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This study proposes a regression-based approach for calculating the likelihood probability in time-varying multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters. These time-varying MIMO systems often face performance challenges because of the difficulty in tracking changes in the likelihood probability. To address this challenge, the proposed method leverages channel statistics and decoded outputs to refine the likelihood. An optimization problem is then formulated to minimize the mean-squared error between the true and refined likelihood probabilities. A linear regression approach is derived to solve this problem, and a regularization technique is applied to further optimize the calculation. The simulation results indicate that the proposed method improves reliability by effectively tracking temporal variations in the likelihood probability and outperforms conventional methods in terms of performance. [ABSTRACT FROM AUTHOR]

This study proposes a novel power control method for amplify-and-forward (AF) relay systems operating in time-varying channels. Transmit power control between the source and relay nodes significantly enhances the performance of the AF relay system, and the improvements are proportional to the number of antennas. However, these enhancements are restricted by the presence of time-varying channels, and this limitation increases in severity with increasing number of antennas. To address the challenges posed by these channel variations, the proposed method adaptively optimizes the power-scaling factors to minimize the mean-squared errors under a power-inequality constraint. Numerical results demonstrate that the proposed method effectively mitigates the bit-error-rate performance degradation caused by channel variations, maintaining robust performance even in systems with a large number of antennas. This approach offers a promising solution for improving the reliability and efficiency of AF relay systems under dynamic channel conditions. [ABSTRACT FROM AUTHOR]

Abstract Here, fast time‐varying channels of high‐mobility vehicle‐to‐vehicle communications for massive multiple‐input multiple‐output orthogonal frequency division multiplexing systems are considered. Large‐scale uniform linear arrays are configured at the transmitter and receiver to separate multiple angle domain Doppler frequency offsets based on transmit and receive beamforming with high spatial resolution. Then, each beamforming branch comprises only one dominant Doppler frequency offset. Next, the conventional channel estimation method is performed for each beamforming branch, and carry out maximum‐ratio‐combining for data detection. Power spectrum density and Doppler spread of the equivalent link between the transmitter and receiver are derived and regarded as the criterion for assessing the residual channel time variation caused by limited antennas in practice. Interestingly, a scaling law between the asymptotic Doppler spread and the number of transceiver antennas shows that asymptotic Doppler spread is proportional to the maximum Doppler frequency offset and decreases at the rate of 1NT2+1NR2, where NT and NR are the number of transmit and receive antennas, respectively. Simulation results confirm the validity of the proposed Doppler suppression framework in high‐mobility vehicle‐to‐vehicle communications.

Here, fast time‐varying channels of high‐mobility vehicle‐to‐vehicle communications for massive multiple‐input multiple‐output orthogonal frequency division multiplexing systems are considered. Large‐scale uniform linear arrays are configured at the transmitter and receiver to separate multiple angle domain Doppler frequency offsets based on transmit and receive beamforming with high spatial resolution. Then, each beamforming branch comprises only one dominant Doppler frequency offset. Next, the conventional channel estimation method is performed for each beamforming branch, and carry out maximum‐ratio‐combining for data detection. Power spectrum density and Doppler spread of the equivalent link between the transmitter and receiver are derived and regarded as the criterion for assessing the residual channel time variation caused by limited antennas in practice. Interestingly, a scaling law between the asymptotic Doppler spread and the number of transceiver antennas shows that asymptotic Doppler spread is proportional to the maximum Doppler frequency offset and decreases at the rate of 1NT2+1NR2$\sqrt {\frac{1}{{{N_T}^2}} + \frac{1}{{{N_R}^2}}}$, where NT${N_T}$ and NR${N_R}$ are the number of transmit and receive antennas, respectively. Simulation results confirm the validity of the proposed Doppler suppression framework in high‐mobility vehicle‐to‐vehicle communications. [ABSTRACT FROM AUTHOR]

This study investigates the resource management problem for millimeter-wave-based switched beam (SWB) full-duplex small cell networks with the consideration of user equipment’s (UE’s) quality of experience (QoE) requirement and time-varying wireless channels. An optimization problem is formulated to maximize the long-term QoE by implementing beam assignment (BA) and rate control (RC) under short-term beam and long-term energy efficiency constraints. By leveraging the Lyapunov optimization technique, the original problem is converted into a series of BA and RC problems in each time slot. To solve the converted problem with affordable complexity, novel closed-form solutions for BA and RC are first derived by considering beam constraints in SWB systems. A decomposition-based BA and RC (DBR) algorithm with only polynomial computational complexity is then proposed based on the derived closed-form solutions. The simulation results demonstrate that the proposed DBR method can effectively[‘effectively’ appears to be a more suitable word in this case.] balance the performance and complexity because the DBR scheme outperforms the benchmark scheme and achieves nearly optimal performance in terms of system delay and QoE.

Abstract In this letter, a novel digital pre‐distortion (DPD) scheme for an end‐to‐end forward link (FL) channel in a satellite internet network (SIN) is first proposed. It aims at the unprecedented challenge of the model of non‐stationary (NS) cascade response consisting of channel fading and non‐linear behaviour of onboard payload. Unlike most existing schemes, the proposed DPD scheme explores a new model to describe the distortion with improved band‐limited memory polynomial (BLMP) by the equivalent response. To decrease the complexity of utilization, the authors design a new strategy to divide the unified non‐stationary situation into several different stationary situations. Meanwhile, a space‐time‐frequency stationary window function (STF‐WF) is proposed to compensate for the signal distortion. The experimental results demonstrate that the proposed schemes realize maximum total degradation (TD) gain by 15%.

For physical layer security with multiple antennas over wireless channels, we consider an artificial noise-aided secure beamforming system. A transmitter can send the confidential information to the legitimate user more securely without eavesdropping when an artificial jamming signal interfering eavesdroppers is transmitted with the confidential information signal. The transmitter splits its transmit power for both the information and jamming signals with a power splitting factor. Under such a system model, we investigate the impacts on secrecy performance of a power splitting factor, the numbers of antennas and eavesdroppers, and noise variance at the legitimate receivers and eavesdroppers by analyzing the expected secrecy rate. The optimal power splitting factor and limiting secrecy rate with large number of antennas are also derived. Moreover, we examine the expected secrecy rate loss caused by channel variation over time. It is shown that the secrecy rate loss is independent of system parameters like a power splitting factor, the numbers of antennas and eavesdroppers in a high signal to interference plus noise ratio (SINR) region. In a low SINR region, on the other hand, the secrecy rate loss changes with system parameters. Simulation results verify these observations on ergodic secrecy rate and its loss due to time-varying channels.

Channel estimation is a challenging issue for millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) in the future sixth generation (6G) wireless systems, where the conventional estimation schemes may fail to track the fast varying channels, especially in high-speed mobile scenarios. In this paper, a novel tensor-based uplink channel estimation scheme is proposed for multi-user MIMO (MU-MIMO) systems over time-varying channels. In the proposed scheme, a low-overhead pilot transmission scheme is designed to track the varying channel. The received uplink signal at the base station (BS) is formulated as a third-order tensor which admits a CANDECOMP/PARAFAC (CP) model. The CP decomposition issue is then solved using blind matrix decomposition, in which the special structures of signals in the time dimension and the matrix subspace are utilized. By exploiting low-rank structure of the signal tensor, the channel parameters (angles of arrival/departure, path gains, and Doppler shifts) are estimated from the factor matrices. Moreover, the proposed scheme is theoretically analyzed and it is guaranteed with low pilot overhead. Simulation results verify that the proposed scheme can outperform the compressed sensing (CS) based scheme and the iteration-based scheme in terms of accuracy and stability. The uniqueness of the proposed scheme is also verified in our simulation. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We consider a practical setting where (i) the IRS reflection coefficients are configured by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not applicable in this setting due to the difficulty of channel estimation and the low feedback rate. Therefore, we develop a novel adaptive codebook-based limited feedback protocol where only a codeword index is transferred to the IRS. We propose two solutions for adaptive codebook design, random adjacency (RA) and deep neural network policy-based IRS control (DPIC), both of which only require the end-to-end compound channels. We further develop several augmented schemes based on RA and DPIC. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by our schemes. [ABSTRACT FROM AUTHOR]

The paper develops novel algorithms for time-varying (TV) sparse channel estimation in Massive multiple-input, multiple-output (MMIMO) systems. This is achieved by employing a novel reduced (non-uniformly spaced tap) delay-line equalizer, which can be related to low/reduced rank filters. This low rank filter is implemented by deriving an innovative TV (Krylov-space based) Multi-Stage Kalman Filter (MSKF), employing appropriate state estimation techniques. MSKF converges very quickly, within few stages/iterations (at each symbol). This is possible because MSKF uses those signal spaces, maximally correlated with the desired signal, rather than the standard principal component (PCA) signal spaces. MSKF is also able to reduce channel tracking errors, encountered by a standard Kalman filter in a high-mobility channel. In addition, MSKF is well suited for large-scale MMIMO systems. This is unlike most existing methods, including recent Bayesian-Belief Propagation, Krylov, fast iterative re-weighted compressed sensing (RCS) and minimum rank minimization methods, which requires more and more iterations to converge, as the scale of MMIMO system increases. A Bayesian Cramer Rao lower bound (BCRLB) for noisy CS (in sparse channel) is also derived, which provides a benchamrk for the performance for novel MSKF and other CS estimators.

Sparse scatterer identification with atomic norm minimization (ANM) techniques in the delay-Doppler domain is investigated for a vehicle-to-infrastructure millimeter wave propagation channel. First, a two-dimensional ANM is formulated for jointly estimating the time-delays and Doppler frequencies associated with individual multipath components (MPCs) from short-time Fourier transformed measurements. The two-dimensional ANM is formulated as a semi-definite program and promotes sparsity in the delay-Doppler domain. The numerical complexity of the two-dimensional ANM limits the problem size which results in processing limitations on the time-frequency sample matrix size. Subsequently, a decoupled form of ANM is used together with a matrix pencil, allowing a larger sample matrix size. Simulations show that spatial clusters of a point-scatterers with small cluster spread are suitable to model specular reflection which result in significant MPCs and the successful extraction of their delay-Doppler parameters. The decoupled ANM is applied to vehicle-to-infrastructure channel sounder measurements in a sub-urban street in Vienna at 62 GHz. The obtained results show that the decoupled ANM successfully extracts the delay-Doppler parameters in high resolution for the channel’s significant MPCs.

Abstract Polarisation diversity radar enhances the performance of target detection. Time reversal exploits the multipath effect by adaptively matching propagation channels to improve the signal‐to‐noise ratio (SNR) and realise high detectability. This work combines polarisation diversity and time‐reversal technology to propose a time‐reversal detector suitable for a polarisation array radar. The maximum likelihood estimate is carried out for the time‐varying channel response to solve the channel mismatch problem, which may cause the degradation of time‐reversal performance. In addition, a conventional polarisation detector is developed under the same varying channel scenario to benchmark the time‐reversal detector, and furthermore, the SNR gain of the time‐reversal detector over the conventional detector is derived. Numerical simulations demonstrate the superiority of the time‐reversal detector. Both the polarisation detectors proposed can take advantage of the channel variations to improve the detectability, and the greater the energy brought by the channel changes, the better the detection performance achieved.

In this paper, simultaneous wireless information and power transfer based on time–frequency block allocation (TFBA-SWIPT) is proposed to save the energy of an orthogonal frequency-division multiplex (OFDM) decode-and-forward cooperative communication system, by allocating different resource blocks for information decoding and energy harvesting at the relay and destinations. The joint allocation of time–frequency blocks and power is optimized to maximize the transmission rate of the destination, while ensuring the transmission rate and energy supply of the relay. The time–frequency block pairing algorithm and the block power allocation algorithm are integrated to achieve the optimal solution. Simulation results illustrate the benefits of using TFBA-SWIPT in the OFDM cooperative communication system over time-varying channels. [ABSTRACT FROM AUTHOR]

We study the generalized degrees of freedom (gDoF) of the block-fading noncoherent 2-user interference channel (IC) with a coherence time of $T$ symbol durations and symmetric fading statistics. We demonstrate that a standard training-based scheme for the noncoherent IC is suboptimal in several regimes. We study and analyze several alternate schemes: the first is a new noncoherent scheme using rate-splitting. We also consider a scheme that treats interference-as-noise (TIN) and a time division multiplexing (TDM) scheme. We show that a standard training-based scheme for the noncoherent IC is outperformed by one of these schemes in several regimes: our results demonstrate that in the very weak interference regime, the TIN scheme is the best; in the strong interference regime, the TDM scheme and the noncoherent rate-splitting scheme give better performance; in other cases either of the TIN, TDM or noncoherent rate-splitting scheme could be preferred. We also study the noncoherent IC with feedback and propose another noncoherent rate-splitting scheme. Again for the feedback case, our results demonstrate that a standard training-based scheme can be outperformed by other schemes. [ABSTRACT FROM AUTHOR]

Accurate channel state information (CSI) is important for MIMO systems, especially in a high-speed scenario, fast time-varying CSI tends to be out of date, and a change in CSI shows complex nonlinearities. The kernel recursive least-squares (KRLS) algorithm, which offers an attractive framework to deal with nonlinear problems, can be used in predicting nonlinear time-varying CSI. However, the network structure of the traditional KRLS algorithm grows as the training sample size increases, resulting in insufficient storage space and increasing computation when dealing with incoming data, which limits the online prediction of the KRLS algorithm. This paper proposed a new sparse sliding-window KRLS (SSW-KRLS) algorithm where a candidate discard set is selected through correlation analysis between the mapping vectors in the kernel Hilbert spaces of the new input sample and the existing samples in the kernel dictionary; then, the discarded sample is determined in combination with its corresponding output to achieve dynamic sample updates. Specifically, the proposed SSW-KRLS algorithm maintains the size of the kernel dictionary within the sample budget requires a fixed amount of memory and computation per time step, incorporates regularization, and achieves online prediction. Moreover, in order to sufficiently track the strongly changeable dynamic characteristics, a forgetting factor is considered in the proposed algorithm. Numerical simulations demonstrate that, under a realistic channel model of 3GPP in a rich scattering environment, our proposed algorithm achieved superior performance in terms of both predictive accuracy and kernel dictionary size than that of the ALD-KRLS algorithm. Our proposed SSW-KRLS algorithm with M = 90 achieved 2 dB NMSE less than that of the ALD-KRLS algorithm with v = 0.001 , while the kernel dictionary was about 17% smaller when the speed of the mobile user was 120 km/h. [ABSTRACT FROM AUTHOR]

To achieve the performance gains of massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) must be acquired at the base station (BS). In frequency division duplexing (FDD) massive MIMO systems, the BS always first transmits downlink pilot symbols so that the user equipment (UE) can estimate CSI and then feedback to the BS. However, the huge number of antennas at the BS will lead to overwhelming feedback overhead. Moreover, time-varying caused by high mobility of user terminals makes the priori channel knowledge of the channels to change from one slot to another so that CSI aquisition is hard. To simultaneously reduce the overhead of overwhelming downlink pilot signaling and uplink feedback in time-varying massive MIMO systems, we propose a channel estimation scheme based on compressive sensing (CS) and deep learning (DL) in frequency division duplexing (FDD) massive MIMO systems. Specifically, we first develop a new CS-based algorithm for sparse channel estimation, which requires no priori knowledge of channel statistics. After obtaining the innitial channel estimation, we utilize two DL-based networks, named DnNet and DnLSTM respectively for denoising. Simulation results demonstrate that the proposed method can considerably reduce the training and feedback overhead and outperform the existing classical algorithms. [ABSTRACT FROM AUTHOR]

We study communication in the presence of a jamming adversary where quadratic power constraints are imposed on the transmitter and the jammer. The jamming signal is allowed to be a function of the codebook, and a noncausal but noisy observation of the transmitted codeword. For a certain range of the noise-to-signal ratios (NSRs) of the transmitter and the jammer, we are able to characterize the capacity of this channel under deterministic encoding or stochastic encoding, i.e., with no common randomness between the encoder/decoder pair. For the remaining NSR regimes, we determine the capacity under the assumption of a small amount of common randomness (at most $2\log (n)$ bits in one sub-regime, and at most $\Omega ({n})$ bits in the other sub-regime) available to the encoder-decoder pair. Our proof techniques involve a novel myopic list-decoding result for achievability, and a Plotkin-type push attack for the converse in a subregion of the NSRs, both of which may be of independent interest. We also give bounds on the strong secrecy capacity of this channel assuming that the jammer is simultaneously eavesdropping. [ABSTRACT FROM AUTHOR]

Abstract Multistatic radar has the advantage of spatial diversity, but its detection performance is decreased in dense multipath scattering environments. This problem can be solved by using the time reversal (TR) technique to take advantage of the multipath effect to improve target detection; however, this usually requires a stationary channel response that is difficult to achieve in practice. This article studies a TR detection algorithm for a multistatic radar system in a varying multipath channel environment. We establish a varying channel response model and derive a time reversal likelihood ratio test (TR–LRT) detector to utilise the characteristics of multiple paths when the multipath environment is changing during the detection process. We use Monte Carlo simulations and theoretical analysis to show the superior performance of the proposed TR detector compared with a conventional detector. Our proposed TR–LRT detector shows good robustness to environmental variations. Simulation results show that better detection results are achieved in a more severe multipath scattering environment.

Summary: This paper introduces the concept of spatial and media‐based modulated (SMBM) orthogonal frequency division multiplexing (OFDM) as a potential candidate for highly mobile next generation beyond 5G (B5G) wireless communications. The proposed SMBM‐OFDM technique utilizes not only the transmit antenna and channel state indices but also OFDM subcarriers to improve the system performance under high mobility. In addition, this study sheds light on challenging fast time‐varying channel estimation problem of MBM‐based systems by using the linear minimum mean square error (LMMSE) approach due to its optimality to investigate the achievable system performance with the aid of basis expansion modeling. The minimum lower bound on the channel estimation error (Bayesian Cramer–Rao bound) is derived theoretically and shown to be attainable by the considered LMMSE estimator. Moreover, symbol detection performance is provided for different modulation types and higher mobile velocities. Simulation results demonstrate that SMBM‐OFDM system under high mobility is able to provide around 12‐dB performance gains in terms of both channel estimation and symbol detection error compared to conventional spatial modulation (SM)‐OFDM systems without MBM. The presented framework is important due to addressing the high mobility support of SMBM‐OFDM systems for B5G wireless communications in terms of achievable channel estimation and data detection performance. [ABSTRACT FROM AUTHOR]

This article concerns the physical layer security (PLS) problem in distributed antenna systems (DASs), where eavesdroppers are equipped with multiple antennas. If both legitimate transmitter (Alice) and receiver (Bob) broadcast wireless signals via a DAS, the channel state information (CSI) between Alice and Bob may be exposed to an eavesdropper (Eve) due to the broadcast nature of wireless signals. Based on the exposed CSI, Eve can use multiple antennas to improve the eavesdropping capability on the confidential message transmitted from Alice. The prior PLS approaches to defend against the eavesdropping attack have two limitations. The first limitation is to assume that the CSI of Bob is available to Alice. The second limitation is to assume that there is no error in the feedback CSIs. In this article, we propose a new PLS scheme of a DAS by using one-bit feedback information. The basic idea of our approach is to generate a null transmission vector from the distributed transmitters of Alice to Bob while using different artificial noise components to protect the exposure problem of CSI between Alice and Bob. The proposed scheme has the following advantages: improved secrecy rate, rapid convergence, and good performance under a time-varying channel. We implemented our scheme and conducted extensive performance comparisons through simulations. Our experimental results show that the proposed scheme improves the secrecy rate with 1.20 b/s/Hz as compared to prior approaches. At last, we present some interesting future works for the PLS problem in a DAS. [ABSTRACT FROM AUTHOR]

Due to their fast deployment and high flexibility, unmanned aerial vehicles (UAVs) are regarded as vital enablers of various applications that include emergency rescue, telecommunications, delivery of supplies, and more. In order to connect UAVs to a core network anywhere, satellite communications is expected to provide wider backhaul coverage compared to ground wireless communications. Although UAV Communications has been studied in many technical works, high-mobility satellite-UAV communications has not been investigated well. Specifically, physical layer operations of the high-mobility satellite-UAV communication system are affected the most by high-mobility environments. In this article, we focus on the physical layer operation problem in satellite-UAV communications from three perspectives: time-varying channel estimation, the high-mobility transceiver, and high-mobility multiple access. Related key technologies, existing works, open problems, and the next development direction are introduced and surveyed. Furthermore, our proposed scheme is also discussed. Overall, this article aims to help readers understand challenges, solutions, and future research trends regarding physical layer operation issues on high-mobility satellite-UAV aerial communications. [ABSTRACT FROM AUTHOR]

In this letter, a novel digital pre‐distortion (DPD) scheme for an end‐to‐end forward link (FL) channel in a satellite internet network (SIN) is first proposed. It aims at the unprecedented challenge of the model of non‐stationary (NS) cascade response consisting of channel fading and non‐linear behaviour of onboard payload. Unlike most existing schemes, the proposed DPD scheme explores a new model to describe the distortion with improved band‐limited memory polynomial (BLMP) by the equivalent response. To decrease the complexity of utilization, the authors design a new strategy to divide the unified non‐stationary situation into several different stationary situations. Meanwhile, a space‐time‐frequency stationary window function (STF‐WF) is proposed to compensate for the signal distortion. The experimental results demonstrate that the proposed schemes realize maximum total degradation (TD) gain by 15%. [ABSTRACT FROM AUTHOR]

A detailed assessment of the radiofrequency interference from terrestrial fixed service into aeronautical earth stations operating with geostationary satellite orbit networks in the fixed-satellite service is performed. We analyze the impact of the major physical, technical, operational, and regulatory parameters in the interference environment having multiple degrees of freedom to be considered. By performing exhaustive geometric computer simulations, we determine the statistical characteristics of the time-variant radiofrequency interference caused by multiple terrestrial fixed service transmitters using information of existing/registered fixed service stations in France. Subsequently, a stochastic channel model to predict the impact of the terrestrial interference using a higher order finite-state Markov process is developed. Balancing model accuracy and complexity, a reasonable vector quantization clustering algorithm is applied for the partitioning of the interference into representative channel states. For these channel states, we provide a method for scaling this model to different antenna sizes and flight velocities, to be used for development of interference mitigation measures and system design of aeronautical earth stations in motion operating in the 17.7–19.7 GHz frequency range. [ABSTRACT FROM AUTHOR]

A channel time variation suppression scheme based on beamforming is proposed for high-mobility orthogonal frequency division multiplexing (OFDM) downlink transmissions. The residual channel time variation after Doppler shift compensation by finite resolution receive beamforming can be measured by the Doppler spread. The interchannel interference (ICI) due to the time-varying channel is then analyzed and the relation between the signal-to-interference ratio (SIR) and Doppler spread is given. Based on the derived Doppler spread and ICI, a beamforming network optimization scheme is proposed to reduce the channel time variation. A closed-form solution is first obtained to minimize the average ICI, which exploits the structure of a tridiagonal Toeplitz matrix. Next, an optimization problem is formulated to minimize the maximum Doppler spread to further verify the impact of average ICI on channel time variation. The sequential parametric convex approximation (SPCA) algorithm is exploited to solve the non-convex min-max problem. Numerical results verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

This article describes a novel approach for robust and reliable underwater acoustic communication targeting low data rate applications in very challenging channels. The well-known binary frequency shift keying (BFSK) modulation is extended with multiple pair-wise BFSK subcarriers forming a multicarrier (MC) waveform referred to as MC-BFSK. Time offsets between neighboring subcarriers combined with windowing and guard periods are employed to increase the robustness against Doppler-induced frequency spread/shift and reverberation together with novel methods for synchronization, peak-to-average-power ratio reduction, and soft-value extraction. For the design of the parameters of the waveform, a heuristic approach is utilized. A noncoherent matched filter receiver with spatial diversity is adopted. The MC-BFSK transmission scheme is evaluated by means of computer simulations as well as sea-trial measurements. The obtained results are compared with competing modulation schemes where the proposed MC-BFSK shows better performance in challenging channels. In particular, we are able to decode frames in a sea-trial scenario where the direct path between the transmitter and the receiver is blocked by an island. [ABSTRACT FROM AUTHOR]

Dynamic channel clustering and modeling have recently attracted much interest. Extensive channel measurements have shown that multipath components (MPCs) generally exist as clusters. Therefore, an accurate clustering algorithm is required to model the time-varying channels. However, the dynamic behaviors of MPCs have not been well considered in the existing algorithms. In this paper, we present a new metric for MPCs’ clustering in time-variant channels, which considers MPC’s evolution in time domain and clusters MPCs based on the fluctuation and multi-dimension distance of MPC’s trajectories. The performance of the new metric is verified by comparing measurements and simulations. It is found that the proposed algorithm using the new metric can well recognize the dynamic behaviors of MPCs and identify time-varying clusters accurately. [ABSTRACT FROM AUTHOR]

In this paper, we study asynchronous federated learning (FL) in a wireless distributed learning network (WDLN). To allow each edge device to use its local data more efficiently via asynchronous FL, transmission scheduling in the WDLN for asynchronous FL should be carefully determined considering system uncertainties, such as time-varying channel and stochastic data arrivals, and the scarce radio resources in the WDLN. To address this, we propose a metric, called an effectivity score, which represents the amount of learning from asynchronous FL. We then formulate an Asynchronous Learning-aware transmission Scheduling (ALS) problem to maximize the effectivity score and develop three ALS algorithms, called ALSA-PI, BALSA, and BALSA-PO, to solve it. If the statistical information about the uncertainties is known, the problem can be optimally and efficiently solved by ALSA-PI. Even if not, it can be still optimally solved by BALSA that learns the uncertainties based on a Bayesian approach using the state information reported from devices. BALSA-PO suboptimally solves the problem, but it addresses a more restrictive WDLN in practice, where the AP can observe a limited state information compared with the information used in BALSA. We show via simulations that the models trained by our ALS algorithms achieve performances close to that by an ideal benchmark and outperform those by other state-of-the-art baseline scheduling algorithms in terms of model accuracy, training loss, learning speed, and robustness of learning. These results demonstrate that the adaptive scheduling strategy in our ALS algorithms is effective to asynchronous FL. [ABSTRACT FROM AUTHOR]

Orthogonal chirp division multiplexing (OCDM) is a recently proposed multiplexing scheme and has drawn increasing attention in underwater communications. In an OCDM system, a series of orthogonal chirp signals carry the data information. However, the time variation feature of underwater acoustic (UWA) channels is rarely considered in the existing OCDM systems. In this paper, a particular transmission structure is proposed for the OCDM system to process the signal block-by-block at the receiver. Moreover, a low complexity channel estimation algorithm and the corresponding equalizers are derived. The proposed structure has a better ability to track time-varying channels. The simulation results show that the proposed method achieves promising performance under time-varying channels. [ABSTRACT FROM AUTHOR]

A novel intercarrier interference (ICI)-aware orthogonal frequency division multiplexing (OFDM) channel estimation network ICINet is presented for rapidly time-varying channels. ICINet consists of two components: a preprocessing deep neural subnetwork (PreDNN) and a cascaded residual learning-based neural subnetwork (CasResNet). By fully taking into account the impact of ICI, the proposed PreDNN first refines the initial channel estimates in a subcarrier-wise fashion. In addition, the CasResNet is designed to further enhance the estimation accuracy. The proposed cascaded network is compatible with any pilot patterns and robust against mismatched system configurations. Simulation results verify the superiority of ICINet over existing networks in terms of better performance and much less complexity. [ABSTRACT FROM AUTHOR]

In this paper, a novel beam alignment algorithm based on the sparse graph coding theory is proposed for millimeter wave (mmWave) time-varying channels. Firstly, a pilot design method is introduced to transform the mmWave time-varying beam alignment into a sparse-graph design and detection problem. Inspired by Low-Density-Parity-Check (LDPC) codes and fountain codes, a multi-stage sparse coding method is proposed for the design of the measurement matrix and the theoretical bound of the probability of success is derived to guide the design of the sparse-graph. A beam alignment algorithm is subsequently proposed to detect the beam index and estimate the carrier frequency offset (CFO). Then, the Carmeŕ-Rao Lower Bound (CRLB) is derived. Simulation results demonstrate that the proposed beam alignment algorithm achieves significant performance improvements over the conventional counterparts in both the noiseless and noise cases. [ABSTRACT FROM AUTHOR]

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker’s model update while multiple workers’ updates incur significant interference under limited bandwidth. To address these challenges, in this work we formulate a novel constrained optimization problem, and propose an FL framework harnessing wireless channel perturbations and interference for improving privacy, bandwidth-efficiency, and scalability. The resultant algorithm is coined analog federated ADMM (A-FADMM) based on analog transmissions and the alternating direction method of multipliers (ADMM). In A-FADMM, all workers upload their model updates to the parameter server (PS) using a single channel via analog transmissions, during which all models are perturbed and aggregated over-the-air. This not only saves communication bandwidth, but also hides each worker’s exact model update trajectory from any eavesdropper including the honest-but-curious PS, thereby preserving data privacy against model inversion attacks. We formally prove the convergence and privacy guarantees of A-FADMM for convex functions under time-varying channels, and numerically show the effectiveness of A-FADMM under noisy channels and stochastic non-convex functions, in terms of convergence speed and scalability, as well as communication bandwidth and energy efficiency. [ABSTRACT FROM AUTHOR]

This study elucidates a reinforcement learning (RL)-based detection with one-bit analog-to-digital converters (ADCs) in time-varying massive multiple-input and multiple-output channels. In one-bit ADCs, conventional channel estimation exhibits poor performance owing to nonlinear quantization. The RL-based detection alleviates this degradation by learning the true likelihood probability during data transmission. However, in time-varying channels, the learned likelihood probability is inconsistent with the true likelihood probability due to temporal channel variations. This inconsistency can cause severe performance degradation. To effectively exploit the learned likelihood probability, we propose a training length adaptation method that determines an appropriate training length based on the channel conditions. To achieve this, we consider an optimization problem that minimizes the training length while guaranteeing the performance of RL-based detection. The solution of the optimization problem is obtained by an explicit form based on simple approximations, and it reveals that the optimal training length depends on the change in the likelihood probability. Simulation results demonstrate that the proposed method efficiently reduces the training length when a rapid change in likelihood probability is produced in fading channels. Moreover, this reduction contributes to improving the spectral efficiency by an avoiding undesirable learning process. Consequently, the spectral efficiency of the proposed method can be significantly increased compared to that of conventional RL-based detection. For instance, the proposed method achieves 1.76 times higher spectral efficiency than the conventional method at 30 km/h. [ABSTRACT FROM AUTHOR]

The recent emergence of orthogonal time frequency space (OTFS) modulation as a novel PHY-layer mechanism is more suitable in high-mobility wireless communication scenarios than traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have analyzed OTFS performance using theoretical and ideal baseband pulseshapes, a challenging and open problem is the development of effective receivers for practical OTFS systems that must rely on non-ideal pulseshapes for transmission. This work focuses on the design of practical receivers for OTFS. We consider a fractionally spaced sampling (FSS) receiver in which the sampling rate is an integer multiple of the symbol rate. For rectangular pulses used in OTFS transmission, we derive a general channel input-output relationship of OTFS in delay-Doppler domain without the common reliance on impractical assumptions such as ideal bi-orthogonal pulses and on-the-grid delay/Doppler shifts. We propose two equalization algorithms: iterative combining message passing (ICMP) and turbo message passing (TMP) for symbol detection by exploiting delay-Doppler channel sparsity and the channel diversity gain via FSS. We analyze the convergence performance of TMP receiver and propose simplified message passing (MP) receivers to further reduce complexity. Our FSS receivers demonstrate stronger performance than traditional receivers and robustness to the imperfect channel state information knowledge. [ABSTRACT FROM AUTHOR]

Filter bank multi-carrier (FBMC) is an emerging 5G technology due to its finer spectral characteristics than its counterpart orthogonal frequency-division multiplexing (OFDM). In most cases, FBMC is united with an Offset Quadrature Amplitude modulation (OQAM) for individual sub-carrier to maximize the spectrum efficiency and Nyquist rate. But it is achieved at the cost of an inherent accumulative imaginary interference called intrinsic interference. When a channel is highly selective, the FBMC system undergoes inter-symbol-interference (ISI) and co-channel interference (CCI). In this research, we consider FBMC QAM as a solution to intrinsic interference and mitigates both ISI and CCI by incorporating ML equivalent signal detection. In this paper, some of the prominent issues related to the existing FBMC–OQAM are explored and its potential integrity constraints with existing MIMO and signal detection techniques are studied. In addition to that, a lightweight optimized ML which constitutes parallel sub-detectors with the least hardware complexity overhead is proposed. It also achieved a near-optimal performance that matches the CP-OFDM system. In this research, the experimental results proved that the proposed suboptimal ML detector offered significant complexity reduction with improved quality of services. Thus evidently shows the proposed system is attractive from both BER performance and hardware complexity reduction perspectives for 5G communications. [ABSTRACT FROM AUTHOR]

This article focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Secondly, the 3D-Newtonized orthogonal matching pursuit algorithm is utilized for the extraction of the UL channel parameters, including channel gains, directions of arrival, delays, and Doppler frequencies, over the antenna-time-frequency domain. Thirdly, we carefully analyze energy dispersion and power leakage of the 3D angle-delay-Doppler channels. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides and to assure that the observation regions for different users do not overlap over the 3D cubic area, i.e., angle-delay-Doppler domain. After scheduling, different users can map their respective data to the scheduled delay-Doppler domain grids, and simultaneously send the data to base station (BS) without inter-user interference in the same OTFS block. Correspondingly, the signals at desired grids within the 3D resource space of BS are separately collected to implement the 3D channel estimation and maximal ratio combining-based data recovery over the angle-delay-Doppler domain. Then, we construct a low complexity beamforming scheme over the angle-delay-Doppler domain to achieve inter-user interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme. [ABSTRACT FROM AUTHOR]

In parallel with the decrease in cost, size and weight of Unmanned Aerial Vehicles (UAVs) and the increase of their flight autonomy, many commercial applications are rapidly arising. Most of those applications rely on a communications system between a terrestrial base station and the UAV. Due to the UAV movement, time-variant channel models are required. In this article, we propose a geometrical model for the channel Multipath Components (MPCs) evolution with the UAV flight that supports MPCs that are born and die in several occasions due to blockages. Based on this model, the novel Geometry-Based Spatial-Consistent MPC Tracking Method (GSTM) is proposed and its performance on channel MPCs tracking was shown both by simulations and by an Air-to-Ground (A2G) low-height UAV measurement campaign. The GSTM also provides the parameters of a geometrical model of the evolution of the main MPCs of the channel, which allows to identify the scatterers that lead to the MPCs and greatly contributes to the understanding of the propagation mechanisms in A2G environments. The correctness of the MPC tracking is proven to be higher than 90% and the results show that the model obtained by the GSTM includes more than 95% of the received power. [ABSTRACT FROM AUTHOR]

Frequency-domain equalization (FDE) is a computationally efficient method to recover underwater acoustic single-carrier transmissions in a multipath environment. The FDE receivers often operate in a block-by-block manner, assuming that the channel is time invariant during a block duration. This assumption implies that traditional block-based FDE receivers experience performance degradation in fast time-varying channel conditions, since the interfrequency interference (IFI) is often not considered. Here, we focus on the IFI cancelation (IFIC) in single-carrier transmissions. The IFI formulations are developed for a blockwise FDE receiver, where the time reversal (TR) processing is incorporated to alleviate the interblock interference. Based on the formulations, the IFI is estimated by the time-varying impulse responses, which are obtained from an adaptive basis expansion model. A soft two-step IFIC strategy is then employed to address the IFI. In the first step, the main IFI is removed by the time-varying TR processing and explicit IFI cancelation. The residual IFI is suppressed by the frequency-domain decision feedback equalization (FD-DFE) in the second step. To further enhance the communication performance, we develop a soft-symbol-based iterative receiver, referred to as IFIC-FD-DFE. The impulse responses, explicit IFI, and residual IFI are updated in an iterative fashion, based on the soft symbol estimates. The proposed receiver has been tested using the at-sea measurements collected at the Gulf of Mexico in August 2017. Communication packets at four ranges at the carrier frequency of 160 kHz with a symbol rate of 32 kHz have been decoded. The effectiveness of the IFIC-FD-DFE receiver has been demonstrated via comparisons with several FDE schemes in the current literature. [ABSTRACT FROM AUTHOR]

Due to the decrease in cost, size, and weight, unmanned aerial vehicles (UAVs) are becoming more and more popular for general-purpose civil and commercial applications. Provision of communication services to UAVs both for user data and control messaging by using off-the-shelf terrestrial cellular deployments introduces several technical challenges. In this article, an approach to the air-to-ground channel characterization for low-height UAVs based on an extensive measurement campaign is proposed, giving special attention to the comparison of the results when a typical directional antenna for network deployments is used and when a quasi-omnidirectional one is considered. Channel characteristics, such as path loss, shadow fading, root-mean-square delay, Doppler frequency spreads, and the K-factor, are statistically characterized for different suburban scenarios. [ABSTRACT FROM AUTHOR]

Required throughput for a fronthaul in a cloud-radio access network tremendously increases and becomes a bottleneck in a communication network with wide bandwidth and a large number of antennas. To reduce the fronthaul capacity requirement, more digital function blocks are shifted from a central unit to a distributed unit. In this study, we consider separated delivery of data and pilot signals with different compression rates under time-varying channels to reduce fronthaul burden. By analyzing channel prediction error based on a Kalman filter, an achievable rate for uplink data is derived. The fronthaul rates for data and pilots are derived using rate-distortion theory. Both uplink rate maximization and fronthaul rate minimization are simultaneously designed by considering the data and pilot signal distortions as well as the frame structure. First, the optimization of signal distortion with a fixed pilot interval is investigated. Next, a sub-optimal algorithm to find the pilot interval with the fixed signal distortion is developed. Finally, an iterative algorithm to obtain the sub-optimal solution, which is the joint solution of signal distortion and pilot interval, is proposed. The numerical results demonstrate that the proposed algorithm can provide convergent solutions. [ABSTRACT FROM AUTHOR]

In this paper, a novel channel quality indicator (CQI) prediction scheme for adaptive modulation and coding in high-speed mobility environments is proposed, where the wavenumber spectrum segmentation filters' bank is utilized to improve the estimation accuracy of channel prediction algorithm by enhancing the correlation of wireless channels in each orthogonal subspace. In the proposed method, the channel estimation, channel prediction, and signal-to-noise-rate (SNR) prediction are processed on the orthogonal subspace level, which can significantly decrease the mismatch level of the feedback CQI under the fast time-varying channels. Based on the predicted SNR after subspace signal combination, the effective SNR is obtained and is utilized to calculate the feedback CQI parameter. The autocorrelation function and spatial fading rate are analyzed with different feedback delays, which show that our proposed scheme provides stronger correlation of channels with the order of wavenumber spectrum segmentation filters' bank increasing. The simulation results show that the proposed scheme can achieve better throughput performance even with larger feedback delay in high-speed mobility environments.

It is well-known that time-varying channels can provide time diversity and improve error rate performance compared to time-invariant fading channels. However, exploiting time diversity requires very accurate channel estimates at the receiver. In order to reduce the number of unknown channel coefficients while estimating the time-varying channel, basis expansion models can be used along with long transmission frames that contain multiple orthogonal frequency division multiplexing (OFDM) symbols that experience the channel variation. The design of these OFDM frames need to judiciously incorporate training and data insertions in the transmitted signal while maintaining orthogonality. In this work, we propose an inter channel interference (ICI)-free training model depending on pilot symbols only and provide a corresponding time-varying channel estimation method. This scheme relies on an algorithm to determine the number of OFDM symbols per frame and the number of basis functions per path with minimal information about the Doppler bandwidth. As a performance benchmark, Bayesian Cramér Rao lower bound (CRLB) and the corresponding MSE bound are derived analytically for the proposed training model. Theoretical MSE expressions of the proposed estimation scheme are also derived as well as the MSE expressions in the presence of Doppler frequency mismatch. Simulations exhibit substantial MSE improvement and the corresponding Symbol Error Rate (SER) performances of the low complexity estimation scheme. They also corroborate that, unlike the common results in the literature, an OFDM system can perform better as the Doppler frequency increases with judicious design of training and channel estimation schemes. [ABSTRACT FROM AUTHOR]

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)’s beamforming and the IRS’s reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, a modified PDS scheme is presented to trace the channel dynamic and adjust the beamforming policy against channel uncertainty accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems. [ABSTRACT FROM AUTHOR]

In high-mobility massive multiple-input multiple-output (MIMO) systems, Doppler shifts compensation can be combined with beamforming network to effectively suppress the channel time variation. The key of the beamforming network lies in the optimization of the common configurable amplitudes and phases (CCAP) parameter. In this paper, we reveal more insights of this approach by conducting the in-depth analysis. First, we demonstrate that the CCAP parameter optimizes the beamforming network to reduce channel time variation and approximates in a semi-sinusoidal form. Then, a scaling law between the asymptotic Doppler spread and the number of antennas $M$ is derived, revealing that the asymptotic Doppler spread decreases at a rate of $1/M$. We further prove that the optimal CCAP parameter obtained from Jakes’ channel model can be directly applied to more general cases, while the inverse proportionality between the resulting asymptotic Doppler spread and the number of antennas remains valid. Numerical results confirm the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Fast time-varying channel parameters and large penetration losses for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper, we introduce, for the first time, backscatter technology into HSR wireless communications, which can address these two challenges and yet have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relaying or beamforming. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and demonstrate that it outperforms the existing direct wireless transmission (DWT) scheme. We derive the upper and lower bounds of channel capacity for BAWT and prove that it exceeds that of DWT on certain conditions. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset and channel estimator, and signal detector. We show that BAWT, rather than DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally, simulation results are provided to corroborate the proposed solutions. [ABSTRACT FROM AUTHOR]

Modern wireless systems are increasingly dense and dynamic that makes the channel highly non-stationary, rendering conventional receivers sub-optimal in practice. Predicting the channel characteristics for non-stationary channels has the distinct advantage of pre-conditioning the waveform at the transmitter to match the expected fading profile. The difficulty lies in extracting an accurate model for the channel, especially if the underlying variables are uncorrelated, unobserved and immeasurable. Our work implements this prescience by assimilating the Channel State Information (CSI), obtained as feedback from the receiver, over time and space to adjust the modulation vectors such that the channel impairments are significantly diminished at the receiver, improving the Bit Error Rate (BER). We design a channel recommender, in which an adaptive smoother is used to filter the noise in CSI, while a tensor factorization & completion approach is used to track the ephemeral changes in non-stationary channel statistics by observing the changes in certain measurable parameters. V2X communication is used as an example of non-stationary channels to shows the efficacy of this approach. Overall, the system is shown to operate with a prediction accuracy of 10−3 MSE even in dense scattering environments over space and time, improving the BER at the receiver by 90% for higher-order modulations. [ABSTRACT FROM AUTHOR]

Vehicle‐to‐vulnerable‐road‐user (V2VRU) communications have the ability to provide 360° of awareness to both vehicles and vulnerable road users to prevent accidents. An accurate V2VRU channel model in critical accident scenarios is essential to develop a reliable communications system. Therefore, extensive wideband single‐input and single‐output channel measurement campaigns at 5.2 GHz were carried out in open‐field and urban environments. Accident prone scenarios between a vehicle and a cyclist as well as between a vehicle and a pedestrian are considered. In this study, locations of the scatterers in the propagation environment are estimated. The authors propose a method to extract specular multipath components (MPCs) from the estimated time‐variant channel impulse response based on the density of neighbouring MPCs. The specular MPCs are then tracked using a novel tracking algorithm based on the multipath component distance approach. Each path is then related to a physical scatterer in the propagation environment by employing a joint delay‐Doppler estimation. According to the results, single and double bounce reflections from buildings and parked vehicles are identified in line‐of‐sight (LoS) situation. In non‐LoS(NLoS) situation, scattering from nearby trees as well as reflections from traffic signs and lampposts beneath the trees canopy are identified. [ABSTRACT FROM AUTHOR]

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

We propose novel variable-block-length joint channel estimation and data detection for spatial modulation over time-varying channels, where the data frame is divided into multiple blocks, and channel state information (CSI) is estimated in a block-by-block manner. More specifically, in each block maximum-likelihood-based data detection and channel estimation are iteratively carried out, where the detected data block is used as a long pilot sequence to update CSI. Our simulation results demonstrate that the performance of the proposed scheme is capable of approaching that of a perfect-CSI scenario. Moreover, the proposed scheme outperforms the recent rectangular differential spatial modulation scheme, without any substantial cost in terms of the preamble overhead and detection complexity. [ABSTRACT FROM AUTHOR]