Low Earth orbit (LEO) satellite networks are expected to support heterogeneous services, including enhanced mobile broadband (eMBB) communications, massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). However, existing coexistence schemes, such as puncturing and superposition, struggle to achieve an effective trade-off among reliability, latency, and spectral efficiency due to their limited degrees of freedom (DoF). To address this challenge, we propose a novel rate-splitting grant-free (RS-GF) transmission scheme that integrates rate-splitting multiple access (RSMA) with grant-free random access (GF-RA) to efficiently support heterogeneous quality of service (QoS) requirements. The high-rate eMBB user employs single-layer rate splitting (RS) over the entire slot, while short-packet Internet-of-Things (IoT) devices associated with URLLC and mMTC adopt GF-RA via single mini-slot transmissions. Building on this RS-GF framework, we analyze the outage performance of the proposed scheme. Specifically, we derive the average packet error probability (PEP) of IoT devices in the finite blocklength (FBL) regime and analyze the eMBB user’s outage probability under imperfect successive interference cancellation (SIC) and mini-slot collisions. On this basis, we present simplified analytical solutions for sparse and dense IoT deployment scenarios, and Monte Carlo simulations validate our analytical derivations. Simulation results demonstrate that the proposed RS-GF scheme outperforms state-of-the-art solutions for service coexistence in LEO satellite networks.

Grant-free random access (GF-RA) has recently emerged to support massive random access. Due to the absence of access grant in GF-RA, the base station (BS) has to first identify each active user. However, the state-of-the-art user-activity detection (UAD) solution, i.e., covariance-based maximum-likelihood detection (CB-MLD) is still subject to some critical deficiencies. Specifically, the update step size in each CB-MLD iteration relies on an asymptotically large antenna number, which may cause convergence issues in practice. In addition, the hard-decision threshold for UAD remains to be fine-tuned in complicated scenarios. Both deficiencies are hard to address via analytical methods. Thus, we propose a hybrid-driven UAD network (HyD-UADNet), where a model-driven network is constructed to learn the proper update step size, and a data-driven network is designed to learn the soft decision on user activity. Furthermore, we construct a dynamic configuration-adaptive mixture-of-expert network (CA-MoENet). This CA-MoENet can adaptively produce weighting coefficients for different expert HyD-UADNets, so as to enhance the UAD robustness against varying configurations. Finally, simulations show the superior UAD accuracy of the HyD-UADNet, and reveal the robustness of the CA-MoENet even if the testing configuration is never seen by any expert during training.

An enhanced on-off division multiple access (ODMA) transmission scheme is introduced for unsourced multiple access networks. Building upon the foundational ODMA transmission scheme, we have implemented further refinements to the original joint on-off pattern and data detection algorithm. Specifically, we propose a pattern collision resolution technique that can blindly recognize the collision degree of each on-off pattern, and then iteratively recover the data of collided users over a joint factor graph. Furthermore, we introduce a finite-length performance analysis for on-off pattern detection and iterative multi-user decoding. Through this analysis, we derive numerous numerical results, revealing the impact of various parameters on the performance of collision degree detection and multi-user decoding, respectively. By summarizing the rules observed from these numerical results, we formulate design strategies for these parameters, aiming to optimize the overall performance of our scheme. The inherent super sparse property of ODMA ensures that our scheme maintains low decoding complexity. Numerical results demonstrate that, with the implementation of our pattern collision resolution method and meticulous parameter design, the proposed scheme achieves a gap of less than 1.2 dB compared to the random coding bound for up to 300 active users. This performance surpasses state-of-the-art schemes across a broad range of user numbers.

The achievable rate of code over resistive random-access memory (ReRAM) channel with finite selector failures was published in our recent work. The rate was derived under the assumption of independent and identically distributed (i.i.d.) input. In this work, focusing on the ReRAM channel with a single selector failure in the memory array, we derive an upper bound on achievable rate under arbitrary input distribution. This upper bound is within 0.02 bits from the achievable rate of i.i.d. input, indicating that i.i.d. is very close to optimal for large memory arrays. Moreover, we analyze the achievable rate of random code over ReRAM channel with suboptimal decodings where the decoder ignores the channel correlation. Our result indicates that in this case the achievable rate is limited by the capacity of a memoryless channel. We reveal both weak and strong asymptotic properties of ReRAM channel to prove this. The proof can be directly extended to the case of ReRAM with an arbitrary number of selector failures in the memory array.

Low earth orbit (LEO) satellite-based Internet of Things (IoT) has great potential to provide seamless global coverage, but the large propagation delay and severe Doppler shift in terrestrial-satellite link (TSL) will become the most challenging problem. To handle these challenges and facilitate massive grant-free random access, we propose an orthogonal time frequency space-based scramble-division multiple access (OTFS-SDMA) scheme, where the scrambling technique is used to tackle the correlated TSL channels between neighboring devices. At the receiver, we first propose a user activity detection (UAD) method based on capturing the dominant line-of-sight (LoS) path, without relying on the assumption of a static TSL. To facilitate accurate channel estimation (CE) against severe Doppler shifts, we exploit prior information about satellite velocity to detect the angles of arrival (AoAs) of active devices with the two-dimensional multiple signal classification (2D-MUSIC) algorithm, and further estimate the Doppler shifts. Building on the Doppler estimation, the orthogonal matching pursuit (OMP) algorithm is used to estimate the sparse TSL channel in the time-delay (TD) domain. In accordance with the OTFS-SDMA scheme, we propose a cross-domain elementary signal estimator (CD-ESE) for multi-user detection (MUD). In the CD-ESE MUD structure, both bit-level and symbol-level scrambling sequences help to distinguish neighboring active devices with correlated TSL channels, and the channel decoder works in conjunction with the CD-ESE to enhance MUD accuracy. Simulation results are provided to demonstrate the superior performance of the proposed OTFS-SDMA scheme over the state-of-the-art solutions.

An enhanced on-off division multiple access (ODMA) transmission scheme is proposed for unsourced multiple access network. The message of each active user is divided into two parts, where the first part is used to determine an on-off pattern, and the second part is encoded and transmitted in a time-hopping manner according to an on-off pattern. Leveraging the super sparse property of ODMA, the users' on-off pattern and pattern collisions are blindly detected based on the received signal without the help of pilot. Moreover, the on-off pattern detection, data decoding and collision recovery are performed iteratively over one sparse graph to enhance the overall system reliablity. We propose a finite-length performance analysis to the on-off pattern detection and iterative multi-user decoding, based on which both the user access sparsity, and channel code are optimized. Numerical result shows that with a rate 1/ 3 low-density parity-check code over G F (26), the gap between the proposed scheme and the random coding bound is less than 1.2 dB for up to 300 active users.

Massive Machine-Type Communications (mMTC) features a massive number of low-cost user equipments (UEs) with sparse activity. Tailor-made for these features, grant-free random access (GF-RA) serves as an efficient access solution for mMTC. In GF-RA systems, the covariance-based maximum likelihood detection (CB-MLD) algorithm is extensively employed to achieve the user-equipment activity detection (UAD). However, the limited receiving antennas and UAD decision made upon the hard threshold may undermine the UAD accuracy of the CB-MLD algorithm. To address this problem, we propose a hybrid-driven deep neural network (DNN) for UAD termed as the hybrid-driven user-equipment activity detection network (HyD-UADNet), which is composed of a model-driven coordinate descent network (MD-CDNet) and a data-driven soft thresholding network (DD-STNet). Specifically, the MD-CDNet is designed to modify the step size for the coordinate descent operation in each CB-MLD iteration and alleviate the impact of the limited antennas. Following the MD-CDNet, the DD-STNet is constructed as an adaptive soft thresholding function to replace the hard threshold for the UAD decision. Simulation results are provided to demonstrate the effectiveness of the hybrid-driven method and the performance of the HyD-UADNet.

Massive machine-type communications (mMTCs) features a massive number of low-cost user equipment (UE) with sparse activity. Tailor-made for these features, grant-free random access (GF-RA) serves as an efficient access solution for massive machine-type communication (mMTC). However, most existing GF-RA schemes rely on strict synchronization, which incurs excessive coordination burden for the low-cost UEs. In this work, we propose a receiver design for asynchronous GF-RA, and address the joint user-activity detection (UAD) and channel estimation (CE) problem in the presence of asynchronization-induced intersymbol interference. Specifically, the delay profile is exploited at the receiver to distinguish different UEs. However, a sample correlation problem in this receiver design impedes the factorization of the joint likelihood function, which complicates the UAD and CE problem. To address this correlation problem, we design a partially uni-directional (PUD) factor graph representation for the joint likelihood function. Building on this PUD factor graph, we further propose a PUD message passing-based sparse Bayesian learning (SBL) algorithm for asynchronous UAD and CE (PUDMP-SBL-aUADCE). Our theoretical analysis shows that the PUDMP-SBL-aUADCE algorithm exhibits higher signal-to-interference-and-noise ratio (SINR) in the asynchronous case than in the synchronous case, i.e., the proposed receiver design can exploit asynchronization to suppress multiuser interference. In addition, considering potential timing error from the low-cost UEs, we investigate the impacts of imperfect delay profile, and reveal the advantages of adopting the SBL method in this case. Finally, extensive simulation results are provided to demonstrate the performance of the PUDMP-SBL-aUADCE algorithm.

A signal scrambling based joint blind channel estimation, activity detection, and data decoding (SS-JCAD) scheme is proposed for coded massive random access. This signal scrambling technique imposes symbol-wise phase rotation to each user’s modulated data, and the scrambling pattern serves as a user-specific signature which is free from any bandwidth expansion or pilot signaling overhead. Building on this scrambling signature, we further propose a simple yet efficient receiver design, which integrates the blind channel state information (CSI) estimation module with the forward error correction (FEC) decoder. Specifically, according to the scrambling signature, a user-specific posterior probability density function of the CSI is derived, based on which both the CSI and activity of each user can be blindly detected using a low-complexity single-user maximum a posteriori estimation. Given the estimated CSI asa prioriinformation, a joint CSI (including user activity) estimation and data decoding algorithm is proposed, where the soft information is iteratively updated between the FEC decoder and the CSI estimation module to refine the detection reliability. Simulation shows that for massive random access systems with moderate code length and system load factor less than 1.5, the SS-JCAD scheme achieves almost the same bit error rate as the ideal case aided with perfect CSI, implying the SS-JCAD scheme as a near-optimal solution to the massive random access scenario.

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates the sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as the unknown identity of active users and multiuser interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference-based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.

Massive Machine-Type Communications (mMTC) scenario features a massive number of randomly activated user equipments (UEs). To efficiently support the random access for UEs in mMTC, a classifier diversity-combining based in-dependent component analysis (CDC-ICA) grant-free random access (GF-RA) scheme is proposed in this paper. Specifically, the base station (BS) employs multiple ICA classifiers for GF-RA detection. In each ICA classifier, an unsupervised learning technique, i.e. the independent component analysis (ICA) is employed to directly separate the source signals of active UEs from the received data signals. Furthermore, three strategies are proposed in the CDC-ICA scheme, i.e. independent ID encoding, noise-level estimation, and classifier diversity combining, to fully exploit the diversity provided by different ICA classifiers. Finally, simulation results show that the proposed CDC-ICA scheme outperforms existing ICA-based and compressed sensing (CS)-based GF-RA schemes in terms of the detection accuracy.

Massive machine-type communications (mMTC) is an important scenario to support Internet of Things (IoT) services. However, the massiveness of user equipments (UEs) poses new challenges for existing grant-free random access (GF-RA) schemes, such as pilot collisions and accumulation of failed UEs. To address this problem, we consider consecutive RA slots, and propose a cross-slot UE scheduling strategy for collision resolution in GF-RA systems. Specifically, different types of UEs are scheduled to select different sets of pilots via the feedback information. In this way, pilot collisions can be alleviated by dynamic UE scheduling. Then, we construct three deep neural networks (DNNs) for different collision-resolution tasks in UE scheduling, and these DNNs are trained to improve the scheduling efficiency. Furthermore, we adopt a matched training strategy for DNN training, which integrates the loss function of different DNNs to improve the output accuracy. Finally, a complete GF-RA scheme with DNN-aided UE scheduling (DNN-UESch-GFRA) is established. Simulation results are provided to verify the effectiveness of the matched training strategy, and show that the DNN-UESch-GFRA scheme can effectively resolve random access (RA) collisions and improve RA throughput.

With recent advances on the dense low-Earth orbit (LEO) constellation, the LEO satellite network has become one promising solution for providing global coverage for Internet-of-Things (IoT) services. Confronted with the sporadic transmission from randomly activated IoT devices, we consider the random access (RA) mechanism and propose a grant-free RA (GF-RA) scheme to reduce the access delay to the mobile LEO satellites. A Bernoulli–Rician message passing with expectation–maximization (BR-MP-EM) algorithm is proposed for this terrestrial–satellite GF-RA system to address the user activity detection (UAD) and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into two stages. In the inner iterations, the Bernoulli messages and Rician messages are updated for the joint UAD and CE problem. Based on the output of the inner iterations, the expectation–maximization (EM) method is employed in the outer iterations to update the hyperparameters related to the channel impairments. Finally, simulation results show the UAD and CE accuracy of the proposed BR-MP-EM algorithm, as well as the robustness against the channel impairments.

Due to the massive number of devices in the M2M communication era, new challenges have been brought to the existing random-access (RA) mechanism, such as severe preamble collisions and resource block (RB) wastes. To address these problems, a novel sparse message passing (SMP) algorithm is proposed, based on a factor graph on which Bernoulli messages are updated. The SMP enables an accurate estimation on the activity of the devices and the identity of the preamble chosen by each active device. Aided by the estimation, the RB efficiency for the uplink data transmission can be improved, especially among the collided devices. In addition, an analytical tool is derived to analyze the iterative evolution and convergence of the SMP algorithm. Finally, numerical simulations are provided to verify the validity of our analytical results and the significant improvement of the proposed SMP on estimation error rate even when preamble collision occurs.