We explore in this paper code division multiple access systems with multiple transmitter and receiver antennae combined with algebraic constellations over a quasi-static multipath fading channel. We first propose a technique to obtain transmit diversity for a single user over quasi-static fading channels by combining algebraic constellations with full spatial diversity and spreading sequences with good cross-correlation properties. The proposed scheme is then generalized to a multiuser system using the same algebraic constellation and different spreading sequences. We also propose a linear multiuser detector based on the combination of linear decorrelation with respect to all users, and the application of the sphere decoder to decode each user separately. Finally, we consider the generalization to multipath fading channels where the additional diversity advantage due to multipath is exploited by the sphere decoder, and a method of blind channel estimation based on subspace decomposition is examined.

Achieving higher aggregate data rates for many simultaneous users is considered as a fundamental challenge for the next generation of wireless systems. With low-density spreading (LDS), one could transmit information by overloading, i.e., using more spreading sequences than chips with reasonable implementation complexity. In this paper, we propose an improvement to the conventional LDS technique that consists in introducing a bit interleaver in the transmission scheme. Its role is to render the error bursts that appear at the output of the LDS detector into correctable patterns for the channel decoder. Numerical evaluations of the spectral efficiency show SNR improvements of more than 2dB when using convolutional codes in BI-LDS schemes. Slight improvements are also observed when turbo codes are employed.

A new Deep Neural Network (DNN)-based error correction encoder architecture for channels with feedback, called Deep Extended Feedback (DEF), is presented in this paper. The encoder in the DEF architecture transmits an information message followed by a sequence of parity symbols which are generated based on the message as well as the observations of the past forward channel outputs sent to the transmitter through a feedback channel. DEF codes generalize Deepcode in several ways: parity symbols are generated based on forward channel output observations over longer time intervals in order to provide better error correction capability; and high-order modulation formats are deployed in the encoder so as to achieve increased spectral efficiency. Performance evaluations show that DEF codes have better performance compared to other DNN-based codes for channels with feedback.

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