Low Earth orbit (LEO) satellites is regarded as a promising solution for supporting Non-Terrestrial Network (NTN); for instance, third Generation Partnership Project (3GPP) includes LEO satelites in 5G New Radio (NR). In the LEO-enabling NTN scenario, a distinct issue is the receivers’ impairments caused by different sources of phase variations, mainly phase noise (PN) and Doppler effects. Current compensation methods are challenging to implement in Orthogonal Frequency-Division Multiple-Access (OFDMA) systems due to the high overhead of pilots required. This paper proposes a technique that can work with very low overhead of reference signals and jointly compensate the effects of PN and residual Carrier Frequency Offset. Key factors to achieve effective compensation are also identified, and significant improvement in system performance is demonstrated in simulations.

The low-error-rate requirement of ultra-reliable communication poses a significant challenge to simulation-based performance evaluation of such systems, as conventional Monte Carlo (MC) simulation becomes extremely time-consuming at very low error rates. This work considers fast simulation of multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) transmission over multipath fading channels via importance sampling (IS). The efficiency of an IS simulator hinges on several key factors: 1) how sharply it can differentiate error-causing and non-error-causing subsets in the sample space, 2) how readily it can profile the probability distribution regarding these subsets, and 3) how proficiently it can generate and distribute samples over particular subsets of interest. In this perspective, we develop a novel IS method in the vein known as histogram-shaping Monte Carlo (HSMC). First, an error gauging function that is generally applicable to channel-coded MIMO-OFDM systems is proposed to highlight potentially error-causing samples in multipath fading channels. Second, we develop a sampler that not only generates random samples effectively from error-causing subsets but also facilitates fast estimation of subset-associated probabilities. New Radio (NR) link-level simulation is conducted to showcase the advantages of the proposed method, revealing approximately 10- to$10^{7}$-fold speedup compared to conventional MC simulation.

Utilizing Deep Reinforcement Learning (DRL) for Reconfigurable Intelligent Surface (RIS) assisted wireless communication has been extensively researched. However, existing DRL methods either act as a simple optimizer or only solve problems with concurrent Channel State Information (CSI) represented in the training data set. Consequently, solutions for RIS-assisted wireless communication systems under time-varying environments are relatively unexplored. However, communication problems should be considered with realistic assumptions; for instance, in scenarios where the channel is time-varying, the policy obtained by reinforcement learning should be applicable for situations where CSI is not well represented in the training data set. In this paper, we apply Meta-Reinforcement Learning (MRL) to the joint optimization problem of active beamforming at the Base Station (BS) and phase shift at the RIS, motivated by MRL's ability to extend the DRL concept of solving one Markov Decision Problem (MDP) to multiple MDPs. We provide simulation results to compare the average sum rate of the proposed approach with those of selected forerunners in the literature. Our approach improves the sum rate by more than 60% under time-varying CSI assumption while maintaining the advantages of typical DRL-based solutions. Our study's results emphasize the possibility of utilizing MRL-based designs in RIS-assisted wireless communication systems while considering realistic environment assumptions.

To estimate the probability Peof an error event to an accuracy in variance not exceeding ${ \in ^2}P_e^2$, conventional Monte Carlo (MC) requires approximately 1/(ε2Pe) simulation runs. Hence the low error probabilities demanded for ultra-reliable communication pose a significant issue in simulation-based evaluation of transmission performance. Advanced fast simulation techniques are therefore coveted. Conceptually, we may view a sequence of symbols transmitted by a communication system as a point in a multidimensional space. Channel distortion and noise may cause displacement of the point. The receiver commits an error if it fails to recover the transmitted point location from the received. The efficiency of a simulator depends on how sharply it can differentiate error-causing and non-error-causing displacements and how simply it can determine the probability of encountering an error-causing displacement. We consider convolutionally coded communication in which the receiver employs Viterbi decoding. We propose a corresponding noise gauging function (NGF) which can make relatively sharp differentiation between error-causing and non-error-causing noise vectors. We also propose a way to estimate the distribution of the NGF values to support error rate evaluation. The result is employed in an MC-type technique that adaptively shapes the histogram of the NGF values of the generated noise vector samples towards one that minimizes the variance in Peestimation under given number of simulation runs. Simulation considering the additive white Gaussian noise channel shows an approximately one to two orders of magnitude in speed-up compared to conventional MC.

Cooperative communication can yield spatial diversity gains, but its performance can also suffer severely from the multiple carrier frequency offsets (MCFOs) induced by high-speed relative motion, in addition to Doppler spread. In orthogonal frequency-division multiplexing (OFDM) systems, such performance degradation arises as a result of intercarrier interference (ICI). We have shown previously that, in conventional non-cooperative communication, certain ICI components (called residual ICI) display high normalized autocorrelation and a partial whitening of the residual ICI can significantly benefit OFDM signal detection. In this work, we show that similar whitening can also be employed in cooperative communication to address the MCFO issue mentioned above. As an illustration, we simulate a distributed spatial-frequency block coding (SFBC) OFDM system subject to highly disparate MCFOs, where in the receiver we perform partial whitening of the residual ICI followed by maximum-likelihood sequence estimation (MLSE). The results show that the proposed method can comfortably accommodate a maximum MCFO span well exceeding one subcarrier spacing.

Synchronization is an important issue arises when distributed multiple transmitters are deployed. Unlike conventional MIMO systems, cooperative transmitters may distribute geographically instead of co-locating at the same spot. Therefore, it is hard for the destination node to compensate multiple carrier frequency offsets (MCFOs) even if the cooperative transmitters use the same clock, and the conventional MIMO decoding strategy may not be suitable. In this paper, we consider cooperative SFBC-OFDM transmission with MCFOs. The proposed strategy carefully combines two sets of separately synchronized signal to mitigate inter-carrier interference (ICI). Maximum-likelihood sequence estimation (MLSE) and iterative interference cancellation is further deployed to improve performance. Simulation results show the diversity gain is maintained and an improved tolerance range of MCFOs is achieved.

Relays can potentially enhance the transmission performance of multi-input multi-output (MIMO) systems. A parallel single-antenna relay network has additional advantages in flexibility, diversity and cost, but also poses significant design problems because the absence of inter-antenna connections over different relays makes the underlying mathematical problems much more difficult to solve. In this study, the authors consider the design of parallel amplify-and-forward relay networks. More specifically, the authors consider the design of relay gains to maximise the system capacity. As no closed-form analytic solution can be found, the authors first develop an iterative algorithm to find a locally optimal solution. Since algorithmic optimisation provides little insight into the analytical properties of the solution, they also attempt analytical solutions for several asymptotic noise conditions. It turns out that the solutions involve some methods to select the optimal subsets of relays for signal forwarding. The authors analyse the resulting capacity outage diversity orders and confirm the analysis with simulation results.

Orthogonal frequency-division multiplexing (OFDM) is a popular broadband wireless transmission technique, but its performance can suffer severely from the intercarrier interference (ICI) induced by fast channel variation arising from high-speed motion. Existing ICI countermeasures usually address a few dominant ICI terms only and treat the residual similar to white noise. We show that the residual ICI has high normalized autocorrelation and that this normalized autocorrelation is insensitive to the multipath channel profile as well as a variety of other system and channel conditions. Consequently, the residual ICI plus noise can be whitened in a nearly channel-independent manner, leading to significantly improved detection performance. Simulation results confirm the theoretical analysis. In particular, they show that the proposed technique can significantly lower the ICI-induced error floor in maximum-likelihood sequence estimation (MLSE) designed to address a few dominant ICI terms.

In this paper, a bit-interleaved coded modulation (BICM) scheme with an iterative receiver is proposed for asynchronous cooperative communications. In such environments, synchronous errors may be severe because relays experience different environments. Rather than treating synchronous errors as impairments, this study focuses on exploiting possible benefits of them, particularly of the possible multiple carrier frequency offsets (MCFOs). With the proposed scheme, potential space diversity of multiple relays with MCFOs is manifested as time diversity, which in turn is harvested through the carefully-designed iterative receiver. The system achieves improved diversity gain and is extremely robust even when all asynchronous parameters, including multiple Doppler effects, are considered.

In this paper, a new space-frequency combination technique is proposed for Alamouti coded Orthogonal Frequency Division Multiplexing (OFDM) in the context of cooperative communications. Since cooperative antennas are distributed, there may exist multiple carrier frequency offsets (MCFOs) which cause problems for conventional space-frequency decoding. The proposed algorithm, taking cues from existing MCFO-compensating algorithms [13][19], combines two sets of separately synchronized signal to mitigate inter-carrier interference (ICI). Iterative interference cancellation and a maximum-ratio-combining-like technique are also deployed to further improve performance with low computational complexity. It is observed that the proposed method achieves better bits error rate (BER) performance and has a superior tolerance of multiple CFOs, compared to existing methods.

We consider the design of a multi-input multi-output (MIMO) transmission system equipped with distributed, single-antenna amplify-and-forward relays. In particular, we consider how to optimize the complex forwarding coefficients of the relays to maximize the system capacity. Existing studies on MIMO relay network designs mostly concentrate on the case of single relay with multiple antennas, whose results are not applicable to distributed networks. We first analyze how the system capacity relates to the relay transmission powers and the channel coefficients. An upper bound on the asymptotic capacity at high relay powers is derived. Aided by the capacity results, we consider the optimization of relay coefficients under two noise conditions: that where the destination noise dominates the total noise and that where the relay noise dominates the total noise. It turns out that no simple analytic solutions can be found for all relay network sizes. Therefore, we propose suboptimal solutions that involve computation of optimal relay coefficients with optimal selection of relays for network sizes that can be solved analytically. The results on diversity order are found to show resemblance to that for single-hop MIMO systems employing antenna selection. Simulation results also verify the superior performance of the proposed technique.

Orthogonal frequency-division multiplexing (OFDM) transmission in time-varying channels suffers from intercarrier interference (ICI). Existing ICI countermeasures usually address a few dominant ICI terms only and treat the residual as similar to white noise. We show that the residual ICI has high normalized autocorrelation and that this normalized autocorrelation is insensitive to the maximum Doppler frequency and the multipath channel profile, among other things. The residual ICI can thus be whitened in a largely channel-independent manner, leading to significantly improved detection performance. Simulation results confirm the theoretical analysis. In particular, they show that the proposed technique can lower the ICI-induced error floor by several orders of magnitude in maximum-likelihood sequence estimation (MLSE) designed to address a few dominant ICI terms.

A novel data-aided OFDM symbol timing synchronization method was proposed to reduce the computational cost and achieve more robust performance. The algorithm uses histograms to process the received signal's phase information for estimating symbol timing delay. In this paper, the histogram- based algorithm is extended further to cover MIMO-OFDM systems. In addition, detailed analysis is carried out to unveil the unique characteristics of the algorithm, in contrast to popular synchronization methods based on time-domain correlations. In particular, the algorithm converges on the mass center of a channel's power profile as the channel delay estimation. Good balance between performance and cost makes the algorithm a very attractive algorithm of choice for symbol timing synchronization.

In conventional full duplex wireline systems digital echo cancellers are commonly used to suppress echo. For applications with asymmetric rates the complexity and performance of the echo canceller depend on the implementation of the rate matching function. When the transmit rate is lower than the receive rate, the traditional approach of resampling before filtering is inefficient. We show that efficient implementation can be obtained by reversing the order of resampling and filtering. We propose two new echo canceller structures based on scalar and vector error signals and develop associated adaptive algorithms.

Quadratic time varying-spectral analysis methods that achieve a high resolution jointly in time and frequency generally suffer from interference terms that obscure the true location of the auto components in the resulting time-frequency representation. Unfortunately, there is no general mathematical model available for an exact distinction between cross-terms and auto-terms. Consequently an attempt to suppress interference can only rely on a few qualitative properties which are commonly associated with cross terms. Most of the reduced interference distributions that have been developed so far exploit the fact that cross terms tend to oscillate and can hence be suppressed by a properly chosen two-dimensional low pass filter. Besides the fact that cross-terms oscillate, they are also known to be responsible for all negative density values of a time-frequency distribution. None of the currently existing methods addresses this characteristic. In this paper we introduce an entirely new approach that achieves a significant interference reduction by specifically exploiting the negative density structure of cross-terms.

The Renyi uncertainty measure has been proposed to be a measurement of complexity of signals. We further suggest that it can be used to evaluate the performance of different time-frequency distributions (TFDs). We provide two schemes of normalization in calculating the Renyi uncertainty measure. For the first one, TFDs are normalized by their energy, while for the second one, normalized with their volume. The behavior of the Renyi uncertainty measure under several situations is studied. A signal-dependent algorithm is developed to achieve TFDs with a minimal uncertainty measure. For the first normalization scheme, the Wigner distribution is found to be optimal or near-to-optimal under certain constraints. If the second scheme is used, our program can generate minimum uncertainty product kernels which are very effective at suppressing cross terms and maintaining high resolution.