Performance Analysis of Network Diversity Multiple Access with Sequential Terminal Detection and Non-Orthogonal Training Sequences

This paper presents a new approach for terminal presence detection in the family of algorithms called network diversity multiple access (NDMA). The new scheme is based on non-orthogonal sequences. In NDMA, system-induced retransmissions are used to resolve the conflicts between colliding terminals. The key initial aspect in NDMA is to use signal processing tools to identify the size of the collision, as well as the identity of the contending terminals. This information is used to calculate the number of required retransmissions. These retransmissions are stored in memory, thereby creating a virtual MIMO (multiple input multiple-output) system that can be used to resolve the collision via source separation or multi-user detection. These detection and source separation processes are based on a set of orthogonal training sequences, each sequence uniquely assigned to one terminal in the network. This paper proposes a new approach for presence detection in NDMA using non-orthogonal sequences. The number of available sequences is increased and the bandwidth used for training is therefore considerably reduced. This comes at the expense of multiple access interference (MAI) between contending terminals. Additionally, in NDMA the estimation of the collision multiplicity is conventionally achieved in the first time-slot of the collision resolution period. This paper extends the detector to include all the received copies of the original transmissions (the initial transmission and also subsequent retransmissions). This means that after each retransmission received by the access point, the estimation of the collision multiplicity and contending terminals identification must be updated. The analysis here presented includes the effects of MAI caused by non orthogonal training sequences and the effect of sequential collision multiplicity estimation. Results suggest a considerably decrease of performance with respect to the orthogonal case scenario, but a more flexible training sequence allocation that becomes relevant for large numbers of terminals.