Ideal Channel State Information Research Articles

LMA-SLN beamforming optimization for sum-rate maximization in intelligent reflecting surface assisted NOMA Systems

Intelligent reflecting surfaces (IRS) can effectively improve the system performance of non-orthogonal multiple access (NOMA) systems. In this paper, we propose a Levenberg–Marquardt algorithm-based supervised learning network (LMA-SLN) to maximize the sum-rate of an IRS-assisted NOMA system. By decoupling the sum-rate maximization problem into the active and passive beamforming optimization sub-problems, we design an alternating optimization scheme to optimize the active and passive beamformings. Then, the LMA-SLN is trained with ideal channel state information (CSI) to obtain the optimized network parameters. Finally, the trained LMA-SLN is applied to optimize the active and passive beamformings without requiring CSI. The experimental results show that the proposed LMA-SLN scheme achieves the superior performance on improving the sum-rate.

Massive MIMO Receiver Design with Channel State Information

In order to achieve an astounding data transfer rate, 5G Technology’s is essential obligation, this is the primal reason while the world considers it as an pivotal active research topic, Amongst numerous techniques available to attain the transfer rate, opted Massive Multiple-Input Multiple-Output (MIMO) system receivers with respect to CSI assuming the mathematic computations and complex equations would be deduced in a primordial passion, is examined in this study. For the uplink, we show the possible transfer rate for both receivers with perfect and imperfect channel state information (CSI), considering that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers. Channel estimation is carried out by transmitting pilot symbols when there is insufficient information about the channel state to establish its properties. Additionally, the influence of channel state information on the power scaling rule is illustrated, and a numerical analysis for combiner is derived. In this study, we compare the competence of receivers under ideal and imperfect CSI. In case of perfect CSI, Value of data rate is 84 bits/sec/Hz for ZF receiver and 45 bits/sec/Hz. In case of imperfect CSI, Value of data rate is 60 bits/sec/Hz for ZF receiver and 37 bits/sec/Hz with number of antennas being 500 and number of pilot bits as 10.

Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks

We consider a cell-free wireless system operated in Time Division Duplex (TDD) mode with user-centric clusters of remote radio units (RUs). Since the uplink pilot dimensions per channel coherence slot is limited, co-pilot users might incur mutual pilot contamination. In the current literature, it is assumed that the long-term statistical knowledge of all user channels is available. This enables Minimum Mean-Square Error channel estimation or simplified dominant subspace projection, which achieves significant pilot decontamination under certain assumptions on the channel covariance matrices. However, estimating the channel covariance matrix or even just its dominant subspace at all RUs forming a user cluster is not an easy task. In fact, if not properly designed, a piloting scheme for such long-term statistics estimation will also be subject to the contamination problem. In this paper, we propose a new channel subspace estimation scheme explicitly designed for cell-free wireless networks. Our scheme is based on 1) a sounding reference signal (SRS) using latin squares wideband frequency hopping, and 2) a subspace estimation method based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme ensures that for any user and any RU participating in its cluster, only a few pilot measurements will contain strong co-pilot interference. These few heavily contaminated measurements are (implicitly) eliminated by R-PCA, which is designed to regularize the estimation and discount the “outlier” measurements. Our simulation results show that the proposed scheme achieves almost perfect subspace knowledge, which in turns yields system performance very close to that with ideal channel state information, thus essentially solving the problem of pilot contamination in cell-free user-centric TDD wireless networks.

On the Convenience of Perfect Channel Knowledge for Spatial Equalization of Correlated MIMO-VLC Links: Is it Really Worth it?

Visible Light Communication has risen as a promising technology to support indoor wireless connectivity and provide illumination services as well. Moreover, high communication performance can be achieved by implementing Multiple-Input Multiple-Output architectures. However, the crosstalk characterizing spatially correlated channels scenarios may significantly impact on reliability. Such issue is commonly addressed by means of spatial equalization, with zero forcing and minimum mean square error representing the most widespread approaches. These methods allow the mitigation of crosstalk, even though they may cause undesired noise amplification as side effect, inducing errors during signal detection and decoding. In this regard, by focusing on a more realistic approach with respect to the literature, this work overturns the belief that having perfect channel knowledge is always better. Hence, we show that, when applying spatial equalization, imperfect channel knowledge is sometimes preferable to ideal channel state information. As a result, noise amplification is reduced at the expense of a reasonably less accurate crosstalk mitigation, with better communication performance being achieved.

Performance of Beamforming for a Handheld Device in Measured 21.5 GHz Indoor Channels

This article presents an evaluation of the performance of two different approaches for beamforming (BF) in a fifth generation (5G) handset, based on measured channels from a dual-polarized transmitter (Tx) to a mockup handset with a seven-element receiver (Rx) array. The measurements at 21.5 GHz include both small-scale and large-scale channel changes and were conducted for different users holding the mockup at different locations in an indoor corridor environment. The unique measurements enable novel results based on the statistics of the power achieved with BF, using channel state information (CSI) measured at a 90 Hz rate, and both when using beam scanning (BS) and the equal gain combining (EGC) reference case. For the case of ideal CSI knowledge, BF gains of up to about 18 dB were found. However, when the density of the angle scanning grid is reduced and the CSI is delayed to realistic values, the average performance decreases by 8 dB. More results are given in the article.

Power Optimization of IRS-Assisted D2D System Based on Imperfect Channel

Intelligent reflecting surface (IRS) is a promising technology that can help wireless communications achieve efficient spectrum and energy efficiency. However, because of its weak signal processing ability, it is difficult to get ideal channel state information (CSI). Under the imperfect channel state information hypothesis, we investigate a device-to-device (D2D) offload network. And an IRS is used to help calculate offloading from one set of task-intensive users to another set of idle users. We aim to jointly optimize transmit beamforming and IRS phase shifts to minimize system transmit power while requiring each user’s rate to meet the minimum rate constraint in the presence of channel errors. Unfortunately, the problem presented is nonconvex, and the imperfection of CSI makes it even more difficult to solve. Therefore, we apply the S-Procedure to convert the original problem to two effectively solvable semidefinite programming (SDP) subproblems and then solve them through the convex-concave procedure (CCP) algorithm and the alternate optimization method. Numerical results show the effectiveness of the algorithm and verify that the assistance of the IRS can greatly save the system transmit power.

Downlink Resource Allocation in Multiuser Cell-Free MIMO Networks With User-Centric Clustering

In this paper, we optimize user scheduling, power allocation and beamforming in distributed multiple-input multiple-output (MIMO) networks implementing user-centric clustering. We study both the coherent and non-coherent transmission modes, formulating a weighted sum rate maximization problem for each; finding the optimal solution to these problems is known to be NP-hard. We use tools from fractional programming, block coordinate descent, and compressive sensing to construct an algorithm that optimizes the beamforming weights and user scheduling and converges in a smooth non-decreasing pattern. Channel state information (CSI) being crucial for optimization, we highlight the importance of employing a low-overhead pilot assignment policy for scheduling problems. In this regard, we use a variant of hierarchical agglomerative clustering, which provides a suboptimal, but feasible, pilot assignment scheme; for our cell-free case, we formulate an <i>area-based</i> pilot reuse factor. Our results show that our scheme provides large gains in the long-term network sum spectral efficiency compared to benchmark schemes such as zero-forcing and conjugate beamforming (with round-robin scheduling) respectively. Furthermore, the results show the superiority of coherent transmission compared to the non-coherent mode under ideal and imperfect CSI for the area-based pilot-reuse factors we consider.

One-bit mMIMO with defective RF chain over Ricean fading in beyond 5G networks

In this paper, we investigate one-bit massive multiple-input multiple-output (mMIMO) systems with defective radio frequency (RF) chains over Ricean fading channels in beyond 5G networks. The approximate achievable rate expressions are derived in closed-form for the scenarios with ideal and non-ideal channel state information by utilizing the Bussgang decomposition. Furthermore, the scaling laws for transmit powers and RF hardware impairments are established when the number of antennas, M, at the base station (BS) grows indefinitely. Specially, the power scaling laws under the case of a large number of Internet of Things (IoT) devices (users) are also investigated. It is revealed that, as M increases, the systems can yield a non-vanishing rate while cutting down the transmit powers of the IoT devices, and/or scaling up the RF impairments of the BS. Moreover, it is also found that the relationship between the number of IoT devices N and M can influence the results of the power scaling laws. Numerical results are conducted to demonstrate our analysis. It is also revealed that the appropriate M can contribute to better energy efficiency.

An Intelligent Radio Buffer-Aided Relaying Scheme With Adaptive Link Selection

The intelligent radio is a novel emerging paradigm catching on in many practical applications, in order to provide context awareness techniques, able to learn by the environment conditions applying machine learning frameworks. The channel state information (CSI) prediction is one of the main challenges that an intelligent radio has to face with. This paper deals with the CSI prediction in the case of a buffer aided relaying communication scheme with adaptive link selection previously proposed in the literature where a direct source-destination link is not available and with both links affected by an independent fading. In particular, the CSI prediction has been reduced here to a time series prediction problem, performed by involving the usage of the recurrent neural networks. Finally, the validation of the effectiveness of the proposed intelligent radio buffer-aided relaying scheme with adaptive link selection approach has been pursued by presenting performance comparisons with the previously proposed scheme under the assumption of an ideal and actual CSI estimations, respectively.

Performance Analysis of TDD Multicell Massive MIMO Systems With Non-Orthogonal Pilots and Hardware Imperfections in Rician Fading Channels

The performance of a time-division duplex (TDD) multicell massive MIMO ( $\mathrm{M}^2$ -MIMO) system with non-orthogonal pilots (Non-OPs) for channel estimation (CE) and hardware imperfections (HWIs), i.e., low-precision ADCs/DACs at the base station (BS) and non-ideally operating user equipment (UE) distorted by RF chain impairments (RF-CIs), is analyzed and evaluated. Assuming that this communication system operates in a Rician fading channel, firstly, the minimum mean square error (MMSE) CE complexity for both Non-OPs and orthogonal pilots (OPs) are derived and compared. It is also shown that the variance of the MMSE CE error with Non-OPs can be approximated as a linear fraction. Based on this CE analysis, the uplink (UL) and downlink (DL) spectral efficiency (SE) performances with ideal and non-ideal channel state information (CSI) are derived. Furthermore, the UL-SE performances for both Non-OPs and OPs are compared to show the advantage of using Non-OPs for applications which have very high connectivity requirements. The power scaling law is also used to obtain additional insights on how these HWIs affect the overall $\mathrm{M}^2$ -MIMO SE performance. In order to balance the total BS circuitry power consumption against UL-SE, the tradeoff between UL energy efficiency (EE) and UL-SE has been investigated by analyzing the performance of several receive detection strategies as a function of the employed ADC quantization bits (Q-bits). Moreover, the optimal ADC/DAC Q-bit region which maximizes the DL-EE is identified. Various performance evaluation results obtained by means of computer simulations have verified the analysis and validated the accuracy of the equivalent theoretical performance evaluation results.

Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks.

Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning-based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to evaluate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas is investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning-based detectors compared with the classical maximum likelihood algorithm.

Precoding for TDD and FDD in Measured Massive MIMO Channels

This paper analyzes the performance of well-known precoding schemes for massive multiple-input multiple-output (MMIMO) systems. The investigations are based on extensive measurements made with a sounding system capable of capturing the dynamic channels towards users moving in many different outdoor scenarios. Assuming ideal channel state information (CSI), results show that the mean sum-rate of the maximum ratio transmission (MRT) precoder varies considerably with the scenario, e.g., from 6.5 to 14.5 bit/s/Hz (10%- and 90%-percentiles) for a 64 element uniform linear array (ULA) at the base station (BS), while the zero-forcing (ZF) and signal to leakage and noise ratio (SLNR) precoders are more robust and higher performing with variation from 13.4 to 16.3 bit/s/Hz in the same conditions. However, when the CSI is non-ideal the performance drops. With the CSI delayed corresponding to movement of about 1/5 of a wavelength, the ZF and SLNR mean sum-rate is 60-92% of that achieved with ideal CSI (10%- and 90%-percentiles). More statistics for different massive array sizes with both delay and frequency offset CSI are given in the paper.

Low-Complexity Beam Training for 5G Millimeter-Wave Massive MIMO Systems

We consider a 5G millimeter-wave (mmWave) massive MIMO system with subconnected hybrid structure, where each transceiver unit (TXRU) is connected to a subarray at the transmitter (TX) or the receiver (RX). Beam training is the first step for codebook-based beamforming in the absence of ideal channel state information (CSI). The complexity of the optimal exhaustive search beam training is exponentially increased with the number of TXRUs (or subarrays) at the TX and the RX, which is infeasible in practice. The conventional beam training schemes are mainly based on the idea of mathematical search algorithms and novel beam codebook designs. In this study, we explore a novel low-complexity beam training scheme by fully exploiting the sparse nature of the mmWave channel and the strong correlations of large-scale antenna arrays. Specifically, we propose a training scheme to reduce beam search space for 5G mmWave system, which consists of the following two key stages: 1) a beam subset optimization method from the perspective of array gain for capturing the energy of dominant channel propagation paths, and 2) a class of linear iterative beam search algorithms within the optimized beam subset utilizing the idea of limiting the degrees of freedom of the subarray indices during beam training. The range of the optimized beam subset size and linear search condition are also given. The performances of the proposed two algorithms, beam subset optimization-single subarray linear search and beam subset optimization-subarray pair linear search, are evaluated in a 5G mmWave system. Analysis has shown that the complexity of the proposed scheme is linear with the number of channel propagation paths, which can considerably reduce the complexity of conventional schemes. Simulation results have verified the near-optimal performance of the proposed schemes when compared with exhaustive search as well as other existing training methods.

Joint Design of Fronthauling and Hybrid Beamforming for Downlink C-RAN Systems

Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This paper studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network architecture. Digital processing is carried out at a baseband processing unit (BBU) in the “cloud,” and the precoded baseband signals are quantized prior to transmission to the RRHs via finite-capacity fronthaul links. In this system, we consider two different channel state information (CSI) scenarios: 1) ideal CSI at the BBU and 2) imperfect effective CSI. The optimization of digital beamforming and fronthaul quantization strategies at the BBU as well as analog radio-frequency (RF) beamforming at the RRHs is a coupled problem since the effect of the quantization noise at the receiver depends on the precoding matrices. The resulting joint optimization problem is examined with the goal of maximizing the weighted downlink sum-rate and the network energy efficiency. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraint on the RF beamforming matrices. For the case of perfect CSI, a block coordinate descent scheme is proposed based on the weighted minimum-mean-square-error approach by relaxing the constant modulus constraint of the analog beamformer. Also, we present the impact of imperfect CSI on the weighted sum-rate and network energy efficiency performance, and the algorithm is extended by applying the sample average approximation. The numerical results confirm the effectiveness of the proposed scheme and show that the proposed algorithm is robust to estimation errors.

Low Complexity Adaptive Demapper for 2-D Non-Uniform Constellations

In this paper, a novel demapper for 2-D non-uniform constellations (2D-NUCs) is proposed, exploiting the characteristics of these constellations. It represents the combination of two underlying demapping techniques targeting the ATSC 3.0 compliant OFDM transceiver. On the one hand, for low code rates, we define a metric to perform condensed demapping. On the other hand, for high code rates, adaptive sub-region demapping is proposed. In this paper, a combination of both demapping methods is designed showing comparable performance to the classical ML demapper. The gap does not exceed 0.1 dB for all code rates of the ATSC 3.0 standard. Higher complexity reduction, from 79.2% to 95.4%, than state of art 2D-NUC demappers is obtained for 2D-256NUCs. These results are validated for ideal and non-ideal channel state information over additive white noise Gaussian and Rayleigh independently and identically distributed channels. Results are extended to 2D-1kNUCs and 2D-4kNUCs showing demapping complexity reduction from 96% to 99.7% with a negligible impact on performance.

Robust Secure Beamforming in MIMO Wiretap Channels With Deterministically Bounded Channel Errors

In this paper, a multiple-input multiple-output wiretap channel model is studied, where the information packet sent from the transmitter to the desired receiver is wiretapped by a potential eavesdropper. When ideal channel state information (CSI) is available, we design the secure beamforming using a proposed iterative closed-form updating scheme based on majorization–minimization approach. Numerical results demonstrate that the proposed scheme acquires the same secrecy rate as the existing algorithms whereas it has much lower complexity. When ideal CSI is not available and channel errors are modeled by deterministically bounded model, we investigate robust secure beamforming (RSB) optimization problem whose goal is worst-case secrecy rate maximization. In the formulated optimization problem, the objective function includes the expression of a logarithm of a determinant ( $\log \det$ ) minus $\log \det$ . Instead of approximating the $\log \det$ expression as a trace, we propose to linearize two $\log \det$ terms. After linearization, epigraph reformulation is used to deal with deterministically bounded channel errors and semi-infinite constraints are recast as linear matrix inequalities. Finally, an alternating optimization algorithm is utilized to find the RSB design. After obtaining the RSB design, we derive the worst-case secrecy rate. Numerical results illustrate that the proposed RSB design is superior to the nonrobust one.

Multiuser precoding scheme and achievable rate analysis for massive MIMO system

We analyze the downlink multiuser precoding of massive multiple input multiple output (MIMO) system, where the base station (BS) has ideal channel state information (CSI) and adopts three types of different linear precoding schemes, i.e., maximum ratio transmission (MRT), zero-forcing (ZF), and minimum mean squared error (MMSE). Under a Rayleigh fading channel, we attain the exact expressions on the achievable rate for these three precoding schemes. Moreover, we provide several insights on the achievable rates and reveal the relation of the number of BS antennas, the number of users, and the input signal-to-noise ratio (SNR) with the achievable rates respectively. It is found in general that the achievable rate increases with the number of BS antennas and the input SNR. To be more specific, the MRT precoding scheme is much inferior to the ZF and MMSE precoding schemes and tends to be at a fixed rate at the high SNR case. On the contrary, the MRT precoding scheme outperforms ZF precoding schemes at the low SNR case. Moreover, the total achievable rate always does not increase with the number of users and the optimal number of users always exists for the ZF and MMSE precoding schemes.

Wireless Information and Power Exchange for Energy-Constrained Device-to-Device Communications

This paper studies device-to-device wireless communications, where two energy-constrained Internet-of-Things (IoT) nodes, which do not have constant power supplies, wish to exchange their information with each other. Because of small form factor, the IoT nodes are normally equipped with simple energy storages, which might suffer from a high self-discharging effect. Therefore, the energy stored in each node would not be available after a few time duration. In this system, we investigate power splitting (PS)-based energy exchange methods by exploiting radio frequency (RF) wireless energy transfer techniques, and propose a new concept called wireless information and power exchange (WIPE). In this WIPE protocol, each node operates either in a transmit mode and a receive mode at each time slot. First, a transmit node sends the information signal to a receive node which utilizes a PS circuit for information decoding and energy harvesting. Then, the harvested energy of the receive node is stored in the energy storage. At the consecutive time slot, two nodes switch their operations, i.e., the receive node in the previous time slot now operates in a transmit mode which transfers RF signals by using the harvested energy. This procedure continues by changing the operations of two nodes at each time slot. For the proposed WIPE protocol, we provide two different PS ratio optimization schemes which maximize the weighted sum throughput performance according to the level of channel state information (CSI) knowledge. For the ideal full CSI case where the CSI for all time slots is known in advance, the globally optimal PS algorithm is presented by applying convex optimization techniques. Also, for a practical scenario where only the causal CSI is available, we propose an efficient PS optimization method which achieves performance almost identical to the ideal full CSI case. Simulation results verify that the WIPE protocol with the proposed PS optimization techniques performs better than conventional schemes.

Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting

For the problem of channel state information (CSI) delay and error, this paper proposes a joint interference and phase alignment algorithm based on Bayesian estimation and power allocation among data streams for multicell, multiple-input multiple-output broadcast channels (MIMO-BC). Firstly, the sender obtains the best estimate of the current CSI through Bayesian estimation. Secondly, the interference suppression matrix is designed by maximizing the ratio of the desired signal power to the intercell interference plus noise ratio (SINR) in the forward link, and in the reverse communication, by maximizing the SINR design precoding. Further, the water-filling algorithm is combined to optimize power allocation among data streams. Finally, the phase alignment is used to rotate the interference between data streams into the signal space of the target receive data stream, thereby enhancing the received power of the target data stream. Simulation results show that the proposed algorithm has certain performance advantages over other algorithms, whether it is ideal CSI or delay and error CSI.

Adaptive robust beamformer formulti-pair two-way relay networks with imperfect channel state information

In wideband multi-pair two-way relay networks, the performance of beamforming at a relay station (RS) is intimately related to the accuracy of the channel state information (CSI) available. The accuracy of CSI is determined by Doppler spread, delay between beamforming and channel estimation, and density of pilot symbols, including transmit power of pilot symbols. The coefficient of the Gaussian-Markov CSI error model is modeled as a function of CSI delay, Doppler spread, and signal-to-noise ratio, and can be estimated in real time. In accordance with the real-time estimated coefficients of the error model, an adaptive robust maximum signal-to-interferenceand- noise ratio (Max-SINR) plus maximum signal-to-leakage-and-noise ratio (Max-SLNR) beamformer at an RS is proposed to track the variation of the CSI error. From simulation results and analysis, it is shown that: compared to existing non-adaptive beamformers, the proposed adaptive beamformer is more robust and performs much better in the sense of bit error rate (BER); with increase in the density of transmit pilot symbols, its BER and sum-rate performances tend to those of the beamformer of Max-SINR plus Max-SLNR with ideal CSI.