In this paper, we propose a hybrid simultaneous wireless information and power transfer (SWIPT)–enabled multiple-input multiple-output (MIMO) architecture, where the base station (BS) uses a conventional radio-frequency (RF) transmitter for downlink transmission and a Rydberg atomic quantum receiver (RAQR) for receiving uplink signals from Internet of Things (IoT) devices. To fully exploit this integration, we jointly design the transmission scheme and the power-splitting strategy to maximize the weighted sum rate, which leads to a non-convex problem. To address this challenge, we first derive closed-form lower bounds on the uplink achievable rates for maximum ratio combining (MRC) and zero-forcing (ZF), as well as on the downlink rate and harvested energy for maximum ratio transmission (MRT) and ZF precoding. Building upon these bounds, we propose an iterative algorithm relying on the best monomial approximation and geometric programming (GP) to solve the non-convex problem. Finally, simulations validate the tightness of our derived lower bounds and demonstrate the superiority of the proposed algorithm over benchmark schemes. Importantly, by integrating RAQR with SWIPT-enabled MIMO, the BS can reliably detect weak uplink signals from IoT devices powered only by harvested energy, enabling battery-free IoT networks.

Coexistence design that enables the emerging candidate waveforms to interoperate with legacy orthogonal frequency division multiplexing (OFDM) is essential for sixth-generation standardization. This paper focuses on orthogonal time frequency space (OTFS)—a representative waveform for high-mobility scenarios—and proposes a novel coexistence paradigm between OTFS and OFDM, termed cross-domain division multiplexing (XDM), utilizing the different signal representations across multiple domains. XDM is formulated through sparse mapping in the delay-Doppler domain and periodicity-based partial superposition in the time-frequency domain. We prove that XDM holds several favorable invariant properties, which enable tractable interference characterization and suppression even under dynamic channels. We also show that XDM supports flexible numerologies and provide some standardization-oriented practice examples. For practical implementation, XDM transceivers are designed for near interference-free coexistence leveraging the invariant properties. We first develop a cross-domain waveform-level interference cancellation algorithm with ultra-low complexity for single OTFS user coexisting with OFDM. More generally, a domain-transform embedded factor-graph is constructed for multiple OTFS users, over which a cross-domain expectation propagation algorithm is proposed to achieve almost lossless recovery of the coexisting signals. Through a novel variance transfer technique, we prove that the detection errors of coexisted OTFS and OFDM respectively converge to their counterparts under interference-free transmission. Simulations show that XDM exhibits BER loss about 0.5 dB compared to interference-free transmission under various channel conditions and system configurations.

Diverse and complex wireless interference is one of the most critical threats to modern wireless communication systems. The continuous shift of wireless interference toward higher frequency and wider bandwidth imposes sampling-rate limitations on wireless interference identification (WII). Existing compressed sensing-based WII methods designed for undersampling scenarios suffer from high-complexity signal reconstruction and low identification accuracy caused by fixed sampling strategies. To address these challenges, we propose a multi-domain fusion model which employs domain-specific hybrid sampling to extract interference features for multi-attribute WII without signal reconstruction. Given the respective classification advantages of random and uniform undersampling in time-domain sequences and time–frequency images, this paper proposes a dual-branch architecture to exploit their joint benefits. Specifically, we propose a learnable sparse sampler combined with a Transformer to extract time-domain features in the random undersampling branch. We further prove that the Restricted Isometry Property (RIP)-compliant undersampling largely preserves the feature class discriminability, which motivates the introduction of the RIP loss. In parallel, we propose a multi-scale feature extraction module and a cross-fusion module for dimensionality reduction in the uniform undersampling branch. Subsequently, an attribute correlation-driven graph convolution network is introduced to classify the fused features from both branches, further improving WII performance. Finally, we propose an adaptive multi-domain binary cross-entropy loss and prove that the optimal weight of the RIP loss effectively mitigates gradient conflicts. Experimental results show that the proposed model improves precision by 25.1% over the state-of-the-art, while maintaining effective multi-attribute WII performance at undersampling ratios up to 8.

Inter-satellite links (ISLs) are pivotal for information interaction and orbit determination in mega-constellation networks. Terahertz (THz) band, with its abundant spectral resources, exhibits significant potential for high-speed ISL establishment. However, due to frequent operations of Low earth orbit (LEO) satellites, the combined impacts of multi-source timevarying platform vibrations and two-dimensional asymmetrical features of each source on THz inter-satellite communication-ranging systems have yet to be revealed. This paper proposes a vibration-constrained THz-ISL system with LEO satellite attitude dynamics and spatial effects. Specifically, we establish a multi-peak frequency model using Semiconductor Inter-Satellite Link Experiment (SILEX) spectra, complemented by an optimized sum-of-sinusoids (SOS) time-domain model to address high dynamics and multi-source vibrations in LEO scenarios. Statistical link gain models are formulated, encompassing steady, sporadic, and universal scenarios. Subsequently, the proposed ISL gain model is applied to assess communication and ranging performance. We derive the signal-to-noise ratio (SNR)-vibration quantitative relationship through Stirling’s approximation, where SNR quantifies communication performance degradation. Ranging precision is evaluated via the Fisher information matrix (FIM), revealing vibration deterioration of Cram´er-Rao bounds (CRB). Under given power constraints, we define truncation distance as the maximum vibration-tolerant link range where performance remains unaffected, and derive its closed-form solution. Simulations utilizing SILEX orbit data indicate that under typical settings: 1) inter-satellite vibrations are on the order of hundreds of μrad and cause a 5% pointing error attenuation, 2) vibrations alter system performance boundaries, with 600 km communication distance reduction and 2.2 dB ranging accuracy degradation with 0.03° deviation, 3) vibration effects can be mitigated via phased array dimensions and beamforming design, achieving capacity-robustness trade-off.

With great potential in providing global coverage and real-time service, recently, direct satellite-to-device (DS2D) communication has attracted considerable attention. However, how to effectively utilize the costly satellite resources to satisfy the on-demand massive connectivity remains a huge challenge. This paper proposes a cost-effective hetero-granular resource allocation framework that combines the advantages of ground-based scheduling and spaceborne scheduling. In specific, we aim to optimize both the cell-level and user-level scheduling in a beam-hopping system. The formulated optimization problem is first decomposed into a coarse-grained scheduling problem among cells using historical demand information on the ground station, and a fine-grained scheduling problem among users using real-time service demands on satellite. We solve the mixed-integer non-linear programming problem of coarse-grained scheduling with cross-entropy and quantum particle swarm optimization algorithms to find the global optimum, exploiting the adequate ground-based computational resources. The fine-grained scheduling problem is solved with a generalized-benders-decomposition-based algorithm to accommodate the limited spaceborne resources, which decouples power and bandwidth allocation based on a closed-form solution of optimal dual variables in the primal power allocation problem. Simulation results demonstrate that the proposed method effectively reduces the length of the waiting queue by up to 25.05% compared to the existing methods.

The non-linearity of power amplifiers (PAs) is a major challenge in satellite communications and high-frequency bands. Traditionally, discrete fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) uses localized subcarrier mapping, which induces a high peak-to-average power ratio (PAPR) and obstructs the efficient operation of the PA. In this paper, we propose an interleaved mapping based DFT-s-OFDM with data length extension to reduce PAPR. To overcome the unrealistic constraints in interleaved mapping, where the occupied subcarriers must be a divisor of the total, we introduce a trans-formation method based on time-domain waveform invariance. Furthermore, we provide its equivalent frequency-domain matrix representation. The constructed waveform reduces PAPR and simultaneously mitigates inter-carrier interference (ICI) caused by non-linearity, effectively achieving resistance to PA non-linearity. Simulations show that our method achieves a gain of up to 1.6 dB in PAPR and 2.4 dB in block error rate (BLER) compared to localized mapping.

Design of advanced modulations needs to be revisited to cater for the diverse requirements of future 6th Generation (6G). While most existing modulation schemes operate coherently and need pronounced channel-tracking pilots to reach performance limits, noncoherent transmission offers a viable option by avoiding channel estimation. Noting that the noncoherent capacity-achieving input symbols are good sphere packing over Grassmannian field and each symbol is a subspace in Euclidean space, additional information can be embedded by further packing over the subspaces. In this paper, we propose a hybrid high-dimensional modulation scheme that bears both noncoherent and coherent components via multi-branch mapping. The packing problem is formulated into optimizing rotational angles of noncoherent codewords whose closed-form expressions are derived in this paper. A two-stage binary labeling as well as integration method with waveform are proposed to extend performance limits. Correspondingly, a channel-adjustable receiver is proposed to recover the noncoherent and coherent components sequentially. Simulations demonstrate that the proposed method yields Grassmannian most 2.4dB gain compared with pilot-based scheme under suitable coding pair of two branches.

Space-air-ground integrated networks (SAGIN) combine the wide-area coverage of satellites with airborne platforms acting as relays, enabling efficient data delivery for large-scale Internet of Things (IoT) sensing applications. To further enhance transmission efficiency, this paper incorporates airborne onboard processing to reduce communication workloads and proposes an intelligent beam hopping strategy tailored to spatially uneven traffic demands. Specifically, we design a multi-agent deep reinforcement learning (MADRL)-based beam hopping framework, where satellite agents coordinate beam scheduling while considering spatial service heterogeneity and the diverse computational capacities of airborne platforms. To reduce the complexity introduced by individual task requirements, we integrate a summarized statistical profile of the computation tasks into the agent’s observation space, including average and maximum computation efficiencies across tasks. Simulation results demonstrate that the proposed scheme significantly accelerates convergence and reduces the data backlog by up to 80%, especially under scenarios with considerable heterogeneity in service demands and airborne platform computational capabilities.

Precise unmanned aerial vehicle (UAV) localization is crucial for the successful operation of UAV swarms, particularly in environments where global navigation satellite system (GNSS) signals are denied or unavailable. This paper introduces a cooperative positioning framework for such scenarios. Considering the resource and coverage constraints of UAVs, a multi-objective cooperative positioning algorithm using a projection factor graph (FG) is proposed. This algorithm achieves dimensionality reduction and interaction of the positioning message among UAVs, employing low-complexity techniques for parameter estimation. Simulation results demonstrate that the proposed algorithm achieves linear complexity, significantly lower than cooperative benchmarks like SPAWN with cubic. Furthermore, it provides an 82.5% increase in positioning accuracy over non-cooperative methods, yielding accuracy approaching that of the high-complexity SPAWN algorithm.

The simultaneous transmission of numerous users presents substantial challenges due to the inherent trade-off between channel estimation and information transmission in multi-user multiple-input multiple-output (MIMO) system. In this paper, we explore the use of the superimposed pilot (SP) scheme to tackle the large transmitting users, where the number of users may exceed the coherent time. SP scheme incorporates both transmitted data and noise in the channel estimation process, which is significant different from the counterpart of RP scheme. We provide an in-depth analysis of the interaction between interference caused by channel estimation errors and noise. We then derive the explicit expression for the scaling law of the mutual information lower bound (MILB) in relation to the number of users and the levels of transmitted power. Besides, the optimal power allocation between pilots and data transmission is also derived analytically. The analytical results demonstrate that the SP scheme significantly improves performance compared to traditional RP scheme in our consider case. Numerical results are also presented to validate our theoretical derivations.

This paper proposes an efficient backup routing scheme based on potential-minimized path first for Low Earth orbit mega-constellation network to solve the service interruption problem caused by satellite node failure, in which the potential-minimized path first strategy adopts the artificial potential field model to consider the network node congestion level and shortest path delay. This backup routing scheme optimizes both the primary and backup paths by weighting and summing the potentials of both paths. This weighting takes into account congestion and possible broken network nodes. It retains the property of distributed routing using potential functions to simplify complexity and signaling interactions. Simulation results show that the proposed backup routing scheme significantly reduces the outage time and data transmission delay, enhancing the reliability and stability compared to existing backup routing schemes such as intelligent backup multi-path ant colony routing algorithm, redundant multi-path routing algorithm and traffic-load-aware multipath routing algorithm.

Futures are essential instruments in financial markets. Accurately predicting futures volatility is crucial for calculating value-at-risk and comprehensively assessing financial uncertainty. However, the rapid changes in the futures market, the continuous emergence of new commodities, and the close interaction with spot markets create a complex market environment . This results in futures data having intricate characteristics of limited historical data , non-stationary, and non-linear, posing significant challenges for accurately predicting volatility. We propose a futures volatility prediction framework, Meta-Learning with Long Short-Term Transformer (Meta-LSTR) to tackle these challenges. To improve the understanding of market dynamics, we construct a Long-Short Term Transformer network. In conjunction with a de-stationary module and market-side information, the network can effectively capture multi-scale non-stationary features and non-linear temporal dependencies. To enhance the efficiency of limited data utilization, we employ a meta-learning approach to extract common knowledge across different varieties of futures. Comprehensive experiments using Chinese market data highlight the effectiveness of the Meta-LSTR model in futures volatility prediction. Compared to other state-of-the-art methods, the proposed Meta-LSTR model reduces prediction error by over 21.99%.

In MIMO systems, hybrid digital and analog precoding structures are gaining attention for reducing power consumption and costs compared to fully digital precoding. However, deployment expenses and circuit power consumption still pose challenges. Nonideal hardware in practical applications further impacts energy efficiency (EE), making research into high-energy-efficiency MIMO structures under such conditions essential. To tackle this issue, an exceedingly effective hybrid precoding strategy is developed, incorporating the implications of nonideal hardware characteristics. Initially, the research models the aforementioned hardware characteristics, formulates the signal model for the hybrid-structured MIMO communication system, and discerns the nonconvex problem relative to energy efficiency optimization. Subsequently, the optimization problem is effectively resolved by choosing analog radio frequency (RF) precoding and digital baseband precoding designs, including the precoding design for array gain. Furthermore, fractional programming is utilized to achieve digital precoding. An iterative optimization method is utilized to solve this problem. Finally, the mixed precoding scheme is formulated to showcase the system’s energy efficiency performance through simulation and explore the impact of imperfect hardware on the system. Additionally, to further diminish design complexity, the implementation of various channels is analyzed to propose a low-complexity method via the approximation of the equivalent channel matrix. Simulation results show that the performance of this method achieves performance comparable to existing hybrid precoding methods, when the number of antennas is sufficiently large.

Online adaptation to the dynamic channel conditions requires intelligent receivers with online learning ability as well as efficient training samples. In this letter, we propose a new type of reference signal termedlearning reference signal (LRS), which serves as the online training samples for the fast adaptation of the deep neural network (DNN)-aided intelligent receiver. Specifically, we propose a model-agnostic meta-learning (MAML)-based LRS design framework, where the LRS sequence is regarded as the meta parameter and is meta-learned during the offline training. The maximum loss reduction criteria for LRS design is proposed such that the online meta-update based on LRS can maximize the reduction of the symbol error rate (SER). Furthermore, a Matthew effect in gradient-based training of LRS, which causes imbalanced update on different LRS symbols, is identified and then tackled by a novel symbol bundling and multi-stage updating method to ensure convergence. From experiments, we observe that the learned LRS contains both constellation points and non-constellation points, and achieves more than 4dB SER gain compared to using arbitrary constellation points as training samples.

Integrating feeder- and user-links in multi-beam satellite communications significantly enhances system flexibility but requires effective resource allocation to fully realize its potential. Multi-agent deep reinforcement learning (MADRL) has emerged as a scalable solution for beam hopping, by allowing each agent to optimize the transmission parameters for one beam. However, integrating feeder- and user-links introduces complicated dependencies, including resource competition between feeder- and user-links and data-flow coupling between uplinks and downlinks, dramatically deteriorating agent cooperation. To approach the performance limit, this paper introduces a dependency-elimination MADRL framework incorporating model decomposition, link decoupling, and novel agent-level collaboration mechanisms to allocate beams, power, and bandwidth with reduced complexity. Specifically, to facilitate beam-level agent reuse for complexity reduction under the heterogeneity of feeder- and user-links, characterized by data-flow aggregation and division, we decouple bandwidth allocation from the learning model. The uplink-downlink dependencies in the bandwidth allocation is then resolved using a generalized water-filling strategy based on the performance upper bounds. Furthermore, we improve agent cooperation efficiency through state and reward decomposition and a novel non-cooperation penalty. Evaluations show that our method improves the system performance by up to 57.7% compared to sota MADRL methods while reducing training complexity by more than 50%.

Satellite communication faces a great risk of eavesdropping due to the its broadcasting nature and vast coverage. Conventional beamforming-based physical-layer security technology encounters difficulty in preventing eavesdropping within the main-lobe of the satellite, which causes a large insecure area. In this paper, we propose a cooperative multi-beam transmission framework, which elaborately superimposes multiple beams to shrink the intersected coverage area, and exploits the additional design degrees of freedom to enhance the security. The designs of symbol mapping and beamforming are jointly studied at multiple beams to achieve transparent communication in the intersected area while randomizing the signals otherwise. Information-theoretic performance metric is identified to characterize the information leakage. A tight upper bound of the intractable metric is proved using variational methods, and a deep learning-based approximation and optimization scheme is then proposed to efficiently design the multi-beam transmit signals. To further distort the leaked signal, a convergence-guaranteed cross-entropy algorithm is developed for joint antenna subset selection at multiple beams. Simulations show that the proposed scheme can shrink the insecure area by an order of magnitude compared with the conventional methods.

This paper studies the fundamental limits of covert communications covered by randomly activated overt users in both single-frame and multi-frame transmission scenarios. While traditional covert communications mainly consider concealing signal power characteristics, the existence of overt users provides opportunities such that covert communications can be achieved through the confusion between the users. This benefit is first revealed in single-frame transmission scenario. The major obstacle in analyzing performance limits is that the conventional Kullback-Leibler divergence based covertness measurement becomes infinite. To overcome the intractability, a tighter upper bound of the total variation distance (TVD) is then developed using a novel recursive-iterative approximation. On this basis, the collapse effect of the TVD is derived, which shows that the TVD is strictly less than 1 if the covert user sets the transmit power to be an integer multiple of that of the overt users. Then, we find that$\mathcal {O}(N)$-bit information can be transmitted over N channel uses under the above setting, which breaks the well-known square root law. If the above setting is violated, the TVD instantly approaches 1 as$N\rightarrow \infty $, and only$\mathcal {O}(\sqrt {N})$-bit information can be covertly transmitted. To prove this, the detection method of the warden is modified to cope with the random activation of overt users. These conclusions also hold for the transmission with uncertain powers or in fading channels, which resembles realistic wireless transmissions. In multi-frame transmission scenario, however, the access characteristics of overt users can be exposed from a statistical perspective, such that the rate gain disappears and the covert transmission rate drops to$\mathcal {O}(\sqrt {N})$bits per frame. To obtain a positive covert transmission rate, we propose a rate-splitting based covert transmission scheme that introduces an opportunistic access branch to bring randomness, through which the covert user can transmit up to$\mathcal {O}(NL)$-bit information over L frames.

Satellite-aided airborne systems can enable data acquisition in remote areas for the intelligent transportation systems (ITS), leveraging the satellite coverage alongside the mobility and multifunctional capabilities of autonomous aerial vehicles (AAVs). However, due to the harsh environment, ensuring secure and timely task execution is complicated by uncertainties related to both channel conditions and eavesdropping threats. This paper proposes a two-stage optimization method to fully exploit AAVs’ flying, sensing, compressing, and transmitting capabilities for secure data acquisition under dual uncertainties. In the first stage, a deep reinforcement learning strategy is employed to optimize sensing and trajectory planning to explore the eavesdropping environment and balance computational and transmission demands. Building on the sensed information about the eavesdropping environment, the second stage focuses on minimizing task completion time through optimal resource allocation and hierarchical A* path planning, with the channel uncertainty addressed by incorporating an outage probability constraint. Simulations demonstrate that the proposed method can reduce data completion time by 35.3%, validating its effectiveness in uncertain environments.

Assessing the vulnerability of mega-constellation networks (MCNs) to large-scale failure is challenging, due to the time-varying three-dimensional constellation. In this paper, we propose a geometrical method to assess the vulnerability of the MCNs under large-scale geographical failure based on the standard +Gridinter-satellite connectivity pattern, using hop count as the metric. We first equivalently map the original constellation to a two-dimensional flat torus, offering a simplified and time-invariant representation of the geographical failure. The failure’s properties are then revealed on the torus to identify the blockage on the inter-satellite paths that might increase the hop count between end users. Under the blockage, a closed-form hop count expression is then obtained by deriving the explicit expressions of the optimal detours in the scaling limit. Finally, we propose a low-complexity hop count estimation algorithm that achieves a maximum relative error of 1.5% compared with the network simulation while being$10^{5}$times faster. Evaluations using traffic sourced from the 100 most populous cities show that the geographical failure covering the latitude corresponding to the MCNs’ inclination has the greatest impact on the hop count, whereas the MCNs are generally robust even under extreme scenarios.

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$1\sim 4.5$dB bit error rate performance gain compared to the typical D-D domain NOMA.

Space-air-ground integrated network presents a promising solution to the challenge of accommodating large-scale device access while confronting sophisticated interference threats. Existing random access techniques neglect the dynamic interference environment and thus often struggle to realize anti-intelligent interference effectively. This paper proposes a novel approach to address this issue. By employing deep reinforcement learning algorithms, we utilize real-time feedback to adapt to the dynamic environment resulting from the time-varying interference strategy, as well as the involvement of various types of entities. Moreover, we propose a hierarchical reward function to improve the access efficiency. Simulation results show that our method reduces the congestion between users by up to 47% and enhances access efficiency is about 3.2 times compared with random access under malicious jammer intro conclusion finding.

Covert communications studied in prior works typically consider only the average power constraint on the transmit signal. In this paper, we explore the optimal signaling for covert communication under the peak power constraint, in view of the realistic limitation at the transmitter. Our main result is that the rate-optimal transmit signal distribution under the covertness constraint forms finite hyperspheres, on each of which the points distribute uniformly. To prove this, a lemma is first deduced to explore the equivalence between maximizing the achievable rate and minimizing the covertness measured by the Kullback-Leibler divergence. The equivalence property is then exploited to characterize the covert communication as a specific two-user broadcast channel, which simplifies the intractable covertness constraint. After that, by exploiting the spherical symmetry property of Gaussian noise and the identity theorem of holomorphic functions, the main result is derived. Furthermore, the analytical representation of the optimal signaling in low signal-to-noise ratio (SNR) region is studied following Shamai’s approach. For high SNR region, a nonlinear dynamic programming algorithm is developed to generate the optimal signal distribution. For ease of practical implementation, the discrete constellations are optimized based on the sequential quadratic programming algorithm. Simulation results verify the optimality of the proposed hyper-sphere signaling and demonstrate the performance gain of the optimized constellations over typical modulation constellations.

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 densely deployment of satellites enables the realization of direct-to-satellite Internet-of-things system with tremendous terminals through multi-satellite cooperation. Multi-user detection (MUD) based on cooperative satellite network can dramatically increase the detection performance. However, it is chained by the limited number of inter-satellite link (ISL) bandwidth resources. To cope with the stringent constraints on ISLs, we propose a novel auxiliary node (AN)-aided factor graph and the corresponding multi-user detection (MUD) algorithm named auxiliary node-cooperative message passing algorithm (AN-CMPA). Simulation results show that our proposed algorithm achieves only 0.5dB loss with 75% decreased information cost under effectively designed information filter criterion.

The reliability of low Earth orbit (LEO) mega constellation networks (MCNs) under large-scale geographical failure of satellites remains unrevealed. In this paper, we propose an algorithm to assess the connectivity, average latency and hop count by considering topology changes resulting from geographical failure, under different topology management. Numerical simulations are conducted based on the traffics source from end users distributed among the 100 most populous cities. The results show that the MCNs are generally reliable against geographical failure, as a geographical failure with a radius of 3000 km can at most disconnect 8% of the end users, while increasing the average hop count and latency of the remaining users by less than 10%. Also, topology reconfiguration after failure have a greater impact on the hop count than latency.

Online portfolio selection has been actively studied to maximise overall returns by selecting the optimal portfolio weights using online algorithms. However, most work has focused on long-only portfolios, and developing efficient algorithms with loose portfolio constraints remains a challenge. In this letter, the classical online portfolio selection problem is reformulated to allow long/short and margin. For this problem, conventional gradient-based online algorithms face the challenges of high regret and computational complexity due to non-optimal gradients and high-dimensional projections. To tackle this, we propose a novel online algorithm that introduces mirror descent to achieve dimension-free regret in a non-Euclidean space. Specifically, a Bregman divergence is introduced to replace the$\ell _{2}$norm as a valid proximal setup for the problem to achieve uniform gradients and reduce projection computations. Furthermore, a smoothing technique is developed to reduce the variance of the gradients. The evaluation shows that our algorithm achieves low regret bound and computational complexity, which guarantees a 30% advantage over other strategies in Chinese futures market.

In the uplink machine-type communication (MTC) system, a combination of grant-free transmission and non-orthogonal multiple access (NOMA) emerges to reduce the control overhead and transmission latency. In the grant-free scenario, the base station needs to identify the active devices and estimate the channel state information before the data detection. However, due to the lack of a scheduling process, the user activity detection (UAD) and channel estimation (CE) are both challenging, especially when short non-orthogonal preambles are adopted. In this paper, by exploiting the framework of the compressive sensing-based algorithm, we propose a novel deep learning architecture, namely UAD and CE Neural Network (UAD-CE-NN), to effectively solve the joint UAD and CE problem for grant-free NOMA. In the proposed scheme, the user activity and channel state information hidden in the received data signals are also exploited to aid the preamble for higher detection accuracy. Specifically, UAD-CE-NN is composed of two stages: we first build a preamble detection neural network for a tentative UAD-CE; a data detection neural network is then deployed to exploit the data signals. Compared with the conventional schemes, the proposed scheme obtains much higher accuracy for both the UAD and CE, especially when short preamble sequences are employed.

Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly notifying the utilized NOMA signatures causes large signaling cost, blind signature classification naturally becomes a low-cost option. To accomplish signature classification for NOMA, we study both likelihood- and feature-based methods. A likelihood-based method is firstly proposed and showed to be optimal in the asymptotic limit of the observations, despite high computational complexity. While feature-based classification methods promise low complexity, efficient features are non-trivial to be manually designed. To this end, we resort to artificial intelligence (AI) for deep learning-based automatic feature extraction. Specifically, our proposed deep neural network for signature classification, namely DeepClassifier, establishes on the insights gained from the likelihood-based method, which contains two stages to respectively deal with a single observation and aggregate the classification results of an observation sequence. The first stage utilizes an iterative structure where each layer employs a memory-extended network to explicitly exploit the knowledge of signature pool. The second stage incorporates the straight-through channels within a deep recurrent structure to avoid information loss of previous observations. Experiments show that DeepClassifier approaches the optimal likelihood-based method with a reduction of 90% complexity.

Satellite Internet of Things (IoT) is a promising technical trend to tackle the ubiquitous connection requirements for 5G and beyond. In satellite IoT, the conventional random access scheme faces huge interaction overhead caused by the complicated signaling interactions and the large propagation delay of the satellite-ground link. To tackle this problem, we propose a simplified random access scheme. By utilizing the collision tolerance of non-orthogonal multiple access (NOMA) technology, random access and data transmission are integrated to reduce the overall interaction delay. At the transmitter side, the transmitted symbols are spread by the non-orthogonal sequence and the preamble corresponding to the non-orthogonal sequence. At the receiver side, the received signal is iteratively processed based on the minimum mean square error (MMSE) and serial interference cancellation (SIC). The simulation results show that the proposed scheme can reduce the transmission delay without losing the success rate of random access compared with the conventional scheme that only relies on preamble detection.

Deep learning has been exploited to tackle the multi-user detection problem of non-orthogonal multiple access (NOMA), for its high detection accuracy and low computational delay. Existing deep learning algorithms adopt an offline training and online deploying method. When the online system configuration, such as the number of the users, differs from that in offline training, existing deep learning algorithms fail to work due to the mismatch of the output dimensions. In this paper, we propose an online reconfigurable deep learning framework for multi-user detection which can adapt to diversified number of the users. Inspired by the factor graph representation of NOMA, the framework is designed as the composition of several interlinked deep neural network branches where each branch is dedicated for the detection of a single user. The connections among the branches are configurable to achieve online dynamic extension or clipping so as to match the varying number of NOMA users. Experiments validate the online reconfigurability and the performance gain of the proposed deep learning framework.

Grant-free Non-Orthogonal Multiple Access (NOMA) techniques are able to reduce the signaling overhead and the transmission latency in multi-user communications system. However, most of the existing code-domain grant-free NOMA schemes reuse the spreading signatures designed for the grant-based scenarios. Considering the sparsity and randomness nature of user activities in the uplink transmissions, we propose a deep learning-based signature design, where the non-equal user activation probabilities are exploited to optimize the code-domain NOMA signature. In addition, the conventional grant-free NOMA signatures are not specifically designed over finite Galois field, which hinders the implementation of the encoder/decoder using practical hardware. To address these challenges, we utilize the quantized deep learning framework for the NOMA signature training, which jointly optimizes the sequence generation and the quantization. The numerical results reveal that the obtained signatures outperform the conventional ones especially when the users has unequal activation probabilities.

Non-orthogonal multiple access (NOMA) will provide massive connectivity for future Internet of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach the performance limit with only conventional communication-theoretic tools. In this paper, we resort to deep multi-task learning for end-to-end optimization of NOMA, by regarding the overlapped transmissions as multiple distinctive but correlated learning tasks. First of all, we establish a unified multi-task deep neural network (DNN) framework for NOMA, namely DeepNOMA, which consists of a channel module, a multiple access signature mapping module, namely DeepMAS, and a multi-user detection module, namely DeepMUD. DeepMAS and DeepMUD are automatically trained in a data-driven fashion, and a multi-task balancing technique is then proposed to guarantee fairness among tasks as well as to avoid local optima. To further exploit the benefits of communication-domain expertise, we introduce constellation shape prior and inter-task interference cancellation structure into DeepMAS and DeepMUD, respectively. These sophisticated designs help to reduce the implementation complexity without sacrificing DNN's universal function approximation property, which makes DeepNOMA a universal transceiver optimization approach. Detailed experiments and link-level simulations show that higher transmission accuracy and lower computational complexity can be simultaneously achieved by DeepNOMA under various channel models, compared with state-of-the-art.

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.

Massive connectivity is one of the most challenging issues for Internet of Things (IoT) to achieve the quality of service provisions required by the numerous IoT devices. Non-orthogonal multiple access (NOMA) technology, where multiple users multiplex on the same radio resources, is a promising candidate for next generation wireless networks (the 5th Generation, 5G) and has been expected to meet the requirements of high spectral efficiency and massive connections of 5G mobile communication systems. However, conventional downlink NOMA simply superimposes several single-user constellations, which does not consider the interactions between multiple data streams. This paper proposes a novel deep learning-aided downlink NOMA scheme by parameterizing the bit-to-symbol mapping and multi-user detection with deep neural networks (DNN). The network is trained in an end-to-end fashion with synthetic data, and then the trained bit-to-symbol mapping is extracted to derive the multi-user constellation for downlink NOMA. Simulation results demonstrate that, with the proposed constellations, our scheme achieves significantly lower symbol error rate than conventional downlink NOMA.

Wireless sensor network (WSN) is a key enabling technology for Internet of Things (IoT), where the sensed data reported by the distributed sensors are transmitted to a core node for intelligent computation and decision. However, the isolation between wireless communication and computing leads to a waste of radio resources, since not all sensed data are required for making a precise enough decision. Hence, we propose a wireless neural network (WNN) to integrate the neural computing and wireless communication by exploiting the superposition characteristics of radio channels as well as the reciprocity between deep artificial neural network and multi-tier WSN. The learning ability of WNN is further enhanced by introducing multi-carrier transmission where the transmit gain of each sub-carrier can be freely trained to increase the number of adjustable network parameters. Experiments on some datasets demonstrate that, similar decision accuracy can be achieved compared with the conventional isolated method, while the radio resource consumption can be greatly reduced due to superposition transmissions.

Tactile Internet of Things (IoT) requires ultraresponsive and ultrareliable connections for massive IoT devices. As a promising enabler of tactile IoT, grant-free nonorthogonal multiple access (NOMA) exploits the joint benefit of grant-free access and nonorthogonal 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 multiloss 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.

Enabling nonorthogonal multiple access (NOMA) in device-to-device (D2D) communications under the millimeter wave (mmWave) multiple-input multiple-output (MIMO) cellular network is of critical importance for 5G wireless systems to support low latency, high reliability, and high throughput radio access. In this paper, the closed-form expressions for the outage probability and the ergodic capacity in downlink MIMO-NOMA mmWave cellular network with D2D communications are considered, which indicates that NOMA outperforms TDMA. The influencing factors of performance, such as transmission power and antenna number, are also analyzed. It is found that higher transmission power and more antennas in the base station can decrease the outage probability and enhance the ergodic capacity of NOMA.

Owing to the superior performance in spectral efficiency, connectivity, and flexibility, nonorthogonal multiple access (NOMA) is recognized as the promising access protocol and is now undergoing the standardization process in 5G. Specifically, dozens of NOMA schemes have been proposed and discussed as the candidate multiple access technologies for the future radio access networks. This paper aims to make a comprehensive overview about the promising NOMA schemes. First of all, we analyze the state‐of‐the‐art NOMA schemes by comparing the operations applied at the transmitter. Typical multiuser detection algorithms corresponding to these NOMA schemes are then introduced. Next, we focus on grant‐free NOMA, which incorporates the NOMA techniques with uplink uncoordinated access and is expected to address the massive connectivity requirement of 5G. We present the motivation of applying grant‐free NOMA, as well as the typical grant‐free NOMA schemes and the detection techniques. In addition, this paper discusses the implementation issues of NOMA for practical deployment. Finally, we envision the future research challenges deduced from the recently proposed NOMA technologies.

Grant‐free transmission, which simplifies the signaling procedure via uplink instant transmission, has been recognized as a promising multiple access protocol to address the massive connectivity and low latency requirements for future machine type communications. The major drawback of grant‐free transmission is that the contaminations among uncoordinated transmissions can reduce the data throughput and deteriorate the outage performance. In this paper, we propose a rate‐adaptive multiple access (RAMA) scheme to tackle the collision problems caused by the grant‐free transmission. Different from the conventional grant‐free (conv‐GF) scheme which transmits a single signal layer, RAMA transmits the signals with a multilayered structure, where different layers exhibit unequal protection property. At the receiver, the intra‐ and interuser successive interference cancellation (SIC) receiving algorithm is employed to detect multiple data streams. In RAMA, the users can achieve rate adaptation without the prior knowledge of the channel conditions, since the layers with high protection property can be successfully recovered when the interference is severe, while other layers can take advantage of the channel when the interference is less significant. Besides, RAMA also facilitates the SIC receiving since the multiple layers in the transmission signals can provide more opportunities for interference cancellation. To evaluate the system performance, we analyze the exact expressions of the throughout and the outage probability of both conv‐GF and RAMA. Finally, theoretical analysis and simulation results validate that the proposed RAMA scheme can simultaneously achieve higher average throughput and lower outage performance than conv‐GF. Meanwhile, RAMA shows its robustness with large user activation probability, where the collisions among users are severe.

Massive Machine Type Communication (mMTC) is one key usage scenario in future 5G. To fulfill the massive connection requirement in mMTC, and reduce the signaling overhead, a novel grant free Random Non-Orthogonal Multiple Access (RNOMA) scheme is proposed in this paper, inspired by the concept of conventional NOMA. In RNOMA, one physical resource area is reserved and divided into orthogonal resource units (RU), where each user is allowed to persuade-randomly transmit the same packet on each RU according to optimized probability. At the receiver, inter- and intra-RU Successive Interference Cancelation (SIC) is applied for multi-user receiving. A frame structure is also designed to enable RNOMA, where signaling overhead on informing each UE with assigned resources is reduced. Simulation results show, the proposed method is robust and is able to achieve significantly better performance than novel random access method, i.e. irregular repetition slotted aloha, where its access degree distribution is optimized for binary erasure channel.