Channel Temporal Correlation Research Articles

Adaptive Downlink OFDMA System With Low-Overhead and Limited Feedback in Time-Varying Underwater Acoustic Channel

Because of the time-varying characteristic and the large propagation delay in underwater acoustic (UWA) channel, adaptive resource allocation in UWA orthogonal frequency-division multiplexing access (OFDMA) system cannot be performed with the assumption of perfect channel state information (CSI) at the transmitter. Therefore, this paper focuses on the outdated CSI and proposes an adaptive downlink OFDMA system with low-overhead and limited feedback in time-varying UWA channel. First, a data fitting method is proposed for channel reconstruction, which shows better performance and lower overhead than the traditional group quantization method in simulations. Second, we define the per-subcarrier channel temporal correlation (PSCTC) and long-term statistical mean value as two indicators of outdated CSI, for optimizing CSI feedback in UWA channel with large propagation delay. Finally, a CSI selection method based on channel correlation attenuation (CCA) factor is proposed, where CSI used for feedback is selected between outdated-instantaneous CSI and outdated-average CSI according to the CCA factor in the current observation period. Sea trail results show that the CSI reconstruction based on data fitting method has lower bit error ratio and less overhead than traditional group quantization method. The PSCTC coefficient is related to the feedback delay, and the long-term statistics is stationary, which means that the outdated-average CSI is suitable for channel feedback. The CSI selection method based on CCA factor has better performance than that only uses outdated-instantaneous CSI or outdated-average CSI under different time-varying channel conditions in experimental results.

Secure Transmission Design With Feedback Compression for the Internet of Things

Physical layer security is an emerging technique to protect the wireless communications in the Internet of Things (IoT). Motivated by the fact that a single IoT terminal usually occupies a very small fraction of feedback resources, we propose a novel secure transmission design with feedback compression to improve the feedback resources utilization for secure communications. Specifically, we first introduce a multiperiod one-feedback (MPOF) scheme to exploit the channel temporal correlation existing in the IoT scenarios, making the IoT terminal convey its channel knowledge to the central controller in a more efficient manner. Under this MPOF scheme, we then put forward a virtual quantizer model and design a generalized fixed-rate secure on–off transmission scheme, where the central controller adaptively adjusts the transmission parameters in one feedback interval. By averaging the total secrecy throughput of one feedback interval over all the coherence periods thereof, we further characterize the secrecy throughput of our proposed transmission scheme and facilitate the joint optimization design of the feedback interval length, the wiretap codes, and the power allocation ratios. To handle this nonconvex problem, we develop an efficient approach involving the block coordinate descent algorithm and the 1-D search method. Numerical results show that when the channel temporal correlation is high, our proposed secure transmission design achieves a significantly higher secrecy throughput than the conventional design constrained by the same amount of feedback resources.

Capacity, fairness, and queueing performance analysis of opportunistic scheduling with one-bit feedback

In this paper, we consider opportunistic downlink scheduling in a cellular network that exploits multiuser diversity using one-bit feedback. To reduce the feedback overhead inherent in opportunistic scheduling, mobile stations are allowed to send one-bit information to the base station only when their channel quality exceeds a given threshold value. The objective of this paper is two-fold: (i) find the threshold value that can optimize queueing performance of the scheduler and (ii) investigate the relationship among capacity, fairness, and queueing performance. To this end, we first derive a formula for the sum-rate capacity as a function of the threshold. Next, we quantify the long-term and short-term fairness using Jain’s fairness index. Lastly, we develop a packet-level queueing model and derive QoS measures such as queueing delay and packet loss probability. Based on our analysis, we optimize the threshold value by considering both the traffic condition at the MAC layer and the temporal channel correlation at the PHY layer. Numerical results show that the optimized threshold can significantly reduce the average queueing delay and the packet loss probability. In addition, we find that there is a trade-off between short-term fairness and sum-rate capacity, whose control knob is the threshold. We show that, from a queueing performance perspective, supporting the sum-rate capacity (resp. the short-term fairness) is more important when the traffic is heavy (resp. light) or the wireless channel varies with low (resp. high) temporal correlation.

Coordinated beamforming for heterogeneous small‐cell networks with a non‐ideal backhaul

In this article, intra- and inter-cell interference caused by the non-ideal backhaul in coordinated multipoint (CoMP) are studied. A recursive estimation-based zero-forcing beamforming (RE-ZFBF) scheme is proposed to mitigate the interference. Based on an initial value, the RE-ZFBF scheme recursively approximates the beamformer variation between two consecutive time points by exploiting the temporal channel correlation. In contrast to a previous work, the authors convert the original non-convex constraint quality-of-service problem to an unconstrained convex one, by optimising the estimation of the beamformer error, rather than the beamforming power. It is shown that the proposed scheme can mitigate at most half the intra- and inter-cell interference, enhance the ZFBF performance, and outperform the existing works for CoMP with a non-ideal backhaul in heterogeneous small-cell networks.

Temporal-correlation-based compressive channel estimation for universal filtered multicarrier system over fast-fading channels

The universal filtered multicarrier technique is a competitive candidate multicarrier modulation scheme for 5G communication systems. Conventional channel-estimation algorithms suffer from significant performance losses due to the large spread in the delay of multipath channels in high-speed scenarios. To address this problem, we here propose a low-complexity, partial priori information-sparsity adaptive matching pursuit (PPI-SAMP) algorithm. Unlike the conventional SAMP algorithm, the PPI-SAMP algorithm improves performance over fast-fading channels by adequately exploiting the sparse characteristics and temporal correlation of wireless channels. First, the PPI-SAMP algorithm averages the channel impulse responses (CIRs) of consecutive symbols over the coherence time for achieving the accuracy required for coarse channel estimation. Second, the improved SAMP algorithm acquires the accurate CIRs with low complexity based on the coarse CIR. Moreover, the MSE performance and recovery probability with varying sizes of IBI-free region indicate that the proposed PPI-SAMP algorithm offers a longer CIR for multipath interference and is more robust against larger multipath-channel delay than the conventional SAMP and CoSaMP algorithms. The proposed algorithm also estimates channels more accurately than conventional SAMP and CoSaMP algorithms despite having a complexity reduced by approximately 52% compared to conventional SAMP algorithm.

Exploiting Temporal Channel Correlation in Data-Assisted Massive MIMO Uplink Detection

A data-assisted channel estimation scheme to exploit the temporal channel correlation in massive multiple-input multiple-output uplink transmission is proposed. Specifically, the previous decoded uplink frame is used to suppress the inter-cell interference in the channel estimation of the current frame. Based on the proposed scheme, we derive the asymptotic signal-to-interference expression, and the insights on how the network and channel parameters affect the system performance are obtained. It is shown numerically and analytically that the data-assisted scheme can reduce channel estimation errors and improve the signal-to-interference performance of uplink massive MIMO systems significantly with the appropriate channel temporal correlation.

Joint Channel Training and Feedback for FDD Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance gains in massive MIMO systems, accurate channel state information at the transmitter (CSIT) is crucial. Due to the overwhelming pilot signaling and channel feedback overhead, however, conventional downlink channel estimation and uplink channel feedback schemes might not be suitable for frequency-division duplexing (FDD) massive MIMO systems. In addition, these two topics are usually separately considered in the literature. In this paper, we propose a joint channel training and feedback scheme for FDD massive MIMO systems. Specifically, we firstly exploit the temporal correlation of time-varying channels to propose a differential channel training and feedback scheme, which simultaneously reduces the overhead for downlink training and uplink feedback. We next propose a structured compressive sampling matching pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the structured sparsity of wireless MIMO channels. Simulation results demonstrate that the proposed scheme can achieve substantial reduction in the training and feedback overhead.

Structured Distributed Compressive Channel Estimation Over Doubly Selective Channels

For an orthogonal frequency-division multiplexing (OFDM) system over a doubly selective (DS) channel, a large number of pilot subcarriers are needed to estimate the numerous channel parameters, resulting in low spectral efficiency. In this paper, by exploiting temporal correlation of practical wireless channels, we propose a highly efficient structured distributed compressive sensing (SDCS) based joint multi-symbol channel estimation scheme. Specifically, by using the complex exponential basis expansion model (CE-BEM) and exploiting the sparsity in the delay domain within multiple OFDM symbols, we turn to estimate jointly sparse CE-BEM coefficient vectors rather than numerous channel taps. Then a sparse pilot pattern within multiple OFDM symbols is designed to obtain an ICI-free structure and transform the channel estimation problem into a joint-block-sparse model. Next, a novel block-based simultaneous orthogonal matching pursuit (BSOMP) algorithm is proposed to jointly recover coefficient vectors accurately. Finally, to reduce the CE-BEM modeling error, we carry out smoothing treatments of already estimated channel taps via piecewise linear approximation.Simulation results demonstrate that the proposed channel estimation scheme can achieve higher estimation accuracy than conventional schemes, although with a smaller number of pilot subcarriers.

Structured Compressive Sensing-Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO

Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the non-orthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multi-cell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator.

Projection-based Differential Feedback for FDD Massive MIMO Systems

Channel state information at the transmitter (CSIT) plays a key role in achieving potential gain of massive multiple-input–multiple-output (MIMO) systems. In frequency-division duplex (FDD) systems, CSIT can be obtained through feedback from the receiver. Conventional limited feedback schemes, which have been designed for small-scale MIMO systems, suffer from a prohibitive amount of feedback requirement and encoding complexity when the number of transmit antennas becomes massive. In this paper, a projection-based differential feedback (PBDF) protocol is proposed for FDD massive MIMO systems. In the PBDF framework, the difference between original and predicted vectors is projected, and quantization is performed in a smaller dimensional subspace. With an appropriate projection exploiting spatial and temporal correlation of massive MIMO channels, the feedback amount and encoding complexity can be significantly reduced. The simulation results show that the proposed scheme achieves a large portion of potential throughput gain of massive MIMO systems with a small amount of channel feedback.

Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér-Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

Differential Modulation Exploiting the Spatial–Temporal Correlation of Wireless Channels With Moving Antenna Array

Provisioning reliable wireless services for railway passengers is becoming an increasingly critical problem to be addressed with the fast development of high speed trains (HST). In this paper, exploiting the linear mobility inherent to the HST communication scenario, we discover a new type of spatial-temporal correlation between the base station and moving antenna array on the roof top of the train. Capitalizing on the new spatial-temporal correlation structure and properties, an improved differential space-time modulation (DSTM) scheme is proposed. Analytical expressions are obtained for the pairwise error probability of the system. It is demonstrated that, the proposed approach achieves superior error performance compared with the conventional DSTM scheme. In addition, an adaptive method which dynamically adjusts the transmission block length is proposed to further enhance the system performance. Numerical results are provided to verify the performance of the proposed schemes.

Compressive sensing‐based differential channel feedback for massive MIMO

Massive multiple-input multiple-output (MIMO) is becoming a key technology for future 5G wireless communications. Channel feedback for massive MIMO is challenging due to the substantially increased dimension of MIMO channel matrix. A compressive sensing (CS)-based differential channel feedback scheme to reduce the feedback overhead is proposed. Specifically, the temporal correlation of time-varying channels is exploited to generate the differential channel impulse response (CIR) between two CIRs in neighbouring time slots, which enjoys a much stronger sparsity than the original sparse CIRs. Thus, the base station can recover the differential CIR from the highly compressed differential CIR under the framework of CS theory. Simulations show that the proposed scheme reduces the feedback overhead by about 20% compared with the direct CS-based scheme.

Priori-Information Aided Iterative Hard Threshold: A Low-Complexity High-Accuracy Compressive Sensing Based Channel Estimation for TDS-OFDM

This paper develops a low-complexity channel estimation (CE) scheme based on compressive sensing (CS) for time-domain synchronous (TDS) orthogonal frequency-division multiplexing (OFDM) to overcome the performance loss under doubly selective fading channels. Specifically, an overlap-add method of the time-domain training sequence is first proposed to obtain the coarse estimates of the channel length, path delays and path gains of the wireless channel, by exploiting the channel's temporal correlation to improve the robustness of the coarse CE under the severe fading channel with long delay spread. We then propose the priori-information aided (PA) iterative hard threshold (IHT) algorithm, which utilizes the priori information of the acquired coarse estimate for the wireless channel and therefore is capable of obtaining an accurate channel estimate of the doubly selective fading channel. Compared with the classical IHT algorithm whose convergence requires the $l_2$ norm of the measurement matrix being less than 1, the proposed PA-IHT algorithm exploits the priori information acquired to remove such a limitation as well as to reduce the number of required iterations. Compared with the existing CS based CE method for TDS-OFDM, the proposed PA-IHT algorithm significantly reduces the computational complexity of CE as well as enhances the CE accuracy. Simulation results demonstrate that, without sacrificing spectral efficiency and changing the current TDS-OFDM signal structure, the proposed scheme performs better than the existing CE schemes for TDS-OFDM in various scenarios, especially under severely doubly selective fading channels.

Double-Cap Differential Codebook Structure for MU MISO Systems in Correlated Channels

We propose a double-cap differential codebook (DCB) design for multiuser (MU) multiple-input-single-output systems. The MU transmission uses signal-to-leakage-and-noise ratio precoding. The double-cap DCB is designed for temporally/ spatially correlated channels and is based on a combined spherical and polar cap structure. We consider an inner spherical cap and an outer polar cap structure with different radii. The design allows us to systematically alter the radii of the caps over the feedback time depending on the previously selected codeword. The proposed double-cap DCB does not require knowledge of temporal or spatial channel correlation at the base-station, thus removing the overhead of additional feedback from the user. Simulation results show the effectiveness of the proposed double-cap DCB over a single polar cap DCB in terms of sum capacity gain and quantization errors.

Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations

This letter proposes a parametric sparse multiple input multiple output (MIMO)-OFDM channel estimation scheme based on the finite rate of innovation (FRI) theory, whereby super-resolution estimates of path delays with arbitrary values can be achieved. Meanwhile, both the spatial and temporal correlations of wireless MIMO channels are exploited to improve the accuracy of the channel estimation. For outdoor communication scenarios, where wireless channels are sparse in nature, path delays of different transmit-receive antenna pairs share a common sparse pattern due to the spatial correlation of MIMO channels. Meanwhile, the channel sparse pattern is nearly unchanged during several adjacent OFDM symbols due to the temporal correlation of MIMO channels. By simultaneously exploiting those MIMO channel characteristics, the proposed scheme performs better than existing state-of-the-art schemes. Furthermore, by joint processing of signals associated with different antennas, the pilot overhead can be reduced under the framework of the FRI theory.

Joint Transmit and Receive Beamforming for Multiuser MIMO Downlinks With Channel Uncertainty

In this paper, we develop a novel joint transmit and receive design for multiuser multiple-input-multiple-output (MU-MIMO) systems. By exploiting channel temporal correlations, the proposed scheme incorporates a matched-filter (MF) combining vector into the beamformer design and enhances the performance of conventional signal-to-leakage-plus-noise ratio (SLNR) beamforming. With imperfect channel state information (CSI) available at the transmitter, we then focus on improving the robustness of the proposed algorithm against the mismatched CSI. The feedback delay effect is considered in the beamformer design first. We design a statistically robust beamforming design by maximizing the conditional expectation of the SLNR, hence alleviating the performance degradation due to CSI errors. Following that, we further encompass the imperfection caused by the quantized feedback into the beamforming design. Moreover, we also develop efficient power allocation techniques to further improve the sum-rate performance. Finally, we extend the proposed robust joint transmit and receive design to multicell MIMO systems. Simulation results verify the effectiveness of our algorithm under various scenarios.

Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems

In this paper, the problem of pilot beam pattern design for channel estimation in massive multiple-input multiple-output systems with a large number of transmit antennas at the base station is considered, and a new algorithm for pilot beam pattern design for optimal channel estimation is proposed under the assumption that the channel is a stationary Gauss-Markov random process. The proposed algorithm designs the pilot beam pattern sequentially by exploiting the properties of Kalman filtering and the associated prediction error covariance matrices and also the channel statistics such as spatial and temporal channel correlation. The resulting design generates a sequentially-optimal sequence of pilot beam patterns with low complexity for a given set of system parameters. Numerical results show the effectiveness of the proposed algorithm.

Cooperative ARQ With Relay Selection: An Analytical Framework Using Semi-Markov Processes

An important building block in cooperative diversity is relay selection, which has to ensure that a well-suited node is employed as a relay. The required coordination among nodes causes signaling overhead, which in turn can significantly devalue the performance benefits gained by cooperative diversity. A relay update policy defines when a new relay is selected; it can balance the tradeoff between performance and overhead. This tradeoff is studied using mathematical methods. We consider three relay selection schemes, i.e., permanent, reactive, and adaptive, which have different relay update rules. We develop an analytical framework using semi-Markov processes to evaluate the throughput and energy efficiency of cooperative automatic repeat request (ARQ) protocols in time-correlated multipath fading channels. Results reveal potential performance gains of different selection schemes under various conditions. The reactive and adaptive schemes make use of better suited relays due to frequent selections. If their selection overhead, however, is significant, a permanent relay can achieve higher throughput due to negligible overhead. The impact of temporal correlation of fading channels on throughput and energy efficiency is also shown. These insights can be applied for development of cooperative communication protocols.

Inter-cell interference nulling with bit partitioning for extended Clarke's modelled multi-cell wireless networks

The sum-rate performance of multi-cell wireless networks with feedback bits allocation strategy are analysed in this paper. The Doppler power spectrum and autocorrelation function are not appropriate to describe the real wireless channels for which the random environment are time varying. Accordingly the channel is non-constant to describe the Doppler power spectrum and autocorrelation functions. The channel temporal correlation is modelled as a function of delay using the Gauss-Markov model. This paper incorporates the fluctuations in component phases in traditional Clarke's model and studies the effect of number of bits required to quantise the signal plus interference noise ratio. The analysis provides insights on the feedback designs and also shows significant improvement in sum-rate and outage probability.