A Survey: Non-Orthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC <s> Structured matching pursuit <s> In Internet of things (IoT)-oriented 5G systems, more efficient multiple access is essential to handle the massive number of sporadic traffic generating IoT users/devices, which are inactive most of the time but regularly or irregularly access or leave the wireless network without human interaction. Non-orthogonal multiple access (NOMA) is a promising solution to support massive connectivity in future IoT-oriented 5G systems, where the dynamic multi-user detection (MUD) is required. In this paper, by exploiting the structured user activity sparsity, we propose the structured matching pursuit (SMP)-based dynamic MUD to jointly realize dynamic multi-user signal detection in several continuous time slots based on structured compressive sensing (SCS). Specifically, in several continuous time slots, the set of active users usually changes slowly due to continuous data transmission. Therefore, we can divide the active user sets into two different parts, i.e., the common active user set and the dynamic active user sets. Accordingly, the proposed SMP-based dynamic MUD simultaneously detects the common active user set in all time slots at first, and then the dynamic active user sets are detected in each single time slot individually. Simulation results show that the proposed SMP-based dynamic MUD can achieve much better performance than that of the conventional compressive sensing (CS)-based MUD, while they share very similar computational complexity. <s> BIB001 </s> A Survey: Non-Orthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC <s> Structured matching pursuit <s> Non-orthogonal multiple access (NOMA) can support more users than OMA techniques using the same wireless resources, which is expected to support massive connectivity for Internet of Things in 5G. Furthermore, in order to reduce the transmission latency and signaling overhead, grant-free transmission is highly expected in the uplink NOMA systems, where user activity has to be detected. In this letter, by exploiting the temporal correlation of active user sets, we propose a dynamic compressive sensing (DCS)-based multi-user detection (MUD) to realize both user activity and data detection in several continuous time slots. In particular, as the temporal correlation of the active user sets between adjacent time slots exists, we can use the estimated active user set in the current time slot as the prior information to estimate the active user set in the next time slot. Simulation results show that the proposed DCS-based MUD can achieve much better performance than that of the conventional CS-based MUD in NOMA systems. <s> BIB002
In the algorithms, which exploit block sparsity, the general assumption is that a node is active for several consecutive symbols. However, in mMTC some nodes may be active for several consecutive symbols while others may be active for a less number of symbols. Therefore, the active nodes set is changing within the duration of a frame. This structured sparsity in multiuser signal is considered in BIB001 [29] to improve the MUD. It is assumed that a portion of the active users remain active for several continuous time slots (common active users) while others changes at each time slot (dynamic active users). In BIB001 , it is assumed that the number of common active users is known a-prior. An algorithm called structured matching pursuit (SMP) algorithm is proposed for MUD. In SMP, the common active users are detected first based on the sum of their energies over the specified continuous time slots. After the common active users are detected, the residual (initialized to the received signal) is updated by subtracting the effect of the common active users. The dynamic active users are then detected at each time slot using OMP like selection criteria. The algorithm is compared with OMP, which detects the activity on symbol-by-symbol base, and it is shown that the performance is improved by magnitude of one at SNR 3 dB BIB001 . In BIB002 the idea was extended to estimate the data efficiently by exploiting this temporal correlation in the user activity. In the proposed dynamic compressive based MUD algorithm in BIB002 , the active users are detected at symbol level and the support obtained for one symbol is used as an initial support for estimating the data at the next time slot. The proposed algorithm significantly improves the data estimation as compared to the conventional symbol-by-symbol OMP data estimation.