Channel State Information Acquisition Research Articles

Deterministic Pilot Design and Channel Estimation for Downlink Massive MIMO-OTFS Systems in Presence of the Fractional Doppler

Although the combination of the orthogonal time frequency space (OTFS) modulation and the massive multiple-input multiple-output (MIMO) technology can make communication systems perform better in high-mobility scenarios, there are still many challenges in downlink channel estimation owing to inaccurate modeling and high pilot overhead in practical systems. In this paper, we propose a channel state information (CSI) acquisition scheme for downlink massive MIMO-OTFS in presence of the fractional Doppler, including deterministic pilot design and channel estimation algorithm. First, we analyze the input-output relationship of the single-input single-output (SISO) OTFS based on the orthogonal frequency division multiplexing (OFDM) modem and extend it to massive MIMO-OTFS. Moreover, we formulate an accurate model for the practical system in which the fractional Doppler is considered and the influence of subpaths is revealed. A deterministic pilot design is then proposed based on the model and the structure of the pilot matrix to reduce pilot overhead and save memory consumption. Since channel geometry changes very slowly relative to the communication timescale, we put forward a modified sensing matrix based channel estimation (MSMCE) algorithm to acquire the downlink CSI. Simulation results demonstrate that the proposed downlink CSI acquisition scheme has significant advantages over traditional algorithms.

Historical Development of Spatial Modulation and Massive MIMO Communication System with Implementation Challenges: A Review

: For wireless communication networks, massive MIMO (Multiple-Input Multiple- Output) technology has been considered a major achievement. MIMO Technology consists of equipping a base station with a large number of antennas to serve many active users. Through MIMO Technology, the ability to concentrate on transmitted signal energy in very short spaces would result in huge increases in system capacity. While this new concept provides some fascinating benefits, it creates a new tension that has drawn both academia and industry attention. Channel state information acquisition, channel feedback, instant reciprocity, statistical reciprocity, architecture and hardware impairments are few of them. This paper presents a survey on Spatially Modulated Massive MIMO's historical development.

Rank-1 Matrix Approximation-Based Channel Estimation for Intelligent Reflecting Surface-Aided Multi-User MISO Communications

The acquisition of channel state information is an essential step in enabling intelligent reflecting surfaces (IRSs) for wireless communications. In this letter, we introduce a novel procedure to estimate the cascaded channel between the base station (BS), IRS, and users in a multi-user multiple-input single-output system. The common BS-IRS channel, over which all users transmit, is leveraged to decompose the cascaded channel into a series of rank-1 matrices. Low-rank matrix recovery methods are utilized to improve upon the linear minimum mean-squared error estimate of the cascaded channel. A theoretical upper bound for the mean-square error (MSE) of the proposed estimator is derived. Numerical results reveal that the proposed techniques outperform the existing counterparts in terms of the MSE and scale with the number of BS antennas.

An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

Toward Environment-Aware 6G Communications via Channel Knowledge Map

This article proposes the concept of channel knowledge map (CKM) as an enabler toward environment-aware wireless communications. CKM is a site-specific database, tagged with the locations of the transmitters and/or receivers, that contains channel-related information useful to enhance environment-awareness and facilitate or even obviate sophisticated real-time channel state information (CSI) acquisition. Therefore, CKM is expected to play an important role for 6G networks targeting for super high capacity, extremely low latency, and ultra-massive connectivity, by offering potential solutions to practical challenges brought by the drastically increased channel dimensions and training overhead. In this article, the motivations of environment-awareness enabled by CKM are first discussed, followed by the key techniques to build and utilize CKM. In particular, it is highlighted that CKM is especially appealing for four channel types: channels for yet-to-reach locations, channels for non-cooperative nodes, channels with large dimensions, and channels with severe hardware/processing limitations. Two case studies with extensive numerical results are presented to demonstrate the great potential of environment-aware communications enabled by CKM.

Channel estimation for reconfigurable intelligent surface assisted wireless communication systems in mobility scenarios

Reconfigurable intelligent surface (RIS) can manipulate the wireless propagation environment by smartly adjusting the amplitude/phase in a programmable panel, enjoying the improved performance. The accurate acquisition of the instantaneous channel state information (CSI) in the cascaded RIS chain makes an indispensable contribution to the performance gains. However, it is quite challenging to estimate the CSI in a time-variant scenario due to the limited signal processing capability of the passive elements embedded in a RIS pannel. In this work, a channel estimation scheme for the RIS-assisted wireless communication system is proposed, which is demonstrated to perform well in a time-variant scenario. The cascaded RIS channel is modeled as a state-space model based upon the mobility situations. In addition, to fully exploit the time correlation of channel, Kalman filter is employed by taking the prior information of channels into account. Further, the optimal reflection coefficients are derived according to the minimum mean square error (MMSE) criterion. Numerical results show that the proposed methods exhibit superior performance if compared with a conventional channel estimation scheme.

Energy-Efficient Millimeter-Wave Cell-Free Systems Under Limited Feedback

MmWave cell-free systems where multiple base stations (BSs) cooperatively serve user using the mmWave band signal have gained much attention recently due to its capability to dramatically improve the system capacity. One potential drawback of mmWave cell-free systems is that an intensive deployment of BSs increases the energy consumption of network substantially. To improve the energy efficiency, acquisition of accurate downlink channel state information (CSI) at the BSs is essential. However, this task is not easy since the CSI feedback overhead scales linearly with the number of antennas as well as the number of BSs. In this paper, we propose an approach to maximize the energy efficiency of mmWave cell-free systems under the limited feedback. Key idea of the proposed energy-efficient dominating path selection (EE-DPS) algorithm is to choose a small number of paths in the angular domain channel and then exploit the channel information of chosen paths in the data precoding and power allocation. By choosing the dominating paths maximizing the energy efficiency and then feeding back the components of chosen paths, we achieve a considerable reduction in the feedback overhead. Numerical results demonstrate that EE-DPS achieves more than 80% energy efficiency improvement over the conventional CSI feedback-based schemes.

Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networks

Abstract In cooperative massive multiple-input multiple-output (MIMO) networks, channel statistics based adaptive feedback can considerably reduce feedback overhead for channel state information (CSI) acquisition as well as backhaul overhead for CSI sharing. When regularized zero-forcing beamforming is considered to coordinate interference with the skewed codebook, average sum rate depends on not only regularization parameters, but also quantization error impacts of serving and interfering channels according to their channel covariance matrices. To improve the average sum rate by effectively controlling the desired signal strength and the interference cancellation, we propose joint optimization of regularization parameters and feedback bit allocation by leveraging adaptive feedback according to the channel covariance matrices.

Reconfigurable-Intelligent-Surface Empowered Wireless Communications: Challenges and Opportunities

Reconfigurable intelligent surfaces (RISs) are regarded as a promising emerging hardware technology to improve the spectrum and energy efficiency of wireless networks by artificially reconfiguring the propagation environment of electromagnetic waves. Due to the unique advantages in enhancing wireless channel capacity, RISs have recently become a hot research topic. In this article, we focus on three fundamental physical-layer challenges for the incorporation of RISs into wireless networks, namely, channel state information acquisition, passive information transfer, and low-complexity robust system design. We summarize the state-of-the-art solutions and explore potential research directions. Furthermore, we discuss other promising research directions of RISs, including edge intelligence and physical-layer security.

Low-Complexity Channel Estimation for Intelligent Reflecting Surface-Enhanced Massive MIMO

Intelligent reflecting surface (IRS) consisting of massive passive elements is a promising technology to save the hardware cost and energy consumption, but with a challenging channel estimation of IRS transmission links. In this letter, a low-complexity channel estimation method is first proposed for IRS-enhanced multi-user massive multiple-input and multiple-output systems, which can attain the separate channel state information (CSI) of the cascaded link. The acquisition of the separate CSI contributes to the flexibility of channel estimations for the multi-user case with user mobility. The proposed channel estimation consists of direction-of-arrival and path gain estimations, which are implemented by using the limited radio frequency chains and pilots, respectively. Besides, the computational complexity is analyzed for the proposed and existing methods for comparison. Numerical results show that the proposed method can achieve accurate channel estimation with a low complexity.

User Coordination for Fast Beam Training in FDD Multi-User Massive MIMO

Massive multiple-input multiple-output (mMIMO) communications are one of the enabling technologies of 5G and beyond networks. While prior work indicates that mMIMO networks employing time division duplexing have a significant capacity growth potential, deploying mMIMO in frequency division duplexing (FDD) networks remains problematic. The two main difficulties in FDD networks are the scalability of the downlink reference signals and the overhead associated with the required uplink feedback for channel state information (CSI) acquisition. To address these difficulties, most existing methods utilize assumptions on the radio environment such as channel sparsity or angular reciprocity. In this work, we propose a novel cooperative method for a scalable and low-overhead approach to FDD mMIMO under the so-called grid-of-beams architecture. The key idea behind our scheme lies in the exploitation of the near-common signal propagation paths that are often found across several mobile users located in nearby regions, through a coordination mechanism. In doing so, we leverage the recently specified device-to-device communications capability in 5G networks. Specifically, we design beam selection algorithms capable of striking a balance between CSI acquisition overhead and multi-user interference mitigation. The selection exploits statistical information, through so-called covariance shaping. Simulation results demonstrate the effectiveness of the proposed algorithms, which prove particularly well-suited to rapidly-varying channels with short coherence time.

CSI-Free vs CSI-Based Multi-Antenna WET for Massive Low-Power Internet of Things

Wireless Energy Transfer (WET) is a promising solution for powering massive Internet of Things deployments. An important question is whether the costly Channel State Information (CSI) acquisition procedure is necessary for optimum performance. In this paper, we shed some light into this matter by evaluating CSI-based and CSI-free multi-antenna WET schemes in a setup with WET in the downlink, and periodic or Poisson-traffic Wireless Information Transfer (WIT) in the uplink. When CSI is available, we show that a maximum ratio transmission beamformer is close to optimum whenever the farthest node experiences at least 3 dB of power attenuation more than the remaining devices. On the other hand, although the adopted CSI-free mechanism is not capable of providing average harvesting gains, it does provide greater WET/WIT diversity with lower energy requirements when compared with the CSI-based scheme. Our numerical results evidence that the CSI-free scheme performs favorably under periodic traffic conditions, but it may be deficient in case of Poisson traffic, specially if the setup is not optimally configured. Finally, we show the prominent performance results when the uplink transmissions are periodic, while highlighting the need of a minimum mean square error equalizer rather than zero-forcing for information decoding.

Fully connected feedforward neural networks based CSI feedback algorithm

In modern wireless communication systems, the accurate acquisition of channel state information (CSI) is critical to the performance of beamforming, non-orthogonal multiple access (NOMA), etc. However, with the application of massive MIMO in 5G, the number of antennas increases by hundreds or even thousands times, which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme. In this paper, by using deep learning technology, we develop a system framework for CSI feedback based on fully connected feedforward neural networks (FCFNN), named CF-FCFNN. Through learning the training set composed of CSI, CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.

Multipair Relaying With Space-Constrained Large-Scale MIMO Arrays: Spectral and Energy Efficiency Analysis With Incomplete CSI

In this paper, we study a multi-pair two-way large-scale multiple-input multiple-output (MIMO) decode-and-forward relay system. Multiple single-antenna user pairs exchange information via a shared relay working at half-duplex. The proposed scenario considers a practical case where an increasing number of antennas is deployed in a fixed physical space, giving rise to a trade-off between antenna gain and spatial correlation. The channel is assumed imperfectly known, and the relay employs linear processing methods. We study the large-scale approximations of the sum spectral efficiency (SE) and investigate the energy efficiency (EE) with a practical power consumption model when the number of relay antennas becomes large. We demonstrate the impact of the relay antenna number and spatial correlation with reducing inter-antenna distance on the EE performance. We exploit the increasing spatial correlation to allow an incomplete channel state information (CSI) acquisition where explicit CSI is acquired only for a subset of antennas. Our analytical derivations and numerical results show that applying the incomplete CSI strategy in the proposed system can improve the EE against complete CSI systems while maintaining the average SE performance.

Distributed Precoding Design via Over-the-Air Signaling for Cell-Free Massive MIMO

Most works on cell-free massive multiple-input multiple-output (MIMO) consider non-cooperative precoding strategies at the base stations (BSs) to avoid extensive channel state information (CSI) exchange via backhaul signaling. However, considerable performance gains can be accomplished by allowing coordination among the BSs. This paper proposes the first distributed framework for cooperative precoding design in cell-free massive MIMO (and, more generally, in joint transmission coordinated multi-point) systems that entirely eliminates the need for backhaul signaling for CSI exchange. A novel over-the-air (OTA) signaling mechanism is introduced such that each BS can obtain the same cross-term information that is traditionally exchanged among the BSs via backhaul signaling. The proposed distributed precoding design enjoys desirable flexibility and scalability properties, as the amount of OTA signaling does not scale with the number of BSs or user equipments. Numerical results show fast convergence and remarkable performance gains as compared with non-cooperative precoding design. The proposed scheme can also outperform the centralized precoding design under realistic CSI acquisition.

Randomized Approximate Channel Estimator in Massive-MIMO Communication

Massive MIMO is considered as one key enabling technology in 5G communications. Although the high-quality channel state information (CSI) estimation is essential to improve the energy/spectrum efficiency of massive MIMO systems, the optimal MMSE estimator unfortunately creates one major challenge, due to its formidable computation complexity. In this letter, we propose a rank-restrained low-complexity MMSE channel estimator for massive MIMO communications, leveraging a novel concept of randomized low-rank approximation. To accomplish this, we first design a two-stage pilot training scheme. Rather than directly estimating the large channel matrix as usual, we then acquire two low-dimensional subsets of it, which respectively gives the row and column sampling version of unknown channel matrix. Finally, we reconstruct the complete channel matrix via such two estimated low-dimensional sketches, by exploiting the low-rank structure of channel. As demonstrated, our scheme significantly reduces the time complexity as well as the energy consumption in CSI acquisition, which yet attains the near-optimal estimation accuracy. It thus provides great promises to the practical deployment of massive MIMO communications.

Sum Secret Key Rate Maximization for TDD Multi-User Massive MIMO Wireless Networks

Physical-layer key generation (PKG) based on channel reciprocity has recently emerged as a new technique to establish secret keys between devices. Most works focus on pairwise communication scenarios with single or small-scale antennas. However, the fifth generation (5G) wireless communications employ massive multiple-input multiple-output (MIMO) to support multiple users simultaneously, bringing serious overhead of reciprocal channel acquisition. This paper presents a multi-user secret key generation in massive MIMO wireless networks. We provide a beam domain channel model, in which different elements represent the channel gains from different transmit directions to different receive directions. Based on this channel model, we analyze the secret key rate and derive a closed-form expression under independent channel conditions. To maximize the sum secret key rate, we provide the optimal conditions for the Kronecker product of the precoding and receiving matrices and propose an algorithm to generate these matrices with pilot reuse. The proposed optimization design can significantly reduce the pilot overhead of the reciprocal channel state information acquisition. Furthermore, we analyze the security under the channel correlation between user terminals (UTs), and propose a low overhead multi-user secret key generation with non-overlapping beams between UTs. Simulation results demonstrate the near-optimal performance of the proposed precoding and receiving matrices design and the advantages of the non-overlapping beam allocation.

Constellation-Overlapping-Based Secure Transmission for Two-Way Untrusted Relaying: Method, Implementation, and Experimental Results

In this letter we present a physical-layer-security (PLS) experimental platform that realizes secure transmission over a two-way untrusted relaying system. A novel PLS approach called constellation overlapping is adopted to provide both confidentiality and reliability, and sophisticated protocols are developed as well to implement synchronization, channel state information (CSI) acquisition, self-interference cancellation, and signal recovery. Experimental results in the indoor environment are shown to demonstrate the effectiveness of our design.

Intelligent Reflecting Surface Aided Multicasting With Random Passive Beamforming

In this letter, we consider a multicast system where a single-antenna transmitter sends a common message to multiple single-antenna users, aided by an intelligent reflecting surface (IRS) equipped with N passive reflecting elements. Prior works on IRS have mostly assumed the availability of channel state information (CSI) for designing its passive beamforming. However, the acquisition of CSI requires substantial training overhead that increases with N. In contrast, we propose in this letter a novel random passive beamforming scheme, where the IRS performs independent random reflection for Q ≥ 1 times in each channel coherence interval without the need of CSI acquisition. For the proposed scheme, we first derive a closed-form approximation of the outage probability, based on which the optimal Q with best outage performance can be efficiently obtained. Then, for the purpose of comparison, we derive a lower bound of the outage probability with traditional CSI-based passive beamforming. Numerical results show that a small Q is preferred in the high-outage regime (or with high rate target) and the optimal Q becomes larger as the outage probability decreases (or as the rate target decreases). Moreover, the proposed scheme significantly outperforms the CSI-based passive beamforming scheme with training overhead taken into consideration when N and/or the number of users are large, thus offering a promising CSI-free alternative to existing CSI-based schemes.

Downlink channel estimation for millimeter wave communication combining low-rank and sparse structure characteristics

The acquisition of channel state information (CSI) is essential in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The mmWave channel exhibits sparse scattering characteristics and a meaningful low-rank structure, which can be simultaneously employed to reduce the complexity of channel estimation. Most existing works recover the low-rank structure of channels using nuclear norm theory. However, solving the nuclear norm-based convex problem often leads to a suboptimal solution of the rank minimization problem, thus degrading the accuracy of channel estimation. Previous contributions recover the channel using over-complete dictionary with the assumption that the mmWave channel can be sparsely represented under some dictionary. While over-complete dictionary may increase the computational complexity. To address these problems, we propose a channel estimation framework based on non-convex low-rank approximation and dictionary learning by exploring the joint low-rank and sparse representations of wireless channels. We surrogate the widely used nuclear norm theory with non-convex low-rank approximation method and design a dictionary learning algorithm based on channel feature classification employing deep neural network (DNN). Our simulation results reveal the proposed scheme outperform the conventional dictionary learning algorithm, Bayesian framework algorithm, and compressed sensing-based algorithms.