Compressive sensing based overhead reduction scheme in multi-antenna downlink management

Recent researches show that the technique of compressed sensing could be used for channel estimation and simultaneously identify the set of self-selecting users who has joined in the channel in a multiple-input multiple-output downlink resource sharing scenario. Meanwhile, it enables the transmitter to obtain channel state information (CSI) with acceptable accuracy yet significantly reduced uplink feedback overhead. In this article, by considering the properties of measurement matrix in compressed sensing, we present and compare several different types of identity sequences (IDS) (e.g., sequences generated by Hadamard matrix or Bernoulli process), with which the system can achieve superior estimation performance with fewer number of sequences. Consequently, the system with these new IDS could contribute to further minimize the uplink feedback resource cost. Simulations show that the adoption of the new identity sequence proves to be fruitful in terms of mean squared error, and much energy can be saved compared with primitive sequences.