Machine learning enabled heterogeneous semantic and Bit communication

A multi-user heterogeneous semantic communication (SemCom) and bit communication (BitCom) system is investigated. Each user can be served via either SemCom or BitCom for demanding semantic or bit data. Orthogonal/non-orthogonal multiple access (OMA/NOMA) techniques are employed to provide access for multiple users. Channel-based and user demand-based transmission protocols are proposed, where a joint optimization problem of the communication mode selection, frequency bandwidth and power allocation, and NOMA user pairing is formulated to maximize the long-term (equivalent) semantic throughput and user satisfaction, respectively. To solve the formulated problems: 1) For channel-based transmission, a twin-delayed deep deterministic policy gradient with reference neuron enhanced Softmax (TD3-RNS) algorithm is proposed, where a fixed-value neuron is invoked to improve the training efficiency; 2) For user demand-based transmission, a transfer TD3-RNS (T2D3-RNS) algorithm is proposed, where the learned policy is transferred to address the sparse rewards and perverse incentive problem caused by the optimization objective and reward shaping, respectively. Simulation results demonstrate that: i) The proposed heterogeneous scheme outperforms the baselines which merely use SemCom or BitCom; ii) Compared to OMA, NOMA is more compatible with the proposed heterogeneous scheme; and iii) The proposed algorithms outperform the benchmarks in channel-based and user demand-based transmission, respectively.