LSTM-Aided Hybrid Random Access Scheme for 6G MTC Networks

An LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G machinetype communication (MTC) networks, where ultra-reliable low latency communications (URLLC) and massive MTC (mMTC) devices coexist. This scheme allows URLLC devices to access the network via a two-step contentionfree access procedure to satisfy latency and reliability access requirements, and mMTC devices to access the network via a contention-based timing advance (TA)-aided access procedure to meet massive access requirement. Furthermore, to reduce the latency of URLLC devices, we propose an attention-based LSTM prediction model to predict the number of active URLLC devices, and thus determining the parameters of the multi-user detection algorithm dynamically. We analyze the successful access probability of the LSTMH-RA scheme. Numerical results show that, compared to the benchmark schemes, the proposed LSTMH-RA scheme significantly improves the successful access probability, and satisfies the diverse QoS requirements of URLLC and mMTC devices.