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

Large scale multiple-input multiple-output (MIMO) or Massive MIMO is one of the pivotal technologies for future wireless networks. However, the performance of massive MIMO systems heavily relies on accurate channel estimation. While the acquisition of channel state information (CSI) in such systems requires an increasingly large amount of training overhead as the number of users grows. To tackle this issue, in this paper, we propose a deep learning assisted blind channel estimation technique for orthogonal frequency division multiplexing (OFDM) based massive MIMO systems. We prove that by exploiting the asymptotic orthogonality of the massive MIMO channels, the channel distortion can be averaged out without the prior knowledge of channel impulse responses, and after some mathematical manipulation, different users' transmitted data symbols can be extracted. Thus, by deploying a denoising convolutional neural network algorithm (DnCNN), we mitigate a remaining channel and noise effect to accurately detect the transmitted data symbols at the channel sounding stage. Using the detected data symbols as virtual pilots, we estimate the CSI of all the users at each BS antennas. Our simulation results testify the efficacy of our proposed technique and demonstrate that it can provide a mean square error (MSE) performance which coincides with that of the data-aided channel estimation technique.

Multiple Input Multiple Output(MIMO) has been in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel with modified precoder and decoder has been considered when the channel state information (CSI) is considered partial. Due to delay in acquiring transmitted information at the receiver end, the time selective fading wireless channel often induces incomplete or partial CSI. The dynamic CSI model has also been implemented consisting channel mean and covariance which leads to extracting of channel estimates and error covariance which then further applied with the modified precoder and decoder utilizing both the parameters indicating the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partially or fully blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI which has been manipulated according to the precoder and decoder conditions. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new approach which maximizes the capacity of spatial channel model with modified precoder and decoder has been tried. The interference then interactively reduced by employing the iterative channel estimation and data detection approach, where by utilizing the detected symbols from the previous iteration, multiuser/MIMO channel estimation and symbol detection is improved.

Channel state information (CSI) at the base station (BS) is crucial to achieve beamforming and multiplexing gains in multiple-input multiple-output (MIMO) systems. State-of-the-art limited feedback schemes require feedback overhead that scales linearly with the number of BS antennas, which is prohibitive for 5G massive MIMO. This paper proposes novel limited feedback algorithms that lift this burden by exploiting the inherent sparsity in double-directional MIMO channel representation using overcomplete dictionaries. These dictionaries are associated with angle of arrival and angle of departure that specifically account for antenna directivity patterns at both ends of the link. The proposed algorithms achieve satisfactory channel estimation accuracy using a small number of feedback bits, even when the number of transmit antennas at the BS is large-making them ideal for 5G massive MIMO. Judicious simulations reveal that they outperform a number of popular feedback schemes and underscore the importance of using angle dictionaries matching the given antenna directivity patterns, as opposed to uniform dictionaries. The proposed algorithms are lightweight in terms of computation, especially on the user equipment side, making them ideal for actual deployment in 5G systems.

The acquisition of channel state information (CSI) is essential in MIMO-OFDM communication systems. Data-aided enhanced receivers, by incorporating domain knowledge, effectively mitigate performance degradation caused by imperfect CSI, particularly in dynamic wireless environments. However, existing methodologies face notable challenges: they either refine channel estimates within MIMO subsystems separately, which proves ineffective due to deviations from assumptions regarding the time-varying nature of channels, or fully exploit the time-frequency characteristics but incur significantly high computational overhead due to dimensional concatenation. To address these issues, this study introduces a novel data-aided method aimed at reducing complexity, particularly suited for fast-fading scenarios in fifth-generation (5G) and beyond networks. We derive a general form of a data-aided linear minimum mean-square error (LMMSE)-based algorithm, optimized for iterative joint channel estimation and signal detection. Additionally, we propose a computationally efficient alternative to this algorithm, which achieves comparable performance with significantly reduced complexity. Empirical evaluations reveal that our proposed algorithms outperform several state-of-the-art approaches across various MIMO-OFDM configurations, pilot sequence lengths, and in the presence of time variability. Comparative analysis with basis expansion model-based iterative receivers highlights the superiority of our algorithms in achieving an effective trade-off between accuracy and computational complexity.

Multiple Input Multiple Output (MIMO) system with orthogonal frequency division multiplexing (OFDM) is a promising physical layer candidate for the current and future wireless standards. MIMO increases system throughput while OFDM maximizes spectrum efficiency. To reduce the impacts of frequency selective fading and increase the capacity, MIMO requires precise channel state information (CSI). The presence of carrier frequency offset (CFO) introduces intercarrier interference (ICI) among the subcarriers which degrades the system performance. MIMO channel estimation is done under the assumption of perfect frequency synchronisation between the transmitter and receiver, or zero CFO. However, such an assumption is incorrect for time-varying and frequency-selective wireless channels, and the presence of CFO has an impact on channel estimation accuracy. In the existing methods, CFO and channel estimations are carried out independently using separate training sequences which reduces the spectral efficiency. As a result, the pilot aided interpolation approach is proposed in this paper for the joint estimate and compensation of CFO and channel effects using the same set of pilot tones. Theresults show that the proposed method offers precise CFO and channel estimation which improves the bit error rate performance and carrier to interference power ratio (CIR) of MIMO-OFDM system.

Most existing downlink channel estimation methods rely on channel sparsity in the angular domain to reduce pilot overhead for massive multiple-input multiple-output (MIMO) systems. Compared with channel estimation methods based on predefined basis or offline dictionary learning, in this paper, we design an online two-timescale joint uplink/downlink dictionary learning and channel estimation (TTS-JDLCE) algorithm for dynamic scenarios, where the channel sparsity and angle reciprocity between uplink and downlink transmissions are both exploited to reduce the pilot overhead. The downlink channel estimation is constructed as a TTS stochastic optimization problem with a constraint coupled by the long-term dictionary and short-term sparse channel representations. Treating the dictionary as a learnable parameter, the proposed algorithm can capture dynamic spatial information for enhancing performance. By introducing a relaxed TTS primal-dual decomposition (PDD) framework, the original problem is decomposed into a long-term online dictionary learning subproblem and a family of short-term sparse channel estimation subproblems. Besides, the deep unfolding technique is employed to extract gradient information from short-term problems, which circumvents the non-closed form and non-convexity of long-term subproblem by constructing a convex surrogate problem. Finally, simulations show that the proposed method remarkably reduces the pilot overhead and achieves significant performance gains over various baselines.

This paper describes investigations into the antenna mutual coupling (MC) effect on channel estimation and capacity of a multiple-input multiple-output (MIMO) wireless communication system. The presented investigations close the gap existing in the previous works which assessed the effect of mutual coupling on MIMO capacity under the assumption of availability of perfect channel state information (CSI) at the receiver. The new approach assumes that the perfect CSI is not available due to channel estimation errors. The investigations are carried out for different spacing between array antenna elements producing a varying effect of mutual coupling on the channel estimation and the resulting MIMO channel capacity.

Channel state information (CSI) acquisition is a crucial issue in downlink FDD-based massive multi-input multioutput (MIMO) networks, where the channel reciprocity is not applicable. Thus, users are expected to feedback the bestmatch quantized channels to serving transmitters. Hence, an extensively large size of the feedback overhead is needed, which is linearly scaled at each user with the number of transmit antennas at the base-station (BS). In turn, the uplink (UL) channel capacity may be consumed and the overall performance becomes fundamentally limited by the downlink (DL) channel quantization precision. An alternative CSI acquisition scheme is critically needed. In this paper, we propose a novel FDD massive MIMO system based on a spatial DL channel estimation scheme; it relies on the statistical spatial correlation of the UL and DL channel clusters, given an arbitrary frequency band gap between the UL and DL channels. A transformation matrix is constructed to precode the observed UL channel on the estimated dominant DL angles of departure. The proposed scheme significantly outperforms the recent state-of-the-art techniques, without the cost of user feedback overhead bits and prior knowledge of the channel statistics.

Reconfigurable intelligent surface- (RIS) assisted mobile communication is a promising technological paradigm thanks to the attractive advantages of low cost and flexible control of electromagnetic waves. However, the low-cost features of RISs entail some fundamental challenges to the acquisition of channel state information (CSI), which is essential for the optimal RIS design. To tackle this problem, there have been extensive studies on CSI acquisition in RIS-assisted mobile communication systems, from the perspective of architectural improvement as well as specific mathematical solutions. This article aims to overview the existing works on CSI acquisition in RIS-assisted mobile communication systems.

The acquisition of channel state information (CSI) plays a vital role in enhancing the performance of sixth-generation (6G) wireless communication systems. Conventional channel estimation approaches encounter significant scalability limitations in emerging scenarios, such as high-mobility environments, extremely large-scale multiple-input multiple-output (XL-MIMO) configurations, and multi-band operations, where pilot overhead grows dramatically. CSI extrapolation offers an effective solution to these issues by leveraging limited or partial CSI measurements to reconstruct or predict the full CSI, thereby substantially lowering the required overhead without compromising accuracy. Artificial intelligence (AI) has emerged as a powerful tool to advance CSI extrapolation, enabling more accurate and efficient inference across diverse channel conditions. Although research in this area is expanding rapidly, the literature still lacks a thorough and unified survey that synthesizes the latest developments in AI-based CSI extrapolation methods. This paper aims to bride this gap by providing the first comprehensive review of AI-driven CSI extrapolation techniques, covering their current state, key limitations, and promising research avenues. We begin by outlining the foundational aspects of AI-driven CSI extrapolation. This includes essential wireless channel properties that influence extrapolation performance and an overview of the most commonly employed AI architectures suited to this task. Building on these basics, we systematically examine the major categories of extrapolation approaches, both traditional model-based and modern AI-enhanced ones, across the primary domains: time, frequency, antenna, and multi-domain scenarios. For each category, we highlight representative techniques, their underlying principles, strengths, and limitations, along with distilled insights from comparative studies. Recognizing the strong potential of AI-based methods to satisfy the demanding performance targets of future systems, we also review publicly available open channel datasets and channel simulators that support the development and benchmarking of robust AI-driven extrapolation models. Finally, we identify persistent challenges in the field, and outline forward-looking research directions to guide future progress toward practical deployment in 6G networks.

In millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, channel estimation in the presence of sparse multipath fading boils down to two-dimensional (2D) direction-of-arrival (DOA) estimation followed by path gain estimation. To achieve super-resolution angle estimation at affordable complexity, this paper develops an efficient channel estimation approach by applying a truncated atomic norm minimization (T-ANM) technique, which is implemented via partial antenna activation during training-based channel estimation. This technique makes use of a key observation that the sparse scattering characteristics of mmWave MIMO channel gives rise to a low-rank two-level Toeplitz structure in the angular domain. Because of the low-rank property, only a subset of the transceiver antennas needs to be activated to save training resources. Meanwhile, the Toeplitz structure enables ANM-based gridless 2D DOA estimation via reduced-size semidefinite programming. Simulation results show that the proposed reduced-size method can achieve comparable spectral efficiency as the full-size benchmark method at much lower computational complexity and shorter sensing time.

After a few decades’ evolution of wireless communication systems, to ensure reliable highspeed communication over unreliable wireless channels is still one of the major challenges facing researchers and engineers. The use of multiple antennas at transmitter and receiver, known as multiple-input multiple-output (MIMO) communications, is one promising technology delivering desired wireless services. The main goal of this thesis is to study two important issues in wireless MIMO communication systems: receiver design for coded MIMO systems, and diversity-multiplexing tradeoff analysis in general fading channels. In the first part of this thesis, we decompose the receiver design problem into two subproblems: MIMO channel estimation and MIMO detection. For the MIMO channel estimation, we develop an expectation-maximization (EM) based semi-blind channel and noise covariance matrix estimation algorithm for space-time coding systems under spatially correlated noise. By incorporating the proposed channel estimator into the iterative receiver structure, both the channel estimation and the error-control decoding are improved significantly. We also derive the modified Cramer-Rao bounds (MCRB) for the unknown parameters as the channel estimation performance metric, and demonstrate that the proposed channel estimation algorithm can achieve the MCRB after several iterations. For the MIMO detection, we propose a novel lowcomplexity MIMO detection algorithm, which has only cubic order computational complexity, but with near-optimal performance. For a 4×4 turbo-coded system, we show that the proposed detector had the same performance as the maximum a posteriori (MAP) detector for BPSK modulation, and 0.1 dB advantage over the approximated MAP detector (list sphere decoding algorithm) for 16-QAM modulation at BER = 10−4. In the second part of this thesis, we derive the optimal diversity-multiplexing tradeoff for

Massive MIMO has been proposed as a means of achieving huge spectral efficiency in cellular networks. However, its implementation is usually based on linear precoding schemes using Channel State Information (CSI). For this reason, the use of the Time Division Duplex (TDD) mode is typically assumed since it removes the dependence on the number of antennas at the transmitter to acquire CSI. Unfortunately, most of the cellular network operate in the Frequency Division Duplex (FDD) mode in which the acquisition of CSI depends proportionally to the number of transmit antennas. For this reason, the implementation of Massive MIMO is handicapped to few transmit antennas for the current cellular networks. In this paper we study the use of reconfigurable antennas that can switch among a set of preset modes, i.e., radiation patterns, through Blind Interference Alignment schemes. We show that it is a solution to exploit a very large number of antennas at the transmit side, overcoming the bottleneck that today constitutes the acquisition of CSI in Massive MIMO systems operating in FDD mode.

In reconfigurable intelligent surface (RIS)-assisted systems the acquisition of channel state information and the optimization of reflecting coefficients constitute major design challenges. In this paper, a novel channel training-based protocol is proposed, which is capable of striking a flexible trade-off between performance, pilot overhead and complexity. More specifically, first of all , we conceive a holistic protocol that intrinsically amalgamates the existing channel estimation and passive beamforming optimization for creating a new unified scheme. Secondly , we propose a new channel training framework. In contrast to the conventional channel estimation arrangements, our new framework divides the training phase into several periods and has the compelling benefit of directly estimating the superimposed end-to-end channel instead of separately estimating the direct BS-user and reflected RIS links, which would not lend itself to near-instantaneous reconfiguration in the face of high-Doppler mobility. As a result, the RIS reflecting coefficients are optimized by comparing the objective function values over multiple training periods, which leads to optimal performance, despite its reduced complexity as well as reduced signaling and pilot overhead. Thirdly , we analyze the theoretical performance of both the channel estimation-based protocol and the channel training-based protocol in the presence of channel estimation errors. Finally , our theoretical analysis is confirmed by numerical simulations. In particular, the simulation results demonstrate that our channel training-based protocol is more competitive than the channel estimation-based protocol in the presence of channel estimation errors.

In this paper we consider a time slotted cognitive radio (CR) network with N wireless channels and M secondary users (SUs). We focus on a random access policy where each SU stochastically decides whether to access idle channels or not based on the given access probability (AP) that is adapted to the channel state information (CSI), if possible. The AP plays an important role in the random access policy because it can control the number of SUs who can access idle channels in a simple manner and hence alleviate packet collisions among SUs. We assume that each SU can access at most L idle channels simultaneously at a time slot whenever possible.