Obtain Channel State Information Research Articles

Research on transmission performance of multi-input multi-output free space optical communication system channel

Traditional systems are susceptible to noise, atmospheric turbulence and other factors, resulting in large system channel transmission error and outage. Therefore, a new method of analyzing transmission performance of multi-input multi-output free space optical communication system channel with an improved space diversity technology is proposed in this paper in the context of different turbulence environments. In this method, with the Gamma–Gamma distribution model, the atmospheric turbulence process is analyzed and the optical field distribution is transformed into a disturbing function based on multi-scale impact; the differential phase-shift-keyed coherent intensity modulation method is adopted to control the turbulence so that the transmitter and receiver can obtain channel state information; a multi-input multi-output free space optical communication (MIMO-FSO) system is formed by constructing a system model and a multi-input multi-output channel model, and the MIMO-FSO system channel is modulated with the improved maximal ratio combining technology to analyze the transmission performance of MIMO-FSO system channel. Experimental results show that the improved method can reduce bit error rate and outage probability of the free space optical system, which has some advantages over traditional systems.

Channel Estimation in MIMO–OFDM Systems Based on a New Adaptive Greedy Algorithm

Channel estimation methods based on compressed sensing can be used to effectively obtain channel state information of MIMO-OFDM systems. This letter proposes a new adaptive matching pursuit (NAMP) algorithm and the evaluation prototype based on the LTE-Advanced wireless channel model. First, NAMP does not need the priori-knowledge of the sparsity level. Second, the fixed step size is determined in order to improve the efficiency of signal reconstruction. Third, a singular entropy order determination mechanism is employed to prevent the less relevant atoms from being introduced. Finally, simulation results are discussed in detail, which demonstrate that the proposed method results in lower computational complexity, and especially achieves more stable performance.

Smartphone-Based Indoor Fingerprinting Localization Using Channel State Information

Indoor localization technology plays an important role in many indoor application scenarios. Existing WiFi-based indoor localization methods mainly obtain channel state information (CSI) through the personal computer, or obtain coarse-grained received signal strength (RSS) through the smartphone to finish the localization. Little work has been done on using smartphones to obtain fine-grained channel state information for localization. In this paper, we use the smartphone to collect fine-grained CSI that is more convenient and applicable, and propose a indoor fingerprinting localization. Compared with the CSI collected by the computer, the CSI signal collected by the smartphone fluctuates greatly. Hence, we corrects the CSI data through the signal processing technique and selects optimal subcarriers to obtain more stable and effective signals. In order to cope with the noisy WiFi environment, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is used to remove abnormal sample points to reduce environmental interference. Moreover, the support vector machine multi-classification method is used for training and classification to achieve localization. Finally, we use the Google Nexus 5 smartphone to conduct experiments in two typical indoor environments. The localization accuracy is 91% and 86%, respectively, and both average localization errors are less than 0.5m. Experimental results show that the proposed algorithm has higher localization accuracy compared with the typical algorithms.

Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications.

Differential Subcarrier Index Modulation

One of the main challenges in orthogonal frequency division multiplexing (OFDM)–subcarrier index modulation (SIM) systems is the huge amount of resources required to obtain channel state information at the receiver side. In this paper, a differential subcarrier index modulation (DSIM) scheme is proposed, which entirely avoids the need for any channel knowledge at the receiver side. As such, time and energy resources spent in the channel estimation process are perceived. In DSIM, part of the transmitted block is modulated through ordinary signal modulation, whereas the second part is transmitted by selecting a specific permutation of the active subcarriers. The transmitted signals are designed to facilitate differential demodulation at the receiver side. A derivation is conducted for the average bit error probability of DSIM and an upper bound expression is obtained. Derived theoretical expression is substantiated through Monte Carlo simulation results. Reported results reveal that differential demodulation degrades the error performance of coherent SIM by nearly 4 dB in signal-to-noise-ratio.

Pilot Optimization for Estimation of High-Mobility OFDM Channels

Obtaining channel state information is very crucial for realizing high-performance high-rate wireless communications. For an orthogonal frequency-division multiplexing (OFDM) system operating in a high-mobility environment such as in high-speed trains, a sequence of pilot samples is inserted in each OFDM symbol to track the fast-varying channel responses. For such a high-mobility environment, the design of pilot sequence to minimize the mean squared error (MSE) of the channel estimate under a linear minimum mean squared error (LMMSE) estimator poses a difficult polynomial fractional optimization problem. In this paper, we develop a path-following optimization procedure, which improves the MSE in every iteration and quickly converges at least to its locally-optimal solution. Each iterative solution is given in a closed form with very low computational complexity. The developed path-following procedure can also be adapted to design pilot sequences for the least-square and maximum-likelihood estimators. Extensive simulation results demonstrate the effectiveness and superior performance of the proposed solutions and algorithms when compared to the state-of-the-art algorithms in the literature.

Channel Reciprocity Calibration in TDD Hybrid Beamforming Massive MIMO Systems

Hybrid analog-digital (AD) beamforming structure is a very attractive solution to build low cost massive multiple-input multiple-output (MIMO) systems. Typically these systems use a set of fixed beams for transmission and reception to avoid the need to obtain channel state information at transmitter (CSIT) for each antenna element individually. However, such a method can not fully exploit the potential of hybrid AD beamforming systems. Alternatively, CSIT can be estimated by assuming a model for the propagation channel, whereas this model is only validated in millimeter-wave (mmWave) band thanks to its poor scattering nature. In this paper, we focus on time division duplex (TDD) systems with hybrid beamforming structure and propose a reciprocity calibration scheme that allows to acquire full CSIT. Different to existing CSIT acquisition methods, our approach does not require any assumption on the channel model and can, in theory, estimate the CSIT up to an arbitrary small error.

Joint precoding and scheduling algorithm for massive MIMO in FDD multi-cell network

Massive multiple-input multiple-output (MIMO) systems, in which base stations are equipped with a large number of antennas in a two-dimensional antenna array, is one of the most promising technologies to improve the spectral efficiency of the fifth generation mobile communication systems (5G). Since there is no short-term channel reciprocity, the frequency-division duplexing (FDD) massive MIMO system has to obtain channel state information with the help of uplink feedback with acceptable overhead. To cope with this issue, we propose the design of joint precoding and scheduling algorithm in FDD multi-cell network. In the algorithm, all the users are firstly grouped by the statistics of channel correlation matrix, then the inter-group interference exiting among intra-cell and inter-cell is eliminated at the first precoding stage. Then users are adaptively scheduled and beamformed at the second precoding stage based on the low dimension effective channel. Finally, the effectiveness of the proposed algorithm is presented through simulations.

Hybrid precoding with compressive sensing based limited feedback in massive MIMO systems

ABSTRACTMassive multiple‐input multiple‐output (MIMO) systems are an enabling technology for high capacity 5G cellular networks. However, the high hardware complexity and power consumption make it difficult to apply conventional MIMO precoding schemes in massive MIMO. Additionally, thanks to the large channel dimension, applying traditional limited feedback schemes for obtaining channel state information at the transmitter is a challenge. In this paper, we develop a low‐complexity hybrid precoding scheme for multiuser massive MIMO system in mmWave communication. We design the analog beamformer as the conjugate transpose of the downlink channel from base station to the users. Then zero‐forcing baseband processing is applied over the effective channel. We get the optimal power allocation policy and compare its performance with average power allocation based on channel state information at transmitter. The channel state information at transmitter is obtained via a compressive sensing based limited feedback strategy. The effectiveness of hybrid precoding relies on the feedback quality of channel state information, which relies on the compression ratio. We quantify the impact of compression ratio on precoder performance and derive an upper bound on the sum rate. Simulation results illustrate the performance of our proposed scheme and the effect of compressive sensing based limited feedback.

Energy-Efficient and Low Signaling Overhead Cooperative Relaying With Proactive Relay Subset Selection

Energy efficient wireless communications have recently received much attention, due to the ever-increasing energy consumption of wireless communication systems. In this paper, we propose a new energy-efficient cooperative relaying scheme that selects a subset of relays before data transmission, through proactive participation of available relays using their local timers. We perform theoretical analysis of energy efficiency under maximum transmission power constraint, using practical data packet length, and taking account of the overhead for obtaining channel state information, relay selection, and cooperative beamforming. We provide the expression of average energy efficiency for the proposed scheme, and identify the optimal number and location of relays that maximize energy efficiency of the system. A closed-form approximate expression for the optimal position of relays is derived. We also perform overhead analysis for the proposed scheme and study the impact of data packet lengths on energy efficiency. The analytical and simulation results reveal that the proposed scheme exhibits significantly higher energy efficiency as compared to direct transmission, best relay selection, all relay selection, and a state-of-the-art existing cooperative relaying scheme. Moreover, the proposed scheme reduces the signaling overhead and achieves higher energy savings for larger data packets.

4‐weighted fractional Fourier transform over doubly selective channels and optimal order selecting algorithm

An optimal order selecting scheme for 4-weighted fractional Fourier transform (4-WFRFT) over doubly selective channels is developed. First, the expression for carrier-to-interference ratio (CIR) is deduced and then through maximising the CIR the optimal order factor is obtained. Through selecting the optimal order factor of 4-WFRFT, this system can match the doubly selective channel characteristics through switching between orthogonal frequency division multiplexing (OFDM) and single carrier (SC) systems according to obtained channel state information. The simulation results show that the optimal order 4-WFRFT scheme can improve the performance over doubly selective channels with respect to the traditional SC or OFDM.

Closed-Loop Beam Alignment for Massive MIMO Channel Estimation

Training sequences are designed to probe wireless channels to obtain channel state information for block-fading channels. Optimal training sounds the channel using orthogonal beamforming vectors to find an estimate that optimizes some cost function, such as mean square error. As the number of transmit antennas increases, however, the training overhead becomes significant. This creates a need for alternative channel estimation schemes for increasingly large transmit arrays. In this work, we relax the orthogonal restriction on sounding vectors. The use of a feedback channel after each forward channel use during training enables closed-loop sounding vector design. A misalignment cost function is introduced, which provides a metric to sequentially design sounding vectors. In turn, the structure of the sounding vectors aligns the transmit beamformer with the true channel direction, thereby increasing beamforming gain. This beam alignment scheme for massive MIMO is shown to improve beamforming gain over conventional orthogonal training for a MISO channel.

An ICVBPNN Algorithm for Time-varying Channel Tracking and Prediction

An improved complex-valued back propagation neural network (ICVBPNN) algorithm is proposed in this paper. In allusion to the defect of gradient descent of traditional complex-valued back propagation network (CVBPNN) algorithm, additive momentum has been introduced. It is used for time-varying channel tracking and prediction in wireless communication system and better application results are acquired. Firstly, with the use of the learning ability of the neural network, the tracking training is started based on the obtained channel state information (CSI), thus the nonlinear channel model is constructed. Secondly, the unknown channel state information is predicted using the ICVBPNN trained model. The simulation results demonstrate that the proposed method has less estimated error, and can track the channel more accurately than the traditional CVBPNN and the Kalman Filter algorithm.

Diversity-Multiplexing Tradeoff for Opportunistic Access Systems

This letter examines the diversity-multiplexing tradeoff (DMT) for the opportunistic access system (OAS) with a delay-constrained traffic. In particular, the OAS adopts a random access framework: The transmitter first performs channel probing (CP) slot by slot, and in each time slot, it tries to access one of the candidate channels; when the CP is successful, it will decide to transmit or give up for another round of CP, based on the obtained channel state information (CSI). Under the above model, the outage probability of the OAS is derived for both the cases with zero and perfect CSI at the transmitter, respectively. Then, the DMT of the considered system is obtained under both the finite and infinite signal-to-noise ratio (SNR) regimes. It is shown by analysis that multi-round CP together with perfect transmitter side CSI can significantly improve the system performance.

Two-way cooperative communications with statistical channel knowledge

We consider the problem of distributed beamforming for a two-way relay network which consists of two transceivers and multiple relay nodes. The main assumption in this work, which differentiates it from previously reported results, is that one of the transceivers is assumed to have only statistical information about channels between the other transceiver and the relay nodes. This assumption imposes less stringent restrictions on the bandwidth required to obtain channel state information via training. Based on this statistical modeling, we propose to use a chance-constrained programming approach to design a distributed beamforming algorithm. In this approach, we aim to minimize the total transmit power (consumed in the entire network) as perceived by one of the transceivers, subject to two probabilistic constraints. These constraints guarantee that the outage probability of the transceivers' received SNRs, as perceived by the master transceiver, is not less than certain given thresholds. We prove rigorously that such an approach leads to a relay selection algorithm where the relay with the strongest channel coefficient to the master transceiver participates in relaying and the remaining relays are shut off. As such, the optimal distributed beamforming algorithm is simplified to a power control solution. Closed-form solution to this problem is obtained and its performance is evaluated through numerical examples.

Comparison of Practical Feedback Algorithms for Multiuser MIMO

We consider the problem of obtaining channel state information (CSI) via a fast feedback link for transmission on the downlink of a multiuser MIMO system. We examine the relative merits of channel feedback schemes based on Shannon's source-channel separation principle (digital schemes) and non-separation based schemes and show that the latter are preferable in this application when small to moderate bandwidth expansion ratios are used as in many current cellular systems. For comparison, we first compute upper-bounds on the performance of the system as a function of SNR. For the non-separation based schemes, we first consider a simple analog transmission and then develop a hybrid digital-analog transmission scheme which quantizes the CSI using a few bits and sends these bits and also the quantization error using analog transmission. We show that the hybrid scheme achieves a higher throughput compared to both analog and digital transmissions and has a much lower computational complexity compared to a digital scheme.

Estimation of CSI for LST BS-CDMA systems using special sequences with pre-defined spectral nulls

Layered space-time block spread CDMA (LST BSCDMA) has been previously proposed as an alternative to conventional CDMA systems for high-rate mobile communications. This paper proposes two approaches to obtain channel state information (CSI) for LST BS-CDMA system. The basic approach suffers from high computational complexity and poor performance in systems with high order of transmitter diversity due to inter-layer interference (ILI). The second approach reduces computation complexity and eliminates ILI, making it suitable for systems with higher order of transmitter diversity. The detailed simulation studies show that the highly accurate CSI obtained using the improved technique leads to considerable superior performance.

An overview of limited feedback in wireless communication systems

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

Multiuser diversity with capture for wireless networks: protocol and performance analysis

In a wireless network, with the aid of rate adaptation, multiuser diversity can be exploited by allowing the mobile user with the best channel to use the channel. However, the overhead that results from polling mobile stations to obtain channel state information (CSI) in large networks can outweigh the multiuser diversity gain. In this paper, we propose a wireless medium access control protocol, namely multiuser diversity with capture (MDC), which explicitly employs the capture effect to obviate the overhead problem. We analyze the good put performance of MDC and compare it with the medium access diversity (MAD) scheme proposed in the literature. Our results show that MDC is effective in networks with radio receivers possessing reasonably good capture properties and in networks where the number of mobile stations is reasonably large.

Statistical CSI-assisted multiuser scheduling in MIMO broadcast channels

A multiuser scheduling algorithm in multiple input multiple output (MIMO) broadcast channels (BCs) is presented. It is likely that there is a time difference between obtaining channel state information (CSI) and using that CSI at the transmitter. This causes mismatches of the channel estimate in a temporally correlated MIMO BC. The proposed algorithm can minimise the effect of the delay-induced channel estimation error by exploiting statistical CSI for allocating multiple users into spatio-temporal slots in a cross-layer way.