Direct smartphone-to-satellite connectivity is currently under investigation in both 5G Advanced and 6G to extend the support of basic communication services to the most remote geographical areas. While current satellite connectivity systems are focused on offering a global service for emergency and text messaging, their support for other extended data services, such as multimedia services, remains limited due to the challenging wireless conditions of non-terrestrial networks (NTN). These limitations stem from the significantly longer propagation distances between the device and the satellite resulting in poor link budgets and high latencies. To address these challenges and improve the reliability of data transmission in satellite networks, leveraging repeated transmissions of transport blocks with both low-rate modulation schemes and incremental redundancy can be key. In this paper, the focus is on voice services. Particularly the problem of optimally selecting the modulation coding scheme and the number of scheduled repetitions to maximize the probability of successful voice packet reception within a fixed end-to-end latency. The feasibility of supporting voice calls over 5G-NTN under different practical conditions is then evaluated for the optimized scheduler. Link-level simulations are performed based on a 3GPP 5G-NTN model, integrating 5G NR waveforms, LDPC coding, and HARQ with incremental redundancy. Numerical results suggest that high-quality voice calls can be supported in 5G-NTN for a wide range of altitudes and elevation angles, even at 2,000 km satellite altitude, which is the highest altitude considered for LEO satellites. Moreover, numerical results indicate that HARQ retransmissions are unnecessary for supporting voice transmission, as proactively scheduling a large number of repetitions is sufficient. However, using both scheduled repetitions and HARQ retransmissions is more efficient in terms of radio resources and power consumption.

We consider the problem of non-coherent over-the-air computation (AirComp), where$n$devices carry highdimensional data vectors$\mathrm{x}_{i} \in \mathbb{R}^{d}$of sparsity$\left\vert\mathrm{x}_{i}\right\vert_{0} \leq k$and the sum of these data vectors has to be computed at a receiver. Previous results on non-coherent AirComp require more than$d$channel uses to compute functions of$\mathrm{x}_{i}$, where the extra redundancy is used to combat non-coherent signal aggregation. However, if the data vectors are sparse, sparsity can be exploited to offer significantly cheaper communication. In this paper, we propose to use random transforms to transmit lower-dimensional projections$s_{i} \in \mathbb{R}^{T}$of the data vectors. These projected vectors are communicated to the receiver using a majority vote (MV)AirComp scheme, which estimates the bit-vector corresponding to the signs of the aggregated projections, i.e.,$\mathbf{y}=\text{sign}\left(\sum_{i} \mathbf{s}_{i}\right)$. By leveraging 1-bit compressed sensing (1bCS) at the receiver, the real-valued and high-dimensional aggregate$\sum_{i} \mathrm{x}_{i}$can be recovered from$y$. We prove analytically that the proposed MVCS scheme estimates the aggregate data vector$\sum_{i} \mathrm{x}_{i}$with$\ell_{2}$-norm error$\epsilon$in$T=\mathcal{O}\left(k n \log (d) / \epsilon^{2}\right)$channel uses. We consider distributed histogram estimation, a canonical building block for federated analytics, as an aplication for MVCS where the data vectors$\mathrm{x}_{i}$are inherently 1 -sparse. Our numerical evaluations demonstrate that our scheme achieves the same order of communication cost as state-of-the-art methods while avoiding the complexity and overhead of additional cryptographic tools.

This paper investigates over-the-air computation (AirComp) over multiple-access time-varying channels, where devices with high mobility transmit their sensing data to a fusion center (FC) for averaging. To combat the Doppler shift induced by time-varying channels, each device adopts orthogonal time frequency space (OTFS) modulation. Our objective is minimizing the mean squared error (MSE) for the target function estimation. Due to the multipath time-varying channels, the OTFS-based AirComp not only suffers from noise but also interference. Specifically, we propose three schemes, namely S1, S2, and S3, for the target function estimation. S1 directly estimates the target function under the impacts of noise and interference. S2 mitigates the interference by introducing a zero padding-assisted OTFS. In S3, we propose an iterative algorithm to estimate the function in a matrix form. In the numerical results, we evaluate the performance of S1, S2, and S3 from the perspectives of MSE and computational complexity, and compare them with benchmarks. Specifically, compared to benchmarks, S3 outperforms them with a significantly lower MSE but incurs a higher computational complexity. In contrast, S2 demonstrates a reduction in both MSE and computational complexity. Lastly, S1 shows superior error performance at small SNR and reduced computational complexity.

We consider the problem of secure histogram es-timation, where$n$users hold private items xifrom a size-d domain and a server aims to estimate the histogram of the user items. Previous results utilizing orthogonal communication schemes have shown that this problem can be solved securely with a total communication cost of O(n2log(d)) bits by hiding each item xiwith a mask. In this paper, we offer a different approach to achieving secure aggregation. Instead of masking the data, our scheme protects individuals by aggregating their messages via a multiple-access channel. A naive communication scheme over the multiple-access channel requires$d$channel uses, which is generally worse than the O(n21og(d)) bits communication cost of the prior art in the most relevant regime$d$>>$n$. Instead, we propose a new scheme that we call Over-the-Air Group Testing (AirG T) which uses group testing codes to solve the histogram estimation problem in O(n log(d)) channel uses. AirGT reconstructs the histogram exactly with a vanishing probability of error Perror= O(d-T) that drops exponentially in the number of channel uses$T$.

Motivated by the increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL accuracy over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Numerical results demonstrate that the power control scheme offers significant reductions in mean squared error. Additionally, we provide simulation results on MNIST classification with a deep neural network that reveals significant improvements in classification accuracy for low-SNR scenarios.

Over-the-air computation (AirComp) has recently emerged as an efficient analog method for data acquisition from wireless sensor devices. In essence, AirComp exploits the signal superposition property of a multiple access channel to estimate functions of the transmitted data points. Unless devices are excluded from participation, state-of-the-art AirComp methods do not achieve unbiased function computation, thereby introducing systematic errors in the acquired function. In this paper, we propose a new AirComp scheme that employs retransmissions to achieve probabilistically unbiased function computation. We solve a power control problem that minimizes the bias subject to a peak transmission power constraint. We show that the optimal power control follows a greedy structure that maximizes the devices' contribution to the received function at every retransmission. Numerical results show that the proposed scheme can achieve unbiased function computation with a few retransmissions and drastically reduce the mean squared error in the function estimation compared to the current state-of-the-art.