antennas to serve many active users in the same time-frequency block. Among its underlying advantages is the possibility to focus transmitted signal energy into very short-range areas, which will provide huge improvements in terms of system capacity. However, while this new concept renders many interesting benefits, it brings up new challenges that have called the attention of both industry and academia: channel state information acquisition, channel feedback, instantaneous reciprocity, statistical reciprocity, architectures, and hardware impairments, just to mention a few. This paper presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research.

Optimal Energy-Efficient Power Allocation for Distributed Antenna Systems With Imperfect CSI

In this paper, we study energy-efficient power allocation in distributed antenna systems (DASs). Unlike previous works that assumed perfect channel-state information (CSI) at the transmitter, we investigate a more practical case, where only the slowly varying large-scale CSI is known, to reduce the system overhead for CSI acquisition. Under a generalized DAS model, the power allocation problem for energy-efficiency (EE) maximization is formulated as a nonconvex fractional programming problem. Using the random matrix theory, the nonlinear fractional programming theory, and the saddle-point theory, we propose an iterative algorithm that achieves the global optimal solution. We also show that deploying the antennas in a distributed manner may enlarge the region where the optimal EE and the optimal spectral efficiency (SE) can be simultaneously achieved. Accordingly, we suggest the DAS-like way to densify the future 5G and beyond cellular networks to achieve EE and SE simultaneously.

DiFuse: distributed frequency domain user selection for multi-user MIMO networks

Optimal user selection is important in increasing the capacity of Multi-User Multi-Input Multi-Output Wi-Fi networks, yet it faces a significant challenge; the multi-user diversity gain can be overwhelmed by the formidable Channel State Information (CSI) acquisition overhead. To lessen the overhead, existing schemes adopt the greedy user selection which generally takes the projected norm as the user selection metric, since it considers both the channel power gain and the orthogonality. However, the projected norm suffers from occasional poor user selection, since it does not take the optimal sum capacity gain into account. This paper proposes a new distributed user selection protocol called DiFuse. To employ the sum capacity gain as the user selection metric in DiFuse, each user cleverly computes its own estimated capacity gain by overhearing the CSI feedback from others. The users then simultaneously transmit their feedbacks at the frequency domain via the distributed feedback contention, which effectively reduces the feedback overhead. Then the AP collectively utilizes them for user selection that achieves the maximum positive increment to the sum capacity gain. We implemented the prototype of DiFuse on the USRP N210, and evaluated its performance via both testbed experiments and trace-driven emulations. The results showed that DiFuse outperforms the throughput of the existing scheme called OPUS by 1.8 $${\times }$$ on average, while maintaining better fairness.

Artificial Noise Assisted Secure Transmission for Distributed Antenna Systems

This paper studies the artificial noise (AN) assisted secure transmission for a distributed antenna systems (DAS). To avoid a significant overhead caused by full legitimate channel state information (CSI) acquisition, tracking and collection in the central processor, we propose a distributed AN scheme utilizing the large-scale CSI of the legitimate receiver and eavesdropper. Our objective is to maximize the ergodic secrecy rate (ESR) via optimizing the power allocation between the confidential signal and AN for each remote antenna (RA) under the per-antenna power constraint. Specifically, exploiting random matrix theory, we first establish an analytical expression of the achievable ESR, which leads to a non-convex optimization problem with multiple non-convex constraints in the form of high-order fixed-point equations. To handle the intractable constraints, we recast it into a max-min optimization problem, and propose an iterative block coordinate descent (BCD) algorithm to provide a stationary solution. The BCD algorithm is composed of three subproblems, where the first two subproblems are convex with closed-form solutions, and the last one is a convex-concave game whose saddle-point is located by a tailored barrier algorithm. Simulation results validate the effectiveness of the proposed iterative algorithm and show that our scheme not only reduces the system overhead greatly but also maintains a good secrecy performance.

Spectrally Efficient CSI Acquisition for Power Line Communications: A Bayesian Compressive Sensing Perspective

Power line communication (PLC) techniques present a no extra wire solution for the communication purpose in a smart grid due to the ubiquity and low cost. Moreover, the through-the-grid property of PLC has naturally extended its possible applications, including but not limited to the automatic meter reading, line quality monitoring, online diagnostics, and network tomography. To guarantee the performance of communications as well as other applications in PLC systems, accurate channel state information (CSI) acquisition should be performed regularly. However, the conventional pilot-based CSI acquisition approaches in PLC systems have not made full use of the channel characteristics and hence suffer from a low spectral efficiency. In this paper, by exploiting the parametric sparsity and discretizing the electrical length in the well-known PLC channel model, we formulate the non-sparse (either time domain or frequency domain) PLC channel into a compressive sensing (CS) applicable problem. Furthermore, we propose a spectrally efficient CSI acquisition scheme under the framework of Bayesian CS and extend it to the multiple-input multiple-output PLC by investigating the channel spatial correlation. Compared with the existing sparse CSI acquisition schemes for PLC, such as the annihilating filter-based and the estimating signal parameters via rotational invariance technique-based ones, the proposed scheme has better mean square error performance and noise robustness.

Nonorthogonal Time–Frequency Training-Sequence-Based CSI Acquisition for MIMO Systems

In this paper, a spectrally efficient nonorthogonal time–frequency training sequence (TS)-based channel state information (CSI) acquisition approach is proposed for multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems under the framework of structured compressed sensing (SCS). The scheme first relies on a time-domain TS that is identical for all transmit antennas to acquire the partial channel common support by utilizing the spatial correlation property of the MIMO channels and then uses the frequency-domain TS for accurate CSI recovery based on the proposed adaptive spatially–temporally joint simultaneous orthogonal matching pursuit algorithm. Here, the obtained partial channel common support can be utilized to reduce the complexity of the classical SCS algorithm and improve the signal recovery probability. Simulation results show that the proposed scheme could significantly reduce the TS overhead and demonstrate better performance than the existing MIMO-OFDM systems, which might be suitable for massive MIMO systems in future fifth-generation communications.

Training Design for Channel Estimation in Uplink Cloud Radio Access Networks

In this paper, a training design and channel estimation scheme is considered for uplink cloud radio access networks (C-RANs) consisting of multiple user equipments (UEs), remote radio heads (RRHs), and a centralized baseband unit (BBU) pool. Since most signal processing functions in C-RANs are moved from RRHs to the BBU pool, the individual channels over the links between UEs and RRHs and the links between RRHs and the BBU pool cannot be estimated directly. To address this issue, segment training based individual channel estimation for C-RANs is proposed in this paper, in which channel state information acquisition is performed through two consecutive segments. By using the Kalman filter, the sequential minimum mean-square-error (SMMSE) estimator is developed to efficiently estimate the individual channel states through prior knowledge of long-term channel correlation statistics and the latest radio channel state. A training structure design subject to a power constraint is obtained by minimizing the mean-square-error (MSE) of the SMMSE estimator. Since the MSE is insufficient to fully evaluate the overall performance of C-RANs, the uplink ergodic capacity is derived to exploit the impact of channel estimation on the data transmission by taking the estimation errors into consideration, and the tradeoff between the lengths of two segment training sequences is optimized by maximizing the corresponding spectral efficiency. Furthermore, the Cramér-Rao bound is used to evaluate the proposed SMMSE estimator's performance. Simulation results show that the SMMSE estimator and the corresponding training design can effectively decrease MSE and significantly increase the quality and efficiency of data transmission in C-RANs.

Design and Performance Analysis of Noncoherent Detection Systems With Massive Receiver Arrays

Harvesting the gain of a large number of antennas in a mmWave band has mainly been relying on the costly operation of channel state information (CSI) acquisition and cumbersome phase shifters. Recent works have started to investigate the possibility to use receivers based on energy detection (ED), where a single data stream is decoded based on the channel and noise energy. The asymptotic features of the massive receiver array lead to a system where the impact of the noise becomes predictable due to a noise hardening effect. This in effect extends the communication range compared to the receiver with a small number of antennas, as the latter is limited by the unpredictability of the additive noise. When the channel has a large number of spatial degrees of freedom, the system becomes robust to imperfect channel knowledge due to channel hardening. We propose two detection methods based on the instantaneous and average channel energy, respectively. Meanwhile, we design the detection thresholds based on the asymptotic properties of the received energy. Differently from existing works, we analyze the scaling law behavior of the symbol-error-rate (SER). When the instantaneous channel energy is known, the performance of ED approaches that of the coherent detection in high SNR scenarios. When the receiver relies on the average channel energy, our performance analysis is based on the exact SER, rather than an approximation. It is shown that the logarithm of SER decreases linearly as a function of the number of antennas. Additionally, a saturation appears at high SNR for PAM constellations of order larger than two, due to the uncertainty on the channel energy. Simulation results show that ED, with a much lower complexity, achieves promising performance both in Rayleigh fading channels and in sparse channels.

An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications

To meet the future demand for huge traffic volume of wireless data service, the research on the fifth generation (5G) mobile communication systems has been undertaken in recent years. It is expected that the spectral and energy efficiencies in 5G mobile communication systems should be ten-fold higher than the ones in the fourth generation (4G) mobile communication systems. Therefore, it is important to further exploit the potential of spatial multiplexing of multiple antennas. In the last twenty years, multiple-input multiple-output (MIMO) antenna techniques have been considered as the key techniques to increase the capacity of wireless communication systems. When a large-scale antenna array (which is also called massive MIMO) is equipped in a base-station, or a large number of distributed antennas (which is also called large-scale distributed MIMO) are deployed, the spectral and energy efficiencies can be further improved by using spatial domain multiple access. This paper provides an overview of massive MIMO and large-scale distributed MIMO systems, including spectral efficiency analysis, channel state information (CSI) acquisition, wireless transmission technology, and resource allocation.

Downlink Scheduling Over Markovian Fading Channels

We consider the scheduling problem in downlink wireless networks with heterogeneous, Markov-modulated, ON/OFF channels. It is well known that the performance of scheduling over fading channels relies heavily on the accuracy of the available channel state information (CSI), which is costly to acquire. Thus, we consider the CSI acquisition via a practical ARQ-based feedback mechanism whereby channel states are revealed at the end of only scheduled users' transmissions. In the assumed presence of temporally correlated channel evolutions, the desired scheduler must optimally balance the exploitation - exploration tradeoff, whereby it schedules transmissions both to exploit those channels with up-to-date CSI and to explore the current state of those with outdated CSI. In earlier works, Whittle's Index Policy had been suggested as a low-complexity and high-performance solution to this problem. However, analyzing its performance in the typical scenario of statistically heterogeneous channel state processes has remained elusive and challenging, mainly because of the highly coupled and complex dynamics it possesses. In this work, we overcome these difficulties to rigorously establish the asymptotic optimality properties of Whittle's Index Policy in the limiting regime of many users. More specifically: 1) we prove the local optimality of Whittle's Index Policy, provided that the initial state of the system is within a certain neighborhood of a carefully selected state; (2) we then establish the global optimality of Whittle's Index Policy under a recurrence assumption that is verified numerically for our problem. These results establish that Whittle's Index Policy possesses analytically provable optimality characteristics for scheduling over heterogeneous and temporally correlated channels.

I/Q Imbalance Aware Widely-Linear Receiver for Uplink Multi-Cell Massive MIMO Systems: Design and Sum Rate Analysis

In-phase/quadrature-phase imbalance (IQI) is one of the most important hardware impairments in communication systems. It arises in the analogue parts of direct conversion transceivers and can cause severe performance losses. In this paper, IQI aware widely-linear (WL) channel estimation and data detection schemes for uplink multicell massive multiple-input multiple-output (MIMO) systems are proposed. The resulting receiver is a WL extension of the minimum mean-square-error (MMSE) receiver and jointly mitigates multiuser interference and IQI by processing the real and the imaginary parts of the received signal separately. Thereby, the IQI arising at both the base station and the user terminals is taken into account. The considered channel state information acquisition model includes the effect of pilot contamination, which is caused by the reuse of the same training sequences in neighbouring cells. We apply results from random matrix theory to derive analytical expressions for the asymptotic achievable sum rates of the proposed IQI aware and conventional IQI unaware (IQU) receivers in the large system limit. Our simulation and analytical results show that the performance of the proposed IQI aware WLMMSE (IQA-WLMMSE) receiver in a system with IQI is close to that of the MMSE receiver in an ideal system without IQI.

Closed-Loop Compressive CSIT Estimation in FDD Massive MIMO Systems With 1 Bit Feedback

One major practical issue for the implementation of frequency division duplex (FDD) massive MIMO systems is that the acquisition of channel state information at the transmitter side (CSIT) requires overwhelming pilot training and feedback overhead. Recently, compressive sensing (CS) based CSIT estimation approaches have been proposed to reduce the pilot training overhead for massive MIMO systems. However, it is very difficult to compute the minimum required pilot training overhead at the base station because of loose restricted isometry property (RIP) bounds for successful CS recovery and unknown sparsity levels. In this paper, we consider a framework of closed-loop compressive CSIT estimation with 1 bit feedback to learn the minimum required pilot overhead to achieve a certain target CSIT MSE without explicit knowledge of channel sparsity. We analyze the convergence behaviors of the multi-loop pilot overhead adaptation based on the Lyapunov approach. Simulations show that the proposed closed-loop compressive CSIT estimation framework has substantial performance gain over conventional open-loop algorithms and is very robust to dynamic sparsity as well as model mismatch.

Exploiting the Increasing Correlation of Space Constrained Massive MIMO for CSI Relaxation

In this paper, we explore low-complexity transmission in physically-constrained massive multiple-input multiple-output (MIMO) systems by means of channel state information (CSI) relaxation. In particular, we propose a strategy to take advantage of the correlation experienced by the channels of neighbour antennas when deployed in tightly packed antenna arrays. The proposed scheme is based on collecting CSI for only a subset of antennas during the pilot training stage and, subsequently, using averages of the acquired CSI for the remaining closely-spaced antennas. By doing this, the total number of radio frequency (RF) chains, for both CSI acquisition and data transmission, and the baseband signal processing are reduced, hence simplifying the overall system operation. At the same time, this impacts the quality of the channel estimation produced after the CSI acquisition process. To characterize this tradeoff, we explore the impact that the number of antennas with instantaneous CSI has on the performance, signal processing complexity, and energy efficiency of time-division duplex (TDD) systems. The analytical and simulation results presented in this paper show that the application of the proposed strategy in size-constrained antenna arrays is able to significantly enhance the energy efficiency against systems with full CSI availability, while approximately preserving their average performance.

Structured compressive sensing‐based non‐orthogonal time‐domain training channel state information acquisition for multiple input multiple output systems

In practical multiple input multiple output (MIMO) systems, accurate knowledge of the channel state information (CSI) is a prerequisite to guarantee the system performance. The conventional CSI acquisition methods for MIMO system usually rely on the orthogonal (either time- or frequency-domain) training sequences (TSs) to estimate the channel associated with each transmit–receive antenna pair, which is not spectrally efficient. This study proposes a non-orthogonal time-domain training-based CSI acquisition approach for MIMO systems under the framework of structured compressive sensing. By exploiting the spatial–temporal correlations of the sparse MIMO channels, a spatially–temporally spARsity-adaptivE-simultaneous orthogonal matching pursuit algorithm is proposed, which could use the inter-block interference free region of very small dimension within the received TS to recover the multiple channels. Furthermore, the proposed algorithm could utilise the priori channel partial common support to improve the recovery probability and reduce the complexity. Simulation results show that the proposed scheme has better performance and higher spectral efficiency than the conventional MIMO schemes, which might be an appealing solution for the future wireless communications.

A Multipath Extraction-Based CSI Acquisition Method for FDD Cellular Networks With Massive Antenna Arrays

Unlocking the true potential of massive antenna arrays for cellular networks operating in frequency division duplex (FDD) mode is a challenging task as inadequate channel state information (CSI) by conventional limited-size UE feedback forces crude beamforming in downlink. This paper develops a new technique for the CSI acquisition problem of FDD massive multiple-input multiple-output (MIMO) networks based on estimating the reciprocal characteristics of the multipath channel directly from uplink, while merely the nonreciprocal properties of the dominant propagation paths are estimated and fed back from UE. The main benefits of this technique over compressive-sensing methods include allowing single-beam pilot transmission during downlink training and relaxed UE-side computational complexity, as the full knowledge of CSI vector is no longer required at UE. The design can be accomplished by measuring the direction-of-arrival information and average powers of the dominant paths from uplink, indicating the target dominant paths to corresponding UEs, estimating and subsequently signaling the random phase information of the target paths from each UE, and reconstructing the aggregate CSI vector at eNB. The numerical evaluation results demonstrate that the proposed mechanism offers promising potential for reliable FDD operation in next generation cellular networks equipped with massive antenna arrays.

DSTBC experimental research on UV communication system

Diversity gain can be obtained by applying the space---time block code (STBC) technology to ultraviolet (UV) communication. However, some STBCs, for example, the Alamouti code, quite depend on the acquisition of UV channel state information (CSI) because the accuracy of the obtained CSI influences the quality of the received signal and finally influences the system performance. Yet it is not easy to precisely obtain CSI, and this will affect the validity of some STBC decoding. To solve this problem, this paper tends to study the application of differential space---time block code (DSTBC) technology in the UV communication system. The validity with the exact diversity gain is verified with simulation and experimental results. The processes of the transmitters and receivers in $$2\times 2$$2×2 DSTBC are analyzed, and the different UV system performances in different communication situations are acquired. The algorithm complexity of DSTBC is compared with that of Alamouti code. The conclusion then is that the advantage of the DSTBC scheme with no requirement of obtaining real-time CSI is found in the fast time-varying and quick decaying UV channel.

High Diversity Transceiver for Low Power Differentially Encoded OFDM System

In this work, we investigate differentially encoded blind transceiver design in low signal-to-noise ratio (SNR) regimes for orthogonal frequency-division multiplexing (OFDM) signaling. Owing to the fact that acquisition of channel state information is not viable for short coherence times or in low SNR regimes, we propose a time-spread frequency-encoded method under OFDM modulation. The repetition (spreading) of differentially encoded symbols allows us to achieve a target energy per bit to noise ratio and higher diversity. Based on the channel order, we optimize subcarrier assignment for spreading (along time) to achieve frequency diversity of an OFDM modulated signal. We present the performance of our proposed transceiver design and investigate the impact of Doppler frequency on the performance of the proposed differentially encoded transceiver design. To further improve reliability of the decoded data, we employ capacity-achieving low-density parity-check forward error correction encoding to the information bits.

Efficient Channel State Information Acquisition for Device-to-Device Networks

We consider the problem of acquiring channel state information (CSI) in base-station (BS) controlled device-to-device (D2D) networks. Obtaining high-quality CSI requires a tradeoff between interference, outdatedness of CSI, and noise. Thus, the goal is to find an efficient pilot scheduling scheme that minimizes errors in the estimates. In this paper, we present the location aware training scheme (LATS) as simple yet efficient training technique. Assuming that the devices are aware of their location, LATS groups the devices into geographical segments and assigns a frequency reuse pattern to them. To identify the parameters of the scheme (segmentation and guard parameters, S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> and G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> , respectively), we use the average normalized mean square error (NMSE) as a metric, which combines the effects of outdatedness, noise, and interference. We derive an approximation of the average NMSE based on statistics of the devices and LATS structure. We present simulation results that illustrate LATS behavior and show that it outperforms TDMA- and CSMA-based schemes. Furthermore, we consider some practical challenges in using location information and evaluate their effects on the scheme.