With the rapid growth of time-sensitive services, existing infrastructures struggle to guarantee large-scale millisecond-level latency and high reliability. To meet these demands, massive ultra-reliable and low-latency communication (mURLLC) has been introduced as a core scenario in sixth-generation (6G) networks. To address the demands of mURLLC, we propose a hierarchical computing architecture for cell-free (CF) systems, comprising layers for load computation, demand mapping, and resource allocation. Specifically, in the first layer, we develop network load computation methods for CF systems based on the grant-free random access (GFRA) mechanism, where the load is dynamically inferred from subchannel (SC) occupancy patterns. In the second layer, we establish an analytical model that links access failure probability with quality-of-service (QoS) requirements, enabling accurate mapping of subchannel resource demands. In the third layer, based on finite blocklength transmission theory, we establish a joint delay optimization model and formulate the end-to-end (E2E) instantaneous delay minimization problem. To effectively address this problem, we further develop an adaptive blocklength-based resource allocation (ABRA) scheme that integrates with the hierarchical computing architecture for minimizing E2E delay. Numerical results have demonstrated that the proposed architecture can accurately capture network load variations, automatically adjust blocklengths according to users’ QoS requirements, and significantly reduce E2E delay.

Uplink sensing in cell-free integrated sensing and communication (CF-ISAC) systems provides a promising solution by reusing massive communication signals. This approach offers low system overhead and enables wide-area coverage through densely deployed users and distributed access points (APs). However, due to the tight coupling between communication and sensing, accurate extraction of uplink sensing parameters becomes a critical bottleneck: sensing parameter extraction relies on precise demodulation of uplink communication signals, while high-quality channel estimation for data demodulation, in turn, requires accurate sensing results. To address this challenge, we propose an iterative communication-sensing optimization framework under uplink CF-ISAC architecture. This framework establishes dynamic information feedback among the three core modules of data detection, channel reconstruction and target sensing, achieving the collaborative improvement of communication-sensing performance. Specifically, the target sensing module integrates pilot-based sensing and data signal-enhanced sensing to extract target parameters. The channel reconstruction module maps the sensing results to channel state information (CSI). The data detection module recovers data symbols using the reconstructed CSI and feeds back both demodulated symbols and residual errors to the sensing module, enabling iterative correction of target parameter estimation. The simulation results show that the proposed iterative framework achieves simultaneous suppression in communication bit error rate (BER) and enhancement in sensing accuracy through several iterations, thereby effectively breaking through the traditional performance limits.

Semi-grant-free (SGF) access is regarded as a promising technology for next-generation networks, effectively easing the tension between massive access caused by user growth and limited communication resources. However, how to achieve efficient resource utilization through effective pairing of grant-based (GB) users and grant-free (GF) users in SGF access mechanism while ensuring system reliability is a critical challenge for enhancing overall network performance. This paper proposes a scalable SGF access scheme suitable for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Unlike traditional SGF schemes under centralized cellular systems, this scheme first groups access points (APs) into edge distributed unit (EDU)-centric clusters, then fully leverages the macro-diversity gain and signal power sparsity in the cell-free architecture, and employs a genetic algorithm (GA) to achieve efficient pairing between GB users and GF users. This approach ensures the quality of service (QoS) for GB users while sharing their resources with GF users, optimizing the efficiency of communication resource utilization. Subsequently, closed-form expressions for the signal-to-interference-plus-noise ratio (SINR) and outage probability of GB users and GF users under perfect and imperfect successive interference cancellation (SIC) are derived to evaluate the system reliability. Finally, simulation results demonstrate that the proposed scheme significantly outperforms the other scheduling schemes in terms of outage probability and resource utilization performance, particularly under massive access.

A robust frequency minimum mean square error (MMSE) estimator based on the weighted average of multipath signal for OTFS systems is proposed in this paper. Previous frequency estimators are based on the strongest single path signal or equally weighted average of multiple path signals so that the accuracies of estimators are limited. We generalise the weight coefficients to be taken arbitrarily, and their optimal values are determined such that the MSE of estimator is minimum. Simulation results show that the proposed method outperforms existing frequency estimators in terms of MSE performance, particularly when the normalized carrier frequency offset (CFO) is less than 0.5 and the symbol signal-to-noise ratio (SNR) of received signal is below 2 dB. Under static channel conditions, the proposed estimator can achieve a reduction in pilot energy of approximately 21.6 dB.

Single carrier frequency division multiple access (SC-FDMA) is a multicarrier modulation technique known for its low peak-to-average ratio (PAPR) and high spectral efficiency. However, SC-FDMA is more sensitive to carrier frequency offset than single carrier systems. In this paper, a method is proposed to analyse the frequency offset tolerance of SC-FDMA systems under different constellation modulations, and to use it as a theoretical guide for selecting the frequency offset estimation method. An none-data-aided (NDA) method is proposed and the estimation performance of data-aided (DA) and NDA methods are compared. It is found that the proposed carrier frequency offset synchronization method can satisfy the packet error rate (PER) performance with a difference of about 0.2 dB from the ideal case.

To mitigate the delay caused by large-scale devices access, the grant-free (GF) technology has been widely used in massive Ultrareliable-Low-Latency Communications (mURLLCs). However, the absence of a grant-based scheduling handshake with the base station leads to significant collisions when different users select the same resources, thereby compromising the reliability of the communication. To improve the reliability of the network, we propose a GF scheme assisted by hybrid automatic repeat request (HARQ) in Cell-Free massive Multiple Input-Multiple Output (CF mMIMO) system. Distinct from the traditional HARQ strategies employed in centralized cellular system, the proposed scheme first clusters user devices and access points based on Poisson cluster process (PCP) spatial distribution characteristics, and then fully exploits the macro diversity gain and spatial sparsity of the CF mMIMO system, effectively mitigating the impact of interference on the communication between devices. Subsequently, the HARQ strategy is introduced, and its round-trip delay is analyzed to achieve the maximum number of retransmissions under delay constraints. To further validate the effectiveness of the proposed scheme, the closed-form expression of uplink signal to interference plus noise ratio (SINR) with maximal ratio combining (MRC) receiver is deduced as well as the approximate outage probability expression. Finally, simulation results confirm the precision of the derived expressions and the enhancement of the proposed scheme on system reliability under stringent latency constraints.

Single Carrier Frequency Division Multiple Access (SC-FDMA) is a promising technology for uplink communication in satellite communication systems due to its advantages such as low peak-to-average ratio, high spectral efficiency, and flexible resource allocation. This paper designs a physical layer model for the satellite communication return link with SC-FDMA modulation. Based on an established satellite return link, we employ a localized FDMA (L-FDMA) subcarrier mapping scheme to compare the burst error rate (BER) simulation performance of three coding schemes: New Radio (NR) LDPC, Wireless Local Area Network (WLAN) LDPC, and Digital Video Broadcasting - Satellite - Second Generation (DVB-S2) LDPC. The simulation results propose that the NR LDPC coding scheme has better BER performance compared to the other two LDPC coding schemes, and the NR LDPC coding scheme is more suitable for satellite burst communication.

In 5G networks and beyond, enhanced Mobile Broadband (eMBB) service targets at providing extremely high throughput, Ultra-Reliable and Low Latency Communications (URLLC) service supports high reliability and low latency while massive machine-type communication (mMTC) aims at high connection density. Different types of traffic have different requirements, which brings difficultity to serve different users on the same resource block. In this paper, we investigate the co-existence of eMBB, URLLC and mMTC services based on superposition scheme in the 5G downlink scenario. To minimize the average data rate loss of eMBB users while satisfying the latency and reliability constraints of URLLC and mMTC users, we formulate a mixed-integer nonlinear program (MINLP) problem which is non-convex. We propose Successive Convex Approximation (SCA) Method and Proximal Policy Optimization (PPO), an advanced deep reinforcement learning (DRL) algorithm, to solve the non-convex problem. In the proposed algorithm, the agent seeks for the optimal user pairing and power allocation policy when the URLLC traffic arrives sporadically. Simulation results depicts the performance of our proposed PPO based superposition scheme nears to SCA-based method with lower complexity.

The second generation digital television broadcasting interactive satellite system (DVB-RCS2) plays an important role in the satellite communications. Single Carrier Frequency Division Multiple Access (SC-FDMA) is a modulation technique known for its low peak-to-average ratio (PAPR) and high spectral efficiency. However, SC-FDMA is more sensitive than single carrier system to synchronization errors. In this paper, it is proposed a timing and carrier synchronization algorithm that relies on the configuration of the reference signals in SC-FDMA Extended system for DVB-RCS2. The timing synchronization algorithm is designed based on the cyclic zero autocorrelation property of ZC sequences. Carrier synchronization consists of coarse synchronization and fine synchronization. The range of frequency offset is narrowed down in the coarse synchronization stage, and the residual offset is estimated based on the code-aided (CA) algorithm using the compensation value after the coarse estimation in the fine synchronization stage. Simulation results show that the proposed algorithm can achieve a performance similar to the ideal synchronization.

Massive ultrareliable and low-latency communications (mURLLCs) have emerged as a dominating 6G-standard service. Fast uplink grant is an effective means to solve the uplink access in mURLLC due to the advantages of low signaling cost and collision-free. However, as two key challenges in fast uplink grant, active set prediction and optimal scheduling still lead to high resource wastage and low access success probability. Based on the special spatial sparsity of cell-free networks, we propose the spatial-separable nonorthogonal multiple access (SSNOMA). Compared with nonorthogonal multiple access (NOMA), SSNOMA allows multiple users to share same 3-D resources composed of time-frequency resources and pilots, so as to reduce the resource wastage caused by prediction errors. Furthermore, we design an intelligent hierarchical fast uplink grant framework. In this framework, the upper controller is responsible for scheduling users from the predicted active user set to ensure the optimal Quality of Service (QoS) and active probability, while the lower controller strictly controls the allocation of uplink grants among the scheduled users to maximize spectral efficiency. In addition, considering the limited ability to collect information in massive user access, the upper confidence bound (UCB)-based multiarmed bandit (MAB) algorithm is used in the upper layer to schedule fast grant users, while the multiagent deep deterministic policy gradient (MADDPG) is used in the lower layer to perform specific grant allocation. Simulation results show that the proposed SSNOMA-based intelligent hierarchical framework can significantly improve the utilization of limited resources, and track long-term scheduling experience as well as QoS, effectively supporting mURLLC.

The second generation digital television broadcasting interactive satellite system (DVB-RCS2) plays a very important role in the satellite communication return channel. In actual application, interference from timing deviation and carrier frequency deviation will be encountered, especially turbo codes are very sensitive to carrier frequency deviation, so the design of efficient receiver for DVB-RCS2 must consider the synchronization problem. In this paper, we integrate the synchronization scheme into the decoding process of turbo codes. We use the log-likelihood ratio provided by the decoder for iterative synchronization, and use the result of cyclic redundancy check (CRC) as a stop signal for iterative detection, we call this approach code-aided (CA) synchronization. Simulation experiments show that the CA synchronization scheme is significantly better than the traditional data-assisted synchronization scheme, even comparable to the performance in ideal synchronous scenarios. In addition, combined with the CRC check, redundant iterations are avoided.

In this work, a study is conducted on the coexistence of two services, enhanced mobile broadband (eMBB) and massive machine-based communication (mMTC), in a fifth generation (5G) wireless communication system. Coexistence of these two services can be achieved by network slicing and non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) decoding to improve spectral efficiency, but the block error rate (BLER) of mMTC devices needs to be considered for short packet communications (SPC). In this paper, we combine deep reinforcement learning (DRL) proximal policy optimization (PPO) algorithm to efficiently accommodate mMTC devices into available random access (RA) slots while guaranteeing the throughput of eMBB devices to achieve dynamic scheduling of resources to improve system throughput. The performance of the proposed method is compared with recent solutions in the literature, and simulation results show that the proposed method can guarantee a low outage probability for eMBB services and effectively improve the throughput of mMTC services.

In the downlink of multiuser MIMO (MU-MIMO) systems, the co-channel interference (CCI) and noise are two major impairments. Maximizing signal to leakage plus noise ratio (SLNR) is a good criterion for the design of precoders since it making a balance between eliminating the CCI and noise. In this text, we propose a channel adaptive power allocation scheme based on SLNR precoding. Simulation results show that the proposed scheme can improve the BER performance considerably with a minor loss in the sum capacity performance. And we also illustrate that the additional computational complexity is very small.