Sparsity Controlled Random Multiple Access With Compressed Sensing

Abstract

The paper considers random multiple access in a network where only a small portion of users have data to forward and transmit packets in each time slot because user activity ratio is not high in practice. For this reason, the AP has to not only identify the users who transmitted but also decode the received data codewords. Exploiting the sparsity of transmitting users, Lasso, a well-known practical compressed sensing algorithm, is applied for efficient user identification. The compressed sensing algorithm enables the AP to handle more users than the conventional random multiple access schemes do. We develop distributed scheduling methods for maximizing system sum throughput and analyze the corresponding optimal throughput for three different cases of channel knowledgechannel state information at transmitter (CSIT), channel state information at receiver (CSIR), and imperfect channel state information at receiver (ImCSIR). We also derive closed form expressions of asymptotically optimal scheduling parameters and the corresponding maximum sum throughput for each CSI assumption. The results show the effects of system parameters on sum throughput, and it provides useful insights on using compressed sensing for throughput maximization in random multiple access schemes.

A Survey of Non-Orthogonal Multiple Access for 5G

“…A range of CS-based random access schemes have been conceived recently, such as the family of asynchronous random access protocols [145] and compressive random access arrangement of [146]. Additionally, in [147], random multiple access relying on CS was invoked for maximizing the system's total throughput. Furthermore, the attainable throughput associated with different amount of channel knowledge was discussed, which provided useful insights into the quantitative benefits of CS in the context of throughput maximization in random multiple access schemes.…”

Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

“…As mentioned before, according to a report on mobile traffic [7], the number of active users does not exceed 10% of the total number of users even in the busy hours. This implies that even if there are a lot of potential users, which is a typical characteristic of massive connectivity in 5G, only a small part of users have data to send at the same time.…”

“…complex Gaussian variables with zero mean and unit variance, i.e, Rayleigh fading channels are considered in this paper, and such typical channel model has been widely used in wireless communications [6] [7]. v n is the Gaussian noise on subcarrier n with zero mean and variance σ 2 .…”

“…Specifically, according to the statistics of mobile traffics [7], the number of active users is usually much smaller than the number of all possible users even in the busy hours. Thus, the sparsity of user activity naturally exists in massive connectivity, which enables us to formulate the MUD problem under the CS framework [8].…”

A novel compressed sensing-based non-orthogonal multiple access scheme for massive MTC in 5G systems

“…In the 5G system, a single base station will serve 10 to 100 times more machine type devices (MTDs) than the personal mobile phones, which poses great challenges to efficiently support massive users random access [1][2][3]. According to the statistics of mobile traffics [4], the number of active users is usually much smaller than the number of all possible users even in the busy hours in cellular communications, especially for 5G MMTC applications, where users can sporadically access or leave the system. Thus, the sparsity of user activity naturally exists in massive connectivity.…”