The concept of sub-networks has been recently identified as an important component of 6G to enable high-demanding critical services with capabilities of hyper reliable low-latency communications (HRLLC) at the local area of the sub-network. In this paper, we formulate the power control problem for sub-networks as finding the transmit power vector, corresponding to the sub-networks, to minimize the sum interference-to-signal power ratios across all the sub-networks. We propose then a heuristic sequential iterative power allocation (SIPA) and an optimal power allocation based on gradient descent-based algorithm (GDPA). Extensive system simulations validated the proposed methods. In particular, the SIPA achieves significant performance gains over the fixed transmit power setting, with limited performance loss over the GDPA. In most cases, the proposed methods outperform the max-min power allocation with much lower feedback signaling overhead and complexity. Furthermore, to keep 99.9% of all the sub-network link instances to achieve reliability of 6 nines, the SIPA can save radio resources by about 27% compared to the fixed transmit power setting.

In this paper, we create a scalable user activity detection (UAD) scheme for massive access. The proposed solution provides a holistic design from signature sequence design and its transmission method at device side to the reception method at base station side. We design a set of constant-modulus signature sequences for accurate, fast and scalable UAD. The designed sequences are the nonorthogonal complex sinusoid sequence. Different devices can be discriminated by assigning distinct digital frequency, which allows for unique association between user and signature sequence and collision avoidance. Taking advantage of the precoding strategies based on phase compensation or pre-equalization, the UAD problem can be casted as a linear inverse problem of nonnegative underdetermined system. At the heart of which, a sparse nonnegative vector can be solved by a nonnegative least square method, the support of which preserves the activity information exactly. In this way, base station can readily identify any possible combination of active users out of the entire set as long as the sequence length more than the number of actual active users, no matter how the total number of devices scales up.

In this paper, we firstly analyze potential use cases and new requirements related to ultra-reliable and low-latency communications (URLLC) in 6G era. With the objective of showing feasibility of round-trip latency less than 100 µs, we propose a system design that introduces novel features in comparison to 5G design such as larger SCS, new frame structure and paired scheduling, and create a proof-of-concept (PoC). A verification test in a real indoor scenario shows that our system can achieve 78 μs round-trip latency and higher reliability than 5G under static environment. Furthermore, we describe challenges, potential solutions and research directions for 6G hyper reliable and low-latency communication (HRLLC).

The concept of in-X subnetworks has been recently proposed to meet extreme communication requirements such as sub-millisecond latency and up to 9 nines reliability in 6thgeneration (6G) networks. On the other hand, many open challenges have already been recognized for this new concept, from air interface design to interference management in dense and dynamic scenarios. In this paper, we focus on subnetworks for industrial wireless control applications and propose an advanced frequency resource allocation scheme, denoted as sequential iterative subband allocation (SISA), which is designed to minimize the sum interference-to-signal ratio over all subnetwork links. Through extensive system level simulations, we evaluate the benefits of the proposed SISA scheme and compare it with the state-of-the-art. Numerical results show that SISA with interference weighting strongly outperforms a greedy distributed scheme, by reducing by half the frequency resources needed to enable 99.9% of all the subnetwork link instances to achieve reliability of 6 nines.

The 6G network enables a subnetwork-wide evolution, resulting in a “network of subnetworks”. However, due to the dynamic mobility of wireless subnetworks, the data transmission of intra-subnetwork and inter-subnetwork will inevitably interfere with each other, which poses a great challenge to radio resource management. Moreover, most existing approaches require the instantaneous channel gain between subnetworks, which are usually difficult to be collected. To tackle these issues, in this paper we propose a novel effective intelligent radio resource management method using multi-agent deep reinforcement learning (MARL), which only needs the sum of received power, named received signal strength indicator (RSSI), on each channel instead of channel gains. However, to directly separate individual interference from RSSI is an almost impossible thing. To this end, we further propose a novel MARL architecture, named GA-Net, which integrates a hard attention layer to model the importance distribution of inter-subnetwork relationships based on RSSI and excludes the impact of unrelated subnetworks, and employs a graph attention network with a multi-head attention layer to exact the features and calculate their weights that will impact individual throughput. Experimental results prove that our proposed framework significantly outperforms both traditional and MARL-based methods in various aspects.

Among the realm of ultra-reliable and low-latency communication (URLLC), a challenging problem is how to realize an efficient transmission meeting both latency and reliability requirements. As a promising technique to tackle the above problem, cooperative non-orthogonal multiple access (C-NOMA) has gained much attention in recent years. When the relay (R) is close to the source, the superior of C-NOMA has been verified if the cooperation is implemented in the first phase. However, the case where R is close to the destination, which also frequently appears in the cooperative transmission, has not been well investigated for URLLC yet. To fill up this gap, we propose a packet re-management based C-NOMA transmission scheme in which the cooperation is implemented in the second phase. Further, considering the importance of power consumption, we aim for jointly optimizing blocklength, power, re-managed packet size, and block error rate to minimize the power consumption subject to URLLC requirements and the maximum power constraint. To tackle the above non-convex and implicit problem, we first propose an algorithm to obtain a sub-optimal solution. Then, an extended algorithm is proposed to further approach the optimal solution. Simulation results verify the effectiveness of the proposed scheme and algorithms.

To realize the efficient transmission under ultra-reliable and low-latency communication (URLLC), cooperative non-orthogonal multiple access (C-NOMA), in which the two-phase cooperative transmission is considered and non-orthogonal multiple access (NOMA) is implemented during the cooperation, has gained much attention in recent years. When the cooperation is implemented in the first phase of relaying, the C-NOMA scheme has superb performance only when the relay (R) is close to the source, since NOMA gain comes from the channel quality difference. To expand the versatility of C-NOMA in the case where R is close to the destination (D), we propose a packet re-management-based C-NOMA transmission scheme, in which the cooperation is implemented in the second phase of relaying. Further, considering the importance of power consumption in 5G, we also propose an efficient algorithm to minimize the power consumption, subject to URLLC requirements. Specifically, to tackle the above non-convex and implicit problem, we first provide a tight upper bound approximation of channel dispersion to make the problem explicit. Then, on the basis of block coordinate descent and successive upper bound approximation frameworks, a recursion method is proposed to solve the joint optimization problem. Simulation results verify the effectiveness of the proposed scheme in the case where R is close to D.

In this paper, we focus on the network deployment of NR-U system coexisting with WiFi system in the same unlicensed spectrum. In NR-U study, detecting the presence of WiFi was proposed as one candidate solution for coexistence fairness. However, it is very challenge to do such inter-RAT signaling detection by current NR-U receiver due to lack of time and frequency synchronization. We propose a dual-functional deep receiver for NR-U system, which is able to conduct both NR-U data receiving and WiFi preamble detection by the same radio frequency (RF) units. Attention mechanism assisted deep learning is used to recognize the signaling pattern of WiFi preamble to better overcome unknown frequency offset and misaligned receive timing. From simulation evaluation, it can be observed that the ML based approach outperforms the legacy correlation and threshold based signal detection method in all SNR regions. Even with the assumption of frequency error and misaligned timing, the ML based algorithm can still achieve larger than 90% detection probability in case of -5dB SNR. Hence, with proposed solution, inter-RAT signaling detection could be a practicable coexistence manner to be utilized in existing unlicensed bands or greenfield 7GHz unlicensed bands.

This paper studies a industrial wireless control system where a motion controller has a continuous closed-loop control link to a group of actuator devices on a factory floor. The goal is to achieve service requirements of ultra-reliable and low-latency communications (URLLC) for such motion control system. We present our latest findings on group-based device-to-device (D2D) relaying for achieving spectrally efficient URLLC technology. Namely, we propose to group together the devices in close proximity and exploit intra-group D2D relaying to enforce reliable access. Next, we demonstrate the benefits of identifying the users with weak channel in each group and scheduling those users with on-demand relaying. We found that devices in momentary poor channel conditions are also the weakest link in the system-level reliability. Therefore, on-demand relaying for those devices can significantly improve reliability while maintaining a better utilization efficiency of network resources. The presented analysis leads to the conclusion that a future wireless control system for 5G and beyond shall include support for on-demand D2D relaying to achieve the best performance.

In this paper, we focus on a shared relay architecture in the cellular network instead of deploying multiple separated relays near cell edges. The advantage of shared relaying is the joint processing of signals at the relay to suppress the inter-cell interference (ICI). The performance of a relay enhanced cellular system depends significantly on the resource allocation scheme, especially when taking into account the resource competition from different links. Therefore, in this work, an efficient and effective resource allocation and scheduling scheme based on the subframe structure is proposed to maximize the benefit of the shared relay. System level simulations quantify the system performance in different resource demand situations, and show that the deployment of shared relays can improve significantly the overall system performance and also increase the throughput of cell edge users as compared to the conventional separated cell edge relay station deployment.

In this paper, we focus on a half-duplex downlink cellular system with relay cooperation. We consider the beam-forming design problem with a limited amount of feedback for channel state information (CSI), which is only available at the receiver side. Based on this structure, we calculate the sum-rate lower bound when zero forcing (ZF) or eigen-beamforming is implemented at the macro base station and relay station. With the lower bound, we optimize the numbers of feedback bits for quantizing the desired and interfering channels and propose an adaptive beamforming strategy with limited feedback which selects a suitable beamforming scheme from ZF or eigen-beamforming based on the received interference and noise power level of the users. Simulation result shows that the bit partitioning algorithm outperforms the equal bit distribution scheme especially when the signal to noise ratio (SNR) is large. In addition, our proposed adaptive beamforming strategy outperforms existing schemes in different interference scenarios.

In this paper we consider a half-duplex multiple-input multiple-output (MIMO) cooperative amplify-and-forward (AF) relay network with multiple relay nodes. Firstly, we assume the channel state information (CSI) is available at all nodes to investigate the optimal source and relay beamforming vector design and relay selection. Based on this structure, a new scheme of limited feedback joint effective relay selection and beamforming vector design is proposed, which uses a new strategy to share channel information between relay and destination. This proposed scheme, which chooses the best relay and optimizes the beamforming vectors maximizing the received SNR, can significantly reduce the required number of feedback bits and maintain a suitable performance compared to the optimal non-practical solution. Simulation results show that our scheme approaches the performance of the scheme with full CSI assumption and outperforms conventional schemes, while having lower computational complexity and lower feedback delay. The cooperative diversity gain is also verified by the simulation result.