Tactile Internet of Things (IoT) requires ultra-responsive and ultra-reliable connections for massive IoT devices. As a promising enabler of Tactile IoT, grant-free non-orthogonal multiple access (NOMA) exploits the joint benefit of grant-free access and non-orthogonal transmissions to achieve low latency massive access. However, it suffers from the reduced reliability caused by random interference. Hence, we formulate a variational optimization problem to improve the reliability of grant-free NOMA. Due to the intractability of this problem, we resort to deep learning by parameterizing the intractable variational function with a specially designed deep neural network, which incorporates random user activation and symbol spreading. The network is trained according to a novel multi-loss function where a confidence penalty based on the user activation probability is considered. The spreading signatures are automatically generated while training, which matches the highly automatic applications in Tactile IoT. The significant reliability gain of our scheme is validated by simulations.

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

Non-orthogonal multiple access (NOMA) with successive detection receivers, e.g., successive interference cancellation (SIC), is a potential technology for satellite multi-user communication due to its lower complexity. However, within a spot beam, multi-user interference (MUI) is complicated by channel-induced time-frequency offset, while the path loss differences that the receiver relies on for MUI suppression almost disappear. To overcome the above obstacle, this paper exploits the delay-Doppler (D-D) domain circular shifting property under time-frequency offsets and proposes a D-D domain spectral shaping multiple access (SSMA) technique. By analyzing the influence of D-D domain spectrum on channel capacity, we identify that an enlarged inter-user power gap can be derived at the receiver by constructing a non-uniform D-D domain spectrum. Inspired by this, a D-D domain multi-branch structure-based shaping framework is proposed to flexibly construct the user-consistent power envelope. Meanwhile, two additional signal designs are introduced to ensure that the D-D domain information density and constellation fit to the constructed power envelope. First, by adjusting the transmission rate of the signal on each branch, we optimize the information density with a non-uniform pattern. Second, by introducing a branch-wise phase rotation and deploying an iterative variational approximation method, the shape of the composite constellation is reconstructed. In addition, we also design a branch-bundling-based successive detection receiver using an alternating direction method of multipliers. This receiver can flexibly combine detectable branch signals while maintaining the complexity close to the traditional SIC receiver. Analysis and simulation results reveal that the proposed D-D domain SSMA has a higher achievable rate and can provide 1 ~ 4.5 dB bit error rate performance gain compared to the typical D-D domain NOMA.

We consider the cooperative detection for distributed sensing via low Earth orbit constellations. The source data gathered by the ground sensors are non-orthogonally sent to satellite access points, and then aggregated at a central satellite via inter-satellite links (ISLs). To overcome the intrinsic ISL bandwidth limitation, we consider the deep auto-encoding paradigm to jointly design the ISL transceivers among satellites, and propose a novel deep variational information bottleneck (DVIB) method which maximizes the end-to-end sensing accuracy under bandwidth constraints. Specifically, the mathematically untractable ISL bandwidth constraint is first transformed into an entropy-based format. Then a customized batch-norm layer is introduced, where the messages on ISLs are considered as latent variables and are regularized with entropy-constrained posterior for efficient compression. Compared to the benchmark, the proposed DVIB method is shown to simultaneously reduce the bandwidth overhead by $30\%$ and enhance the sensing accuracy by 2-5 dB, validating the significance of relevant information extraction on ISLs.