Sparsity Controlled Random Multiple Access With Compressed Sensing


This paper considers random multiple access in an exceedingly network where only a little portion of users have data to forward and transmit packets in every time slot because the user activity ratio isn't high in apply. For this reason, the access purpose (AP) needs to not solely determine the users who transmitted but conjointly decode the received information codewords. Exploiting the sparsity of transmitting users, Lasso, which could be a well-known practical compressed sensing algorithm, is applied for economical user identification. The compressed sensing algorithm enables the AP to handle a lot of users than the traditional random multiple access schemes do. We tend to develop distributed scheduling strategies for maximizing the system sum throughput, and we tend to analyze the corresponding optimal throughput for 3 totally different cases of channel data, i.e., the channel state data at the transmitter (CSIT), the channel state info at the receiver (CSIR), and also the imperfect channel state data at the receiver (ImCSIR). We also derive the closed-form expressions of asymptotically optimal scheduling parameters and therefore the corresponding most sum throughput for every CSI assumption. The results show the effects of system parameters on the add throughput and offer helpful insights on using compressed sensing for throughput maximization in random multiple access schemes.

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