URLLC applications impose stringent latency and reliability requirements, making its compliance challenging due to the inherent trade-off between them. These applications typically involve the exchange of small information blocks. Convolutional codes (CC) exhibit near-optimal performance when encoding short blocks. To enable packet based transmissions, CCs require some kind of termination. A zero-terminated CC (ZTCC) enables efficient maximum likelihood (ML) decoding through the Viterbi algorithm, but suffers from a rate-loss particularly prominent in short blocks. A tail-biting CC (TBCC) avoids rateloss but entails significantly higher ML decoding complexity. Despite the ZTCC having lower ML decoding complexity and similar error performance, TBCC has received preference by wireless standards, essentially due to ZTCC rate-loss. This work proposes a novel distributed multiuser ZTCC (MU-ZTCC) coding scheme, which eliminates rate-loss by encoding multiple physically separated users over-the-air. Local user data undergoes standard ZTCC encoding followed by multiuser encoding via over-the-air summation. Due to its zero termination, ML decoding of MU-ZTCC is accomplished with a single Viterbi execution. The results reveal that the user error performance in MU-ZTCC approaches that of orthogonal transmissions as the SNR increases. This scheme can be viewed as a nonorthogonal multiple access scheme, whose structure enables ML joint detection and decoding with the complexity of standard Viterbi algorithm.

The ultra-reliable low-latency communications (URLLC) tight latency requirements paired with transmission of small payload packets motivates the development of techniques that reduce or eliminate the need for dynamic scheduling. This justifies the study of grant free (GF) leveraged techniques in order to reduce both the latency and control signaling overhead. Previous works considered preallocating resources not only for the first transmission, but also for all possible IR-HARQ transmissions, effectively reducing the scheduling latency and control signaling overhead. However, this has several drawbacks, as it translates into wasted resources. To address these issues, we propose a group-based preallocation method combined with IR-HARQ. Initially, a pool of preallocated resources is assigned to a group of users, which then cooperatively use IR-HARQ feedback signals to distribute, on the fly, the resources amongst them without collisions. The proposed method has two phases: a preallocation phase that takes place once at the group formation stage and a transmission phase which happens at each uplink transmission. The transmission parameters for all possible transmission scenarios are selected at the preallocation stage, with the goal of reducing the latency under reliability and energy constraints. The transmission parameters are obtained through a constrained latency optimization procedure, which considers the stochastic nature of the underlying process. We prove that, asymptotically, the proposed scheme is able to reduce the latency, at least, down to the average latency of any single user (SU) HARQ.  The numerical results show that the latency and resources wastage is significantly reduced comparatively to single user IR-HARQ with preallocated resources.