Short-Packet Backscatter Assisted Wireless-Powered Relaying With NOMA: Mode Selection With Performance Estimation

This paper proposes a robust transceiver design against the effect of channel state information (CSI) estimation error to optimize precoded uplink (UL) multi-user multiple-input multiple-output (MU-MIMO) transmission in limited feedback system under the consideration of the least-square technique on CSI estimation. To improve this limited feedback precoding, the constrained minimum variance (MV) approach with quadratic form to realize the computationally-efficient optimization problem, advantageously invoking the characteristics of the CSI estimation error, is proposed to suppress the effect of CSI estimation error, multiple user interference and noise. According to the Lagrange multiplier method on this MV approach, the deterministic function to resist uncertain CSI can be obtained to optimize design of the precoder and adaptive matrices jointly. With these optimum adaptive and precoder matrices, an optimum robust weighting matrix can be obtained to facilitate the user-wise detection in precoded UL MU-MIMO system. Performance analysis shows that the proposed robust weighting matrix is an unbiased design and it also can regularize the diagonal loading factor technique, and the detection performance of the proposed robust transceiver design can be predicted simplistically by applying our derived signal-to-interference-plus-noise ratio formulation. Computer simulations are conducted to confirm the efficacy of the proposed design in both perfect and imperfect CSI estimation.

SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

It is well known that the performance of the wireless communication systems can be enhanced by using multiple transmit and receive antennas, which is generally referred to as the MIMO technique, and has been incorporated into the IEEE 802.11n standard, i.e. one of the Wi-Fi systems. The MIMO technique has also been adopted by the 3GPP Long Term Evolution (LTE) and the 3GPP2 Ultra Mobile Broadband (UMB) standards. The space-time coding is a promising way to realize the gain in the wireless communications system using MIMO. The space time block code, and the orthogonal space time block code in particular have been proposed in research and implemented in practical wireless communication systems due to their low computational complexity in maximum likelihood decoding implementation. However, the code rates of the orthogonal space time block codes are usually lower than $3/4$ when the number of transmit antennas is greater than two, which limit their throughput. To increase the code rate and the throughput of the orthogonal space time block code for more than two transmit antennas, a family of new space-time block codes (STBC) for 3 and 4 transmit antennas in a MIMO wireless communication system have been proposed in this Ph.D. research. These new codes are orthogonal and achieve full diversity, full rate using Triple QPSK'' modulation, which is proposed in this research. We investigated the performance of these STBCs when the channel state information (CSI) is perfectly known at the receiver. The CSI is not known to the transmitter in all of the studies in this dissertation. An analytical bound of the probability of bit error is developed and the Monte Carlo simulation is used when investigating these new STBC codes. In practical wireless communication system, the CSI is generally not available at the receiver and it is usually estimated by the receiver through the pilot signals or training signals known to the receiver. No matter what channel estimation method is used, the estimate of CSI will not match the CSI exactly and is noisy. The performance of these new STBCs using theTriple QPSK'' modulation when the estimate of the CSI is employed to detect the symbol is investigated in this dissertation. An upper bound of the bit error probability is developed when the noisy CSI is used to detect the symbols. The performance of the new STBCs using the Triple QPSK'' modulation is also evaluated using Monte Carlo simulation. When the CSI is not available to either the receiver or the transmitter, the differential encoding and decoding algorithm is applied to these new STBCs, which use theTriple QPSK'' modulation. The results showed that these orthogonal STBCs achieve full diversity full rate using ""Triple-QPSK"" modulation with differential detection. This provides a means of achieving space diversity and multiplexing gain without the CSI. A new differential encoding and decoding algorithm is proposed and analyzed for STBCs with 3 transmit antennas, whose transmission matrix is not square matrix, as part of this Ph.D. research. The STBCs using this algorithm achieve full diversity the MIMO system can possibly provide.

This paper addresses channel state information (CSI) estimation in a wireless relay network, which consists of a base station (BS), a relay station (RS), and a mobile station (MS). We propose a method for the BS to obtain the instantaneous CSIs of the three links (BS-RS, BS-MS, and RS-MS links) with minimized overhead. The CSI availability plays an important role for the BS to adapt its transmission strategies to the given channel conditions. Our proposed method exploits the fact that the link condition between fixed BS and fixed RS tends to be stable, and the frequent CSI update is not necessary for this link. In order for the BS to obtain the CSI of the distant link (i.e. RS-MS link), RS amplifies a pilot signal received from MS with a pre-defined amplification factor, and forwards it to BS. This enables the BS to obtain the CSI of the RS-MS link based on the received pilot signal and pre-knowledge on the CSI of BS-RS link. We show by computer simulation that our proposed method can reduce the required overhead for CSI estimation with little degradation on the accuracy of the obtained CSI.

In this paper, we consider a cognitive multi-hop relay secondary user (SU)\nsystem sharing the spectrum with some primary users (PU). The transmit power as\nwell as the hop selection of the cognitive relays can be dynamically adapted\naccording to the local (and causal) knowledge of the instantaneous channel\nstate information (CSI) in the multi-hop SU system. We shall determine a low\ncomplexity, decentralized algorithm to maximize the average end-to-end\nthroughput of the SU system with dynamic spatial reuse. The problem is\nchallenging due to the decentralized requirement as well as the causality\nconstraint on the knowledge of CSI. Furthermore, the problem belongs to the\nclass of stochastic Network Utility Maximization (NUM) problems which is quite\nchallenging. We exploit the time-scale difference between the PU activity and\nthe CSI fluctuations and decompose the problem into a master problem and\nsubproblems. We derive an asymptotically optimal low complexity solution using\ndivide-and-conquer and illustrate that significant performance gain can be\nobtained through dynamic hop selection and power control. The worst case\ncomplexity and memory requirement of the proposed algorithm is O(M^2) and\nO(M^3) respectively, where $M$ is the number of SUs.\n

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

The IEEE802.11a standard for indoor wireless LAN systems was released commercially and is wide spread in the marketplace. To achieve higher throughput, IEEE802.11n was proposed and is now undergoing standardization. The MIMO-OFDM technique, which employs multiple antennas for transmission and reception, is the core technology for IEEE802.11n (Foshini and Gans, 1998). This technique requires estimation of channel state information (CSI) on the receiving side. Using the technique called adaptive MIMO-OFDM (Telatar, 1999) in which CSI is applied to both the transmitting and receiving sides, an even higher throughput is achieved. To achieve the maximum performance from adaptive MIMO-OFDM, we must accurately estimate the CSI for all the subcarriers; however, numerous calculations are needed to obtain this estimation. So, to actualize the adaptive MIMO-OFDM system, the calculation load must be decreased. This can be accomplished by applying one CSI to other subcarriers. However, channel error exists between the actual channel and the applied CSI. The channel error also plays a role in decreasing the communications quality (Medard, 2000). The communications quality when using the adaptive MIMO-OFDM technique that has CSI error was evaluated by simulation (Narula et al., 1998). However, there are few measurement evaluation reports on CSI error. This paper focuses on the ratio between the power of the eigenvalue and the interference power that suppresses the communication quality based on the measurement results. We propose an empirical formula for predicting the signal to interference ratio (SIR), which is the ratio between the power of the eigenvalue and the interference power. This SIR empirical formula helps to predict the communication quality and simplify the simulation.

The availability of Channel State Information (CSI) at the receiver side of a Free Space Optical (FSO) link is required for optimal detection and diversity combining. Consequently, providing instantaneous CSI knowledge introduces challenges to receiver design and an increase in estimation error and bandwidth waste. In this work we investigate two selection-combining diversity schemes and their achieved gains when perfect instantaneous CSI is not available. An average SNR estimation value at the receiver is used for branch selection, and signal combining is performed with branch balance and unbalance. Channel fading is modeled as a lognormal distribution with spatially correlated samples. BER performance and outage probabilities are analytically characterized. Further, analytical results are verified using computer simulation.

In this paper, we consider relay selection for turbo coded cooperative networks subject to Nakagami-m fading when the channel state information (CSI) is known at the receiver but is not necessarily ideal. This non-ideality may be due to a feedback delay caused by the difference between the instantaneous CSI during the transmission and the CSI at the time of relay selection, resulting in outdated CSI phenomena. The impact of the outdated CSI on the proposed scheme is well investigated. A closed-form expression for the exact outage probability is derived as well as its asymptotic expression in the high signal-to-noise (SNR) regime. Moreover, upper bounds on the bit-error rate (BER) are presented and a study of the diversity order reveals that for ideal CSI, full diversity in the number of relays and fading parameters m is achieved as opposed to outdated CSI where the achievable diversity is equivalent to the diversity of a coded cooperative network with a single relay.

Channel qualities between UAVs vary drastically due to the mobility of UAVs. Conventional channel coding relies on channel state information (CSI) estimation and active bit rate selection and thus cannot adapt well to such varying channel conditions. In contrast, rateless codes can achieve almost optimal bit rate under varying channel conditions without CSI estimation and explicit rate selection. In rateless codes, Spinal codes are one of the most prominent solutions and perform much better than other rateless codes over the mobile fading channel between UAVs. However, Spinal codes still face the challenge of error accumulation effect, which largely hurts the transmission efficiency. In this paper, we for the first time analyze the error accumulation effect and its impact on the performance of Spinal codes under mobile fading channel conditions between UAVs. Furthermore, we propose a model for helping the decoder estimate the quality of each received symbol. Based on such model, trust function based Spinal codes (TFSC) are then proposed. Its main idea is to treat received symbols differently according to their qualities so that those symbols with better qualities can contribute more to the decoding process. Simulation results demonstrate that TFSC can significantly mitigate the error accumulation effect and improve the efficiency of Spinal codes, which achieves 1.1x to 4.4x overall performance improvement when compared with Spinal codes over the mobile fading channel between UAVs.

Acquiring accurate channel state information (CSI) is essential for enabling reliable and efficient wireless transmission and reception. However, CSI is inherently stochastic, high-dimensional, and time-varying, which makes its acquisition particularly challenging. Motivated by the success of deep learning (DL) across many data modalities, this thesis explores DL-based solutions for CSI estimation, compression, and prediction.First, we study CSI estimation in full-duplex (FD) multiple-input multiple-output (MIMO) systems, where strong self-interference (SI) complicates channel acquisition. To reduce the pilot and computational burden of estimating both SI and user channels, we propose a pilot-sharing strategy together with a convolutional neural network that jointly estimates these channels.We further introduce a neural mapping that enables CSI acquisition at the transmit chain.Second, we investigate DL–based CSI compression and its limited robustness under distribution shifts. To address this issue, we adopt a full-model fine-tuning while explicitly accounting for model update signaling overhead. Specifically, we employ a spike-and-slab prior to promote sparsity in the model updates and fine-tune the pretrained network using a rate–distortion objective regularized by the update bit rate.Third, we tackle CSI prediction using a diffusion-based generative framework. The method consists of a temporal encoder that extracts latent features from past CSI and a diffusion generator that synthesizes future CSI. We also study a simplified encoder-free design to reduce latency, compare autoregressive and sequence-to-sequence inference, and explore multiple architectures for both temporal encoding and diffusion generation.

Channel state information (CSI) estimation is one of the most fundamental problems in wireless communication systems. Various methods, so far, have been developed to conduct CSI estimation. However, they usually require high computational complexity, which makes them unsuitable for 5G wireless communications due to employing many new techniques (e.g., massive MIMO, OFDM, and millimeter-Wave (mmWave)). In this paper, we propose an efficient online CSI prediction scheme, called OCEAN, for predicting CSI from historical data in 5G wireless communication systems. Specifically, we first identify several important features affecting the CSI of a radio link and a data sample consists of the information of the features and the CSI. We then design a learning framework that is an integration of a CNN (convolutional neural network) and a long short term with memory (LSTM) network. We also further develop an offline-online two-step training mechanism, enabling the prediction results to be more stable when applying it to practical 5G wireless communication systems. To validate OCEAN's efficacy, we consider four typical case studies, and conduct extensive experiments in the four scenarios, i.e., two outdoor and two indoor scenarios. The experiment results show that OCEAN not only obtains the predicted CSI values very quickly but also achieves highly accurate CSI prediction with up to 2.650-3.457 percent average difference ratio (ADR) between the predicted and measured CSI.

Interference mitigation by applying Joint Processing (JP) in cooperative cellular networks boasts cell-edge performance compared to non-cooperative systems. However, downlink JP requires knowledge of Channel State Information (CSI) at all collaborating Base Stations (BSs), where interference received at the User Equipments (UEs) is markedly affected by the level of CSI quality influenced amongst others by the method of distributing CSI to the BSs. In this contribution, we compare CSI Distribution over the Air (CD-A) with CSI Distribution over the Backhaul (CD-B) considering a Frequency Devision Duplex (FDD) system. For CD-A, CSI is directly fed back from the UEs to the collaborating BSs via the uplink channel (assuming link adaptation is performed according to one master BS to which a UE has been assigned). Adopting CD-B, CSI is foremost transmitted only to the master BS and then forwarded to the other BSs using backhaul connections. This method typically introduces additional latency due to routing issues. In this contribution, we show that CD-A outperforms CD-B in the largest part of the cooperation area, even when pedestrian user velocities and small backhaul latencies are considered.

In this paper, we investigate the capacity performance of non-orthogonal transmission schemes under imperfect channel state information (CSI) caused by feedback delay and thermal noise for various values of the signal to noise ratio (SNRinput). We use orthogonal transmission, i.e. singular value decomposition (SVD), as a benchmark. With perfect CSI, the multiplexing schemes achieve higher capacity than single data stream transmission under high SNRinput (30dB). In terms of the delayed CSI, the capacity of all multiplexing schemes converges and multiplexing schemes are only beneficial for large antenna systems (4times2) and high SNRinput (20dB). When the CSI is corrupted by thermal noise, a 20dB SNR can be tolerated in the CSI estimation.

This paper investigates a precoding method in downlink multiuser multiple-input multiple-output (MIMO) transmission with multiple base station (BS) cooperation, where each user device basically feeds back the instantaneous channel state information (CSI) to only the nearest BS, but the users near the cell edge additionally feedback the instantaneous CSI to the second nearest BS among the cooperating BSs. Our precoding method is categorized as a form of multi-cell processing (MCP) [5], in which the transmission information to a user is shared by the cooperating BSs in order to utilize fully the degrees of freedom of the spatial channel, and is based on block diagonalization of the channel matrix. However, since some elements of the channel matrix are unknown, we allow partially non-orthogonal transmission. More specifically, we allow inter-user interference to users with limited instantaneous CSI feedback from the channel where the instantaneous CSIs of those users are not obtained at the BSs. The other sources of inter-user interference are set to zero based on the block diagonalization of the channel matrix. The proposed method more efficiently utilizes the degrees of freedom of the spatial channel compared to the case with full orthogonal transmission at the cost of increased inter-user interference. Simulation results show the effectiveness of the proposed method compared to the conventional approaches, which can accommodate the partial CSI feedback scenario, from the viewpoints of the required transmission power and achievable throughput.