Transmitter Channel State Information Research Articles

Hard Successive Interference Cancellation for M-QAM MIMO Links in the Presence of Rayleigh Deep-Fading

In our paper, we propose a generalized version of the Alternating Projections Digital Hard Successive Interference Cancellation (AP-HSIC) algorithm that is capable of decoding any order of constellation M in an M-Quadrature Amplitude Modulation (QAM) system. Our approach applies to Rayleigh deep-fading Multiple-Input Multiple-Output (MIMO) channels with high-level Additive White Gaussian Noise (AWGN). It can handle various destructive phenomena without restricting the number of antenna arrays in the transmitter/receiver. Importantly, it does not rely on closed-loop MIMO feedback or the need for Channel-State Information Transmission (CSIT). We have demonstrated the effectiveness of our approach and provided a Bit Error Rate (BER) analysis for 16-, 32-, and 64-QAM modulation systems. Real-time simulations showcase the differences and advantages of our proposed algorithm compared to the Multi-Group Space-Time Coding (MGSTC) decoding algorithm and the Lagrange Multipliers Hard Successive Interference Cancellation (LM-HSIC) algorithm, which we have also developed here. Additionally, our paper includes a mathematical analysis of the LM-HSIC algorithm. The AP-HSIC algorithm is not only effective and fast in decoding, including interference cancellation computational feedback, but it can also be integrated with any Linear Processing Complex Orthogonal Design (LPCOD) technique, including Complex Orthogonal Design (COD) schemes such as high-order Orthogonal Space–Time Block Code (OSTBC) with high-order QAM symbols.

Respiration Monitoring in High-Dynamic Environments via Combining Multiple WiFi Channels Based on Wire Direct Connection Between RX/TX

As one widely applied wireless technique, WiFi has the potential to execute noncontact monitoring of vital signs based on channel state information (CSI). However, due to the dynamic of the surrounding environment, the bandwidth of the WiFi channel is not enough to identify the respiration-induced path from other movement-induced paths and this seriously limits the accuracy of respiration rate detection. In this article, we propose ExRadio, a system that can monitor respiration in high-dynamic environments via combining multiple WiFi channels. Specifically, the receiver synchronously switches the channels with the transmitter and samples CSI at multiple channels, and then the CSI data are combined and regarded as CSI data of one extended-bandwidth channel. However, the hardware-related noises from multiple channels are also accumulated. Eliminating these noises causes too heavy computation overhead to be afforded by the embedded devices and affects the real-time performance of respiration monitoring. To address this problem, we propose an effective approach that employs the ratio of CSI readings from the wireless channel and wire direct connection channel to shorten the time of eliminating the hardware-related noise. We deploy the ExRadio in commercial off-the-shelf embedded devices and conduct a series of experiments. The experimental results demonstrate that reducing the execution time is beneficial to respiration rate detection under high-dynamic environments, and the overall detection error of ExRadio is less than 0.5 bpm even when multiple persons who are 1.5-m away from the monitored person are fast walking.

Comments and Corrections to “Capacity of Multiple-Antenna Systems With Both Receiver and Transmitter Channel State Information”

The correspondence cited in the above article derived ergodic capacity of the coherent multiple-input multiple-output (MIMO) channel in independent and identically distributed (IID) Rayleigh fading. While the theoretical results are correct, several plots in the paper are incorrect. In this correspondence, we correct the plots. More importantly, the corrected plots present an interesting and compelling contrast between performances of the coherent MIMO systems with and without channel state information at the transmitter; whereas this view is somewhat limited in the above article because of flaws in the capacity curves.

Information Bottleneck for a Rayleigh Fading MIMO Channel with an Oblivious Relay

This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel with an oblivious relay. The relay is constrained to operating without knowledge of the codebooks, i.e., it performs oblivious processing. Moreover, due to the bottleneck constraint, it is impossible for the relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we first provide an upper bound by assuming that the destination node can obtain a perfect CSI at no cost. Then, we provide four achievable schemes, where each scheme satisfies the bottleneck constraint and gives a lower bound to the bottleneck rate. In the first and second schemes, the relay splits the capacity of the relay–destination link into two parts and conveys both the CSI and its observation to the destination node. Due to CSI transmission, the performance of these two schemes is sensitive to the MIMO channel dimension, especially the channel input dimension. To ensure that it still performs well when the channel dimension grows large, in the third and fourth achievable schemes, the relay only transmits compressed observations to the destination node. Numerical results show that, with simple symbol-by-symbol oblivious relay processing and compression, the proposed achievable schemes work well and can demonstrate lower bounds that come quite close to the upper bound on a wide range of relevant system parameters.

Generalized Space–Time Line Code With Receive Combining for MIMO Systems

In this article, a generalized space–time line code (GSTLC) is proposed. The proposed GSTLC concurrently transmits multiple data streams obtained by space–time line code encoding and precoding to provide spatial multiplexing and diversity. When full channel state information (CSI) is available to the transmitter and limited CSI is available to the receiver, an optimal precoding matrix is derived for GSTLC with linear combining that is based on the minimum mean square error (MMSE) criterion. Moreover, the detection signal-to-interference-plus-noise ratio (SINR) is derived corresponding to the optimal MMSE precoder. Using the SINR, we propose a receiver combining scheme that selects the best combining matrix, thereby minimizing the bit error rate (BER) from a codebook that includes candidate combining matrices. Through numerical simulations, we demonstrate that the proposed GSTLC with the optimal combiner achieves the theoretical lower bound of BER. In addition, we show that the GSTLC with combiner selection performs better than conventional precoded spatial multiplexing methods in terms of the BER.

Information Bottleneck for a Rayleigh Fading MIMO Channel with an Oblivious Relay

This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel with an oblivious relay. The relay is constrained to operate without knowledge of the codebooks, i.e., it performs oblivious processing. Moreover, due to the bottleneck constraint, it is impossible for the relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we first provide an upper bound by assuming that the destination node can get the perfect CSI at no cost. Then, we provide four achievable schemes where each scheme satisfies the bottleneck constraint and gives a lower bound to the bottleneck rate. In the first and second schemes, the relay splits the capacity of the relay-destination link into two parts, and conveys both the CSI and its observation to the destination node. Due to CSI transmission, the performance of these two schemes is sensitive to the MIMO channel dimension, especially the channel input dimension. To ensure that it still performs well when the channel dimension grows large, in the third and fourth achievable schemes, the relay only transmits compressed observation to the destination node. Numerical results show that with simple symbol-by-symbol oblivious relay processing and compression, the proposed achievable schemes work well and can demonstrate lower bounds coming quite close to the upper bound on a wide range of relevant system parameters.

Broadcast Approach for the Information Bottleneck Channel

This work considers a layered coding approach for efficient transmission of data over a wireless block fading channel without transmitter channel state information (CSI), which is connected to a limited capacity reliable link, known as the bottleneck channel. Two main approaches are considered, the first is an oblivious approach, where the sampled noisy observations are compressed and transmitted over the bottleneck channel without having any knowledge of the original information codebook. The second approach is a non-oblivious decode-forward (DF) relay where the sampled noisy data is decoded, and whatever is successfully decoded is reliably transmitted over the bottleneck channel. The bottleneck channel from relay to destination has a fixed capacity $C$ . We examine also the case where the channel capacity can dynamically change due to variable loads on the backhaul link. The broadcast approach is analyzed for cases that only the relay knows the available capacity for next block, and for the case that neither source nor relay know the capacity per block, only its capacity distribution. Fortunately, it is possible to analytically describe in closed form expressions, the optimal continuous layering power distribution which maximizes the average achievable rate. Numerical results demonstrate the achievable broadcast rates.

Transient Performance Limits for Ultra-Reliable Low-Latency Communications Over Fading Channels

In this paper, we develop the transient performance limits of wireless transmissions for ultra-reliable low-latency communications (URLLC). In particular, we derive the exact statistics of the maximum amount of data that can be delivered during a fixed time duration over fading channels, for both channel state information (CSI) at transmitter side and CSI at receiver side only scenarios. Following a recently proposed data-oriented approach, we then analyze the performance of sample ideal transmission schemes in terms of URLLC support. Selected numerical examples are presented to examine the effect of power adaptation and rateless coding on transient transmission performance over fading channels.

Deep UL2DL: Data-Driven Channel Knowledge Transfer From Uplink to Downlink

To remove the need for signaling overhead of feedback channels for transmitter channel state information (CSI) in Frequency Division Duplexing (FDD), we propose using convolutional neural networks and generative adversarial networks (GANs) to infer the downlink (DL) CSI by observing the uplink (UL) CSI. Our data-driven scheme exploits the fact that both DL and UL channels share the same propagation environment. As such, we extracted the environment information from UL channel response to a latent domain and then transferred the derived environment information from the latent domain to predict the DL channel. To prevent incorrect latent domain and the problem of oversimplistic assumptions, we did not use any specific parametric model and, instead, used data-driven approaches to discover the underlying structure of data without any prior model assumptions. To overcome the challenge of capturing the UL-DL joint distribution, we used a mean square error-based variant of the GAN structure with improved convergence properties called boundary equilibrium GAN. For training and testing we used simulated data of Extended Vehicular-A (EVA) and Extended Typical Urban (ETU) models. Simulation results verified that our methods can accurately infer and predict the downlink CSI from the uplink CSI for different multipath environments.

Maximum-Throughput Sidelink Resource Allocation for NR-V2X Networks With the Energy-Efficient CSI Transmission

New radio vehicle-to-everything (NR-V2X) is an emerging technology based on the 5G cellular mobile communication networks. In this paper, the sidelink (SL) resource allocation for NR-V2X in Mode 1 is considered, where 5G base station (gNB) schedules SL resources to be used by V2X users (VUs) based on the periodically reported channel state information (CSI). To reduce the overhead, an energy efficient power allocation scheme of CSI transmission is proposed using hybrid spectrum access technology. With the knowledge of CSI, SL resource allocation is modeled as a mixed binary integer nonlinear programming (MBINP). It is designed to maximize the sum throughput of NR-V2X networks among different subcarriers subject to the total available power and the minimal transmission rate. To achieve that, the original MBINP is decomposed into two sub-problems, namely, subcarrier assignment and power allocation. Firstly, the appropriate subcarriers are quickly obtained by averaging the power allocation. Secondly, an alternative optimization mechanism is proposed to the power allocation. Simulation results show that the proposed power allocation scheme of CSI transmission can obviously save the energy consumption, and the proposed suboptimal SL resource allocation algorithm achieves better performance than the compared methods with relatively low complexity.

On Achievable Distortion in Sending Gaussian Sources over a Bandwidth-Matched Gaussian MAC with No Transmitter CSI

This paper investigates the minimum mean square error (MMSE) of communicating a pair of Gaussian sources over a a bandwidth-matched Gaussian multiple-access channel with block Rayleigh fading in the absence of channel state information (CSI) at the transmitters. The achievable MMSE is not known. To this end, we derive several upper-bounds to the minimum achievable average MMSE as a function of the transmitter powers, the average channel fading power-to-noise ratio, and the correlation coefficient of the two sources. To derive nontrivial upper bounds which improve on those of separate source-channel coding and uncoded transmission, we incorporate ideas from joint source-channel coding and hybrid digital–analog coding to construct specific coding schemes for which the achievable MMSE can be determined.

A CSI-Based Indoor Fingerprinting Localization with Model Integration Approach.

Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.

Interleaving Channel Estimation and Limited Feedback for Point-to-Point Systems With a Large Number of Transmit Antennas

We introduce and investigate the opportunities of multi-antenna communication schemes whose training and feedback stages are interleaved and mutually interacting. Specifically, unlike the traditional schemes, where the transmitter first trains all of its antennas at once and then receives a single feedback message, we consider a scenario, where the transmitter instead trains its antennas one by one and receives feedback information immediately after training each one of its antennas. The feedback message may ask the transmitter to train another antenna; or, it may terminate the feedback/training phase and provide the quantized codeword (e.g., a beamforming vector) to be utilized for data transmission. As a specific application, we consider a multiple-input single-output system with $t$ transmit antennas, a short-term power constraint $P$ , and target data rate $\rho $ . We show that for any $t$ , the same outage probability as a system with perfect transmitter and receiver channel state information can be achieved with a feedback rate of $R_{1}$ bits per channel state and via training $R_{2}$ transmit antennas on average, where $R_{1}$ and $R_{2}$ are independent of t , and depend only on $\rho $ and $P$ . In addition, we design variable-rate quantizers for channel coefficients to further minimize the feedback rate of our scheme.

Energy Harvesting Communications Without Transmitter Channel State Information Using Layered Coding

In this paper, we optimize an energy harvesting transmitter, communicating over a slow fading channel, using layered coding. The transmitter has access to the channel statistics, but does not know the exact channel state. In layered coding, the codewords are first designed for a given finite set of channel states at different rates, and then the codewords are either time-multiplexed or superimposed before the transmission, leading to two transmission strategies. The receiver then decodes the information adaptively based on the realized channel state. The transmitter is equipped with a finite-capacity battery having non-zero internal resistance. In each of the transmission strategies, we first formulate and study an average rate maximization problem with non-causal knowledge of the harvested power variations. We also highlight the structural properties of the optimal solutions. Further, assuming statistical knowledge and causal information of the harvested power variations, we propose a sub-optimal algorithm, and compare it with the stochastic dynamic programming-based solution and a greedy policy. By numerical simulations, we demonstrate that the average rate decreases by approximately 20% when the internal resistance of the battery is increased from 0 ${\Omega }$ to 50 ${\Omega }$ in all the policies.

Research on I / Q Unbalanced Channel Reciprocity Calibration Algorithm in Massive MIMO System

Massive MIMO system uses the transmitter channel state information (CSI) to preprocess the transmitted data, which can effectively reduce the interference and enhance the Massive MIMO system capacity. In the actual transmission process, reciprocity is subject to many factors, resulting in application difficulties. In this paper, we mainly study the influence of RF device’s noncardiosis on channel reciprocity of Massive MIMO system. Combined with channel estimation based on pilot signal and FRRH subspace tracking algorithm, this paper proposes a channel reciprocity calibration method for Massive MIMO. Finally, the compensation effect of the compensation algorithm is verified by simulation.

Relay Pre-Selection for Reducing CSI Transmission in Wireless Sensor Networks

Relay selection requires collection and transmission of channel state information (CSI) to a central node. When all relay nodes contribute in CSI transmission, the amount of power required for signaling the CSIs linearly increases with the number of relays. A central node should determine the proper number of pre-selected relays by aid of channel statistics and a decision criterion. We derive an upper bound on this number when end-to-end bit error rate is considered as the decision criterion and the source-relay channels are time-varying fading channels.

Capacity Analysis of One-Bit Quantized MIMO Systems With Transmitter Channel State Information

With bandwidths on the order of a gigahertz in emerging wireless systems, high-resolution analog-to-digital convertors (ADCs) become a power consumption bottleneck. One solution is to employ low resolution one-bit ADCs. In this paper, we analyze the flat fading multiple-input multiple-output (MIMO) channel with one-bit ADCs. Channel state information is assumed to be known at both the transmitter and receiver. For the multiple-input single-output channel, we derive the exact channel capacity. For the single-input multiple-output and MIMO channel, the capacity at infinite signal-to-noise ratio (SNR) is found. We also derive upper bound at finite SNR, which is tight when the channel has full row rank. In addition, we propose an efficient method to design the input symbols to approach the capacity achieving solution. We incorporate millimeter wave channel characteristics and find the bounds on the infinite SNR capacity. The results show how the number of paths and number of receive antennas impact the capacity.

Guaranteeing Positive Secrecy Capacity for MIMOME Wiretap Channels With Finite-Rate Feedback Using Artificial Noise

While the impact of finite-rate feedback on the capacity of fading channels has been extensively studied in the literature, not much attention has been paid to this problem under secrecy constraint. In this work, we study the ergodic secret capacity of a multiple-input multiple-output multiple-antenna-eavesdropper (MIMOME) wiretap channel with quantized channel state information (CSI) at the transmitter and perfect CSI at the legitimate receiver, under the assumption that only the statistics of eavesdropper CSI is known at the transmitter. We refine the analysis of Lin et al. 's random vector quantization (RVQ) based artificial noise (AN) scheme, where a heuristic upper bound on the secrecy rate loss (compared to the perfect CSI case) was given. We propose a lower bound on the ergodic secrecy capacity. We show that the lower bound and the secrecy capacity with perfect CSI coincide asymptotically as the number of feedback bits and the AN power go to infinity. For practical applications, we propose a very efficient quantization codebook construction method for the two transmit antennas case.

A New Proof for the DoF Region of the MIMO Networks With No CSIT

In this paper, a new proof for the degrees of freedom (DoF) region of the K-user multiple-input multiple-output (MIMO) broadcast channel (BC) with no channel state information at the transmitter (CSIT) and perfect channel state information at the receivers (CSIR) is provided. Based on this proof, the capacity region of a certain class of MIMO BC with channel distribution information at the transmitter (CDIT) and perfect CSIR is derived. Finally, an outer bound for the DoF region of the K-user MIMO interference channel (IC) with no CSIT is provided.

Distributed Transmission Probability Control in Fading Channel with State Information

A wireless system consisting of a finite number of heterogeneous users transmitting packets over a slotted ALOHA Rayleigh-fading channel is investigated in this paper. In this system, each user tries to minimize its number of transmission attempts while meeting the throughput demand. The user interaction in such an ALOHA network is modeled as a random access game. A novel technique to better exploit the channel state information (CSI) named adaptive CSI transmission method is proposed. Benchmarking with the no-CSI method and threshold-based CSI method, it is revealed that the proposed adaptive CSI method can yield up to 9.9% more throughput and 12.3% power reduction. Two simple yet effective guidelines on selecting among these CSI-related methods are formulated for systems with different SNRs and capture ratios. A distributed algorithm is proposed to find the optimal transmission probability. Both analytical and simulation results show that the algorithm exhibits fast convergence speed and is robustness against changes on the number of active nodes in the network.