In this paper we study channel estimation for Narrow-band Internet of things (NB-IoT) systems, a low-power radio access technology introduced by the Third Generation Partnership Project (3GPP) as a part of Long Term Evolution (LTE) in Release 13 and recently extended to support Non-Terrestrial Networks (NTN) in Release 17. To improve the quality of channel measurements from pilot signals, we propose a cross-slot smoothing algorithm based on a fixed-lag Kalman smoother (KS). The non-causal and recursive nature of the proposed smoother is suitable for NB-IoT systems, thanks to their delay-tolerant design (due the presence of sub-frame repetitions) and to unitary precoding (which ensures consistency of channel statistics across slots). We propose an efficient implementation of fixed-lag KS, which we call “reduced-redundancy” fixed-lag. Our algorithm eliminates computational redundancies by extending the state vector instead of augmenting it. We tailor our algorithm to practical scenarios, including geostationary (GEO) and low Earth orbit (LEO) satellites. Results show that the proposed algorithm outperforms causal Kalman filter and non-causal moving average or infinite impulse response filters, with comparable computational complexity.

Estimating second-order channel statistics (specifically, frequency-domain covariance matrices) from narrowband reference signals is crucial for channel estimation in 5 G cellular systems. The problem is challenging because of the limited number of available pilot subcarriers. In this work, we reparametrize the problem using a delay-domain characterization. Three algorithms are proposed for estimating the power delay profile (PDP) using least squared error and maximum likelihood estimation frameworks. The algorithms demonstrate a tradeoff between the computational complexity and model insights used to estimate the underlying PDP. The performance of the proposed algorithms is numerically analyzed under different channel conditions and pilot density. The proposed algorithms demonstrate significant gains (up to 3 dB in signal-to-noise ratio), and are robust to noisy delay spread information.

In orthogonal frequency-division multiplexing (OFDM)-based systems, such as 5 G, precise channel estimation (CE) is crucial for reliable connectivity and high data rates. However, in 5G, the sub-band-specific precoding structure of pilot signals, known as demodulation reference signals (DMRS), limits the number of pilot samples per sub-band, which can lead to significant CE errors. This paper provides an in-depth analysis of the influence of CE errors on pilot and data subcarriers in OFDM systems. We rigorously demonstrate that CE errors on data subcarriers can be modeled as independent additive noise, validating assumptions made in previous works but not formally proven. Additionally, we analyze how CE errors on pilot subcarriers introduce bias in noise variance estimation and derive closed-form expressions for this bias under both perfect and imperfect MMSE CE conditions. We propose practical bias removal schemes tailored for residual power-based noise variance estimation. Numerical simulations confirm the accuracy of our theoretical findings and show that the proposed noise variance estimation method significantly reduces the performance gap between narrow-band and wide-band CE.

In simultaneous transmitting and receiving systems, passive RF components can cause non-linear distortion products that lead to critical self-interference (SI) in the received signal. Adaptive digital filters are an effective approach to cancel this SI. However, when the source of SI involves multiple transmitters (TXs) with different frequency responses, the number of taps required for estimation increases significantly, leading to highly complex estimation methods. In this paper, we propose a computationally efficient digital cancellation solution that is shown to have substantially lower complexity compared to reference methods, while efficiently canceling intermodulation distortion (IMD) under a comprehensive modeling of the interference induced by two unrelated transmit signals. The simulation results demonstrate that the proposed method enables the cancellation of up to 12 dB of signal interference, while simultaneously reducing complexity by 90% compared to conventional full-complexity solutions.

In wireless communication systems where the received signal model is linear with Gaussian-distributed channel and noise, linear minimum mean square error (LMMSE) channel estimation (CE) achieves the best performance in terms of mean square error (MSE). However, LMMSE CE relies on parameters that may be either unavailable at the receiver (e.g., accurate knowledge of the power delay profile (PDP)) or too complex for practical implementation (e.g., the LMMSE filter size). A suboptimal choice of parameters may severely degrade LMMSE CE performance. Motivated by this observation, we investigate machine learning as a tool for refining and improving CE performance. We show that our proposed low-complexity learning-aided LMMSE CE can overcome the impact of suboptimal parameters and approach the ideal LMMSE performance.

Existing auto-encoder (AE)-based channel state information (CSI) frameworks have focused on a specific configuration of user equipment (UE) and base station (BS), and thus the input and output sizes of the AE are fixed. However, in the real-world scenario, the input and output sizes may vary depending on the number of antennas of the BS and UE and the allocated resource block in the frequency dimension. A naive approach to support the different input and output sizes is to use multiple AE models, which is impractical for the UE due to the limited HW resources. In this paper, we propose a universal AE framework that can support different input sizes and multiple compression ratios. The proposed AE framework significantly reduces the HW complexity while providing comparable performance in terms of compression ratio-distortion trade-off compared to the naive and state-of-the-art approaches.

In this paper we investigate the problem of precoder selection assuming compressed channel state information (CSI) feedback over sub-bands. Typically (e.g., in NR Rel-16) the problem is approached by a two-step solution: independent precoder optimization for each sub-band, followed by selection of the compression bases. We show that such conventional approach does not perform well in scenarios of medium/high frequency selectivity. Then, we derive an alternative approach, based on wideband capacity maximization, which provides superior performance under frequency-selective channels, while being compatible with the existing NR Rel-16 framework without any additional signaling. We finally propose an adaptive method in which the receiver dynamically selects the wideband or the sub-band optimization strategy, depending on the instantaneous channel condition. The proposed approach achieves consistent gains (up to 3dB) in all the considered test cases.

This paper explores to estimate power delay profile (PDP) effectively in order to aid bundle-based channel estimation under 3GPP new radio (NR) specification. The 3GPP radio access network (RAN) working group (WG) 1 adopts that data signals can be bundled every 2 or 4 resource block (RB) with their own precoding. In general, a base station called gNB could decide the precoding with its own algorithm, which is transparent to user equipment (UE). As a result, UE could not estimate PDP in a time domain due to the possibility that precoding is different per bundle in the same symbol. Consequently, this report investigates the PDP estimation via channel correlation across bundles in a frequency domain with help of deep learning. This paper does not pursue the estimated PDP to be close to the ideal one. Instead, we target to minimize the mean square error of channel estimation by using the deep learning technique, called advantage actor-critic (A2C). Both approaches look similar but cannot be guaranteed to be the same since channel estimation in practice is still approximated. The A2C algorithm using the policy gradient keeps improving rewards we defined in this paper iteratively. The proposed scheme can even outperform the performance using ideal PDP by adapting a trained network to channel estimation errors.

We employ a resource block (RB1)-based compression method using neural networks for the channel coefficients under the specification of the third generation partnership project (3GPP). An autoencoder is trained to compress/decompress the channel coefficients, and the same compressor/decompressor is used for all RBs. Because the proposed method is RBbased, it universally applies to various combinations of resource configurations allowed by 3GPP. It is essential to compress the channel coefficients because they are stored in a buffer that takes a large memory when a large bandwidth is allocated. The buffer provides the channel coefficients to different blocks of the baseband modem. We investigate the compression considering two formats for the complex channel coefficients, Cartesian and polar. For each case, we train and test a separate autoencoder to maximize the compression performance. We reduce the buffer size by about 3 times without losing the performance by more than 0.1 dB, and the proposed algorithm is reliably applicable to both new radio (NR) and long-term evolution (LTE).1A set of 12 consecutive subcarriers

In this paper, on phase noise of 5G NR mmWave systems, we propose a learning-based common phase error (CPE) estimation algorithm based on the denoising autoencoder serving as a nonlinear filter on the existing CPE estimators. The proposed algorithm learns the low dimension manifold of phase noise distributions with high probability. Traditional CPE methods have limitation when the time domain pilots are few, which causes degraded system performance with CPE interpolation. Besides accurate CPE estimation, the proposed method is more robust to various FR2 channels, Doppler effects, and the numerologies. Simulation results show that block error rate (BLER) could be improved up to 1.43 dB on SNR.

In this paper, we propose a learning-aided signal processing solution for channel estimation in 5G new radio (NR). Channel estimation is an important algorithm for baseband modem design. In 5G NR, estimating the channel is challenging due to two reasons. First, the pilot signals are transmitted over a small fraction of the available time-frequency resources. Second, the real time nature of physical layer processing introduces a strict limitation on the computational complexity of channel estimation. To this end, we propose a channel estimation technique that integrates a small one hidden layer neural network between two linear minimum mean squared error (LMMSE) interpolation blocks. While the neural network leverages the advantages of offline data-driven learning, the LMMSE blocks exploit the second order online channel statistics along time and frequency dimensions. The training procedure tunes the weights of the neural network by back-propagating through the time domain LMMSE interpolation block. We derive bounds on the training loss with the proposed method and show that our approach can improve the channel estimate.

This paper investigates the problem of noise vari-ance estimation in orthogonal frequency domain multiplexing (OFDM)-based systems such as 5G New Radio (NR). Accurate estimation of the noise variance is critical for the receiver performance, especially when applied with linear minimum mean square error (LMMSE) channel estimation (CE). A commonly used method estimates the noise variance from the power of the residual signal at the CE output. In this paper, we prove that such conventional estimator is biased, resulting in underestimation of the noise variance; then, we derive a bias correction method. Simulation results show that the proposed bias correction can significantly improve LMMSE CE performance, achieving up to 1dB gain in terms of block error rate (BLER).

We present a Doppler spread estimation algorithm based on supervised learning, which is compatible with 5G new radio (NR) specifications of 3rd Generation Partnership Project (3GPP). Compared to the conventional algorithm using a predefined function to characterize the relationship between Doppler spread and the channel correlation, the proposed algorithm learns such relationship from the training samples collected in practice. Specifically, a multi-layer perceptron (MLP) neural network is trained as the function approximator to estimate Doppler spread. We design the input features to the MLP, and propose a scheme for training data collection based on the performance metric, i.e., block error rate (BLER), and our domain knowledge of the system. Simulation results show the improved BLER performance, e.g., l.ldB gain at 10% BLER, is obtained in a scenario of high Doppler spread at frequency range 2 (FR2).

This paper explores to improve the quality of channel estimation when a bundling structure is imposed under 3GPP new radio (NR) specification. When bundling is configured, the precoding per bundle is determined solely by a gNB, which is transparent to a user equipment (UE). Due to the possibility that each bundle is precoded in a different manner, the power delay profile (PDP) needs to be estimated within a bundle in a frequency domain, not in a time domain. One simple approach is to assume that PDP is uniformly distributed up to the maximum delay spread. This method is acceptable when a channel is propagated within a short delay spread, but is not in general. Alternatively, this paper utilizes channel correlation even across bundles in a frequency domain and estimates PDP values by using all the correlation directly. A deep learning technique is applied with a neural network to generate correlation as if not to be precoded. To stabilize the performance, post-processing has been added to the neural network such as filtering, truncation and normalization. Simulation results show that the proposed method achieves the performance close to that of ideal PDP and outperforms the uniform PDP assumption by more than 2 dB.

This paper explores the blind decoding of control channels for obtaining other user identities in 3GPP specification, such as high-speed packet access and long-term evolution. The reliable decoding of control channels with user identities is crucial to mitigate inter-cell interference as well as multi-user interference. This paper exploits a method of user identity filtering followed by a method of user identity detection based on the traffic persistency, which is common to all standards. Hence, the proposed methods are applicable to all the standards regulated by 3GPP specification. In particular, this paper analyzes the proposed other user identity detection algorithm under the random coding. Simulation results show that the proposed method is reliable even at low SNRs and is also aligned with the analysis.

This paper proposes a way of applying interference- aware interference mitigation algorithms to device- to-device (D2D) communications in cellular networks for system throughput improvement. One of main purposes for using D2D communications is to offload throughput passing through a base station in cellular networks. In this sense, interference management between cellular and D2D signals is inevitable for both cellular and D2D mobile stations (MSs). Recently, interference-aware interference mitigation algorithms have been proposed in a theoretical aspect as well as a practical aspect. These algorithms operate based on interference information given at a MS, which can be obtained via network assistance or blind estimation. This paper explains how interference- aware algorithms can be applied to D2D communications in cellular networks. Moreover, this paper analyzes the throughput performance by deriving the rate upper-bounds. Simulation results demonstrate that the system throughput can be significantly improved while not losing the performance of a cellular MS too much.

This paper proposes an interference-aware interference cancellation (IAIC) algorithm that effectively mitigates inter-cell interference. In modern cellular networks, a serving signal could be disrupted due to interfering signals transmitted from neighbor cells in proximity to the serving cell. In order to overcome the disturbance of interfering signals, the recent 3GPP standard specification has explored a way of using network assistance, called network-assisted interference cancellation and suppression (NAICS). IAIC is in the same research direction of NAICS such that decoding an interfering signal as well as a serving signal is enabled via network assistance. Hence, IAIC is able to decode both signals as in a multiple access channel (MAC). However, a serving signal is only of concern while all decoded signals are of concern in a MAC. Using this characteristic, IAIC can be implemented as a practical solution. This paper demonstrates that the performance of IAIC is superior as well as requiring less complexity.

This paper proposes a symbol-level combining (SLC) scheme for hybrid automatic-repeat-request (HARQ), being used with an interference-aware successive decoding (IASD) algorithm [1]. Recently, it is revealed that the capacity in an interference channel over point-to-point codes can be achieved by combining two schemes: one is jointly decoding an interfering signal with a serving signal and the other is treating it as noise. Due to its high computational complexity, joint decoding can be practically replaced with successive decoding as suggested in [1]. However, when HARQ is enabled, it has been not well defined how interference should be handled at each transmission. Since interference is changed at each transmission, it is not helpful to store the information of interfering signals. Instead, the proposed scheme employes the decoded information of interfering signals in order to convert an interference channel to a point-to-point channel. The proposed scheme only requires a fixed size of memory and does not increase the detector complexity of IASD with respect to the number of retransmission. Simulation results demonstrate the superiority of the proposed scheme to the optimal SLC scheme at the conventional receiver.

This paper proposes a codeword-based iterative detection and decoding (IDD) algorithm for multiple-input multiple-output (MIMO) systems. In the proposed algorithm, multiple streams in a codeword are jointly detected at the rank-reduced (RR) maximum a posteriori (MAP) receiver and inter-stream interference is mitigated with successive interference cancelation (SIC). Thus, the algorithm is abbreviated to RR-MAP-SIC. Recent wireless standards such as Long-Term Evolution require the system to encode data bits per codeword, not per stream. As a result, conventional SIC algorithms could lose joint information among streams in a codeword because different streams of the same codeword are treated as interference. Instead, the proposed RR-MAP-SIC minimizes the loss of joint information by using the rank-reduced MAP detector. In addition, this paper compares the detector complexity of RR-MAP-SIC and investigates how the probability of symbol error is changed in terms of the covariance of the residual interference. As the number of iterations increases, the covariance is reduced so that the error events also decrease. Lastly, the extrinsic information transfer (EXIT) chart is used to analyze the performance of RR-MAP-SIC. Simulation results demonstrate the superiority of RR-MAP-SIC over the conventional algorithm and numerically verify the EXIT chart analysis.

This paper proposes a codeword-based iterative detecting and decoding (IDD) algorithm using a rank- reduced maximum likelihood (ML) detector with pre- whitened interference over multiple-input multiple- output (MIMO) channels. This iterative algorithm operates on the principle of successive interference cancelation (SIC) where all the layers per codeword are decoded together at each iteration. The recent wireless standard requires to encode data bits per codeword, not per layer at multiple antennas. Thus, SIC algorithms based on layer-separating detectors could lose joint information between layers in a single codeword. Instead, the proposed algorithm minimizes this loss by using rank-reduced ML detectors over soft feedback. Simulations are performed on a space-time bit-interleaved coded modulation over Rayleigh fading channels, and demonstrate the proposed SIC algorithm is superior to comparable iterative and non-iterative IDD algorithms.

This paper proposes an enhanced opportunistic beamforming scheme with power allocation search algorithms over finite feedback channels for a broadcast channel. Instead of continually varying the power allocation for multiple beams based on instantaneous channel state information (CSI) feedback, the proposed scheme determines power allocation in advance solely based on channel statistics and the number of mobile stations (MSs). This scheme maintains power allocation throughout multiple training and data transmission periods as long as channel statistics and the number of MSs are unchanged. As a result, the proposed scheme reduces the complexity for searching power allocation, and becomes robust over finite feedback channels. Computer simulations show that the proposed power allocation search algorithms efficiently find the near optimal power allocation with low complexity. The simulations also show that, although the proposed scheme predetermines power allocation solely based on channel statistics and the number of MSs, it outperforms the opportunistic beamforming scheme using instantaneous CSI feedback with finite feedback rates.

This paper addresses the power allocation problem on relaying channels with user-cooperation for multi-user orthogonal frequency division multiplexing (OFDM) systems. The proposed problem is based on dual-mode mobile stations (MSs), where one interface is used to receive signals from a base station (BS) and the other is used to forward the signals to nearby MSs through out-of-band relaying. This dual-mode operation enables MSs to cooperate with one another without consuming the capacity of a broadcast channel. Theoretically, the cooperation among MSs over orthogonal relaying channels has already been defined as conferencing. The proposed scheme uses conferencing to consider a broadcast channel with user-cooperation, where mobile power is distributed among subcarriers. Simulation results demonstrate that the proposed scheme achieves improved performance over the equal-power-allocation scheme on relay channels and the conventional broadcast scheme. These results are shown by considering the average throughput and the outage throughput.

This paper presents a cooperative relay strategy with a game-theoretic perspective. In multi-hop networks, each node needs to send traffic via relay nodes, which behave independently while staying aware of energy constraints. To encourage a relay to forward the packets, the proposed scheme formulates a Stackelberg game where two nodes sequentially bid their willingness weights to cooperate for their own benefits. Accordingly, all the nodes are encouraged to be cooperative only if a sender is cooperative and alternatively to be non-cooperative only if a sender is non-cooperative. This selective strategy changes the reputations of nodes depending on the amount of their bidding at each game and motivates them to maintain a good reputation so that all their respective packets can be treated well by other relays. This paper analyzes a Nash equilibrium from the proposed scheme and validates a sequential-move game by Stackelberg competition as opposed to a simultaneous-move game by Cournot competition. Simulation results demonstrate that the proposed scheme turns non-cooperative nodes into cooperative nodes and increases the cooperative relaying stimulus all over the nodes. Thus, every node forwards other packets with higher probability, thereby achieving a higher overall payoff.

We propose an interference-aware MAC protocol using a simple transmission strategy motivated by a game- theoretic approach. We formulate a channel access game, which considers nodes concurrently transmitting in nearby clusters, incorporating a realistic wireless communication model - the SINR model. Under inter-cluster interference, we derive a decentralized transmission strategy, which achieves a Bayesian Nash Equilibrium (BNE). The proposed MAC protocol balances network throughput and battery consumption at each transmission. We compare our BNE-based decentralized strategy with a centralized globally optimal strategy in terms of efficiency and balance. We further show that the transmission threshold should be adaptively tuned depending on the number of active users in the network, crosstalk, ambient noise, transmission cost, and radio-dependent receiver sensitivity. We also present a simple dynamic procedure for nodes to efficiently find a Nash Equilibrium (NE) without requiring each node to know the total number of active nodes or the channel gain distribution, and prove that this procedure is guaranteed to converge.

This paper presents a user-cooperation scheme with limited feedback in a broadcast channel. The proposed scheme allows mobile stations to forward received messages over finite-capacity orthogonal channels, known as conferencing. In addition, the proposed scheme jointly uses the feedback from mobile stations to mitigate multi-user interference by choosing the best beamforming vectors from a finite-sized codebook. As a result, a multi-user broadcast channel is located between a point-to-point multiple-input multiple-output channel and a multi-user multiple-input single-output channel. The former is where full coherent cooperation among receiver antennas is allowed, and the latter is where no cooperation among mobile stations is assumed. Then, this paper analyzes the relation between the amounts of user-cooperation and limited feedback, and shows a trade-off between the relative use of each. Simulation results verify the advantage of cooperation and feedback as complementary technologies in wireless cellular networks.

In a wireless multi-user system using multiple antennas, full multiplexing gain can be achieved through perfect channel state information (CSI) at the base station (BS). However, because of the limitations on the feedback rate and of standard requirements, such as the 3GPP, it is difficult to obtain perfect CSI, or even partial CSI with the extremely large number of feedback bits needed at the region of high signal-to-noise ratios (SNR) in practical systems. Instead of a feedback channel resource, this paper considers another resource for the system, a cooperation channel, which can be used by users to participate in cooperation in a broadcast fading channel. The cooperation among users in a multi-user system has the effect of transforming it into a cooperative point-to-point MIMO system. This paper analyzes the cooperation channel gain and the mobile power used in a cooperation channel to maintain a constant rate gap against the zero-forcing beamforming scheme with full CSI at the BS. In addition, the performance results of this proposed cooperation strategy are provided by computer simulations.

This paper proposes the opportunistic power allocation (OPA) scheme for random beamforming with a limited feedback rate. Without the channel state information, the proposed scheme allocates different amounts of power to different beams. Various low-complexity search algorithms for the proposed OPA scheme are also proposed to find the optimal power allocation that maximizes the sum rate. Computer simulations show that the proposed OPA scheme significantly improves the sum rate, and the optimal power allocation is found with low complexity. In addition, it is shown that the number of beams should increase as the SNR increases in order to maximize the sum rate.