You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey: Non-Orthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC 
Chapter Title:  Block-OLS and group-OMP 
1. Abstract of Cited Paper (BIB001):  Machine-to-Machine communication requires new physical layer concepts to meet future requirements. In previous works it has already been shown that Compressive Sensing (CS) detectors are capable of jointly detecting both activity and data in multi-user detection (MUD). For this detection we propose a new generalized Group Orthogonal Matching Pursuit algorithm that allows the use of additional side information regarding the sparsity structure. As a specific example, we exploit the information of a sparsity-aware Viterbi decoder in an iterative feedback loop to improve the activity detection. Here, a significant improvement of the activity detection is already achieved by executing only a single additional detection and decoding step. 
2. Abstract of Cited Paper (BIB002):  Rapid growth in the number of users (largely sensor nodes) of most Internet-of-Things (IoT) applications is widely expected in recent years. 3G or 4G protocols are designed for regular Human-to-Human communication purposes instead of low-rate, Machine-to-Machine (M2M) communication, which is the basis of IoT. Comparatively, traditional spread-spectrum protocols such as Code-Division Multiple Access (CDMA) are considered applicable for the majority of IoT applications. However, they are unsuitable to be utilized in the expected massive-IoT scenarios. The traditional Multi-User Detection (MUD) approach used in these protocols has poor scalability. When the number of users reaches hundreds, the complexity and hardware cost of traditional MUD become impractically high. In this paper, we employ the concept of Multiple Measurement Vector Compressive Sensing (MMV-CS) to exploit the feature of user sparsity in spread-spectrum-based IoT applications. By reformulating the CS detection model, a memory reduction of approximately 9,400 times can be achieved. Along with other hardware cost-down, the proposed simplified structure of the reconstruction model also allows a faster detection speed (∼10x to 1000x improvement) while a satisfactory level of Bit-Error-Rate (BER) is still maintained.

Literature Review:
The activity detection of CS-MUD using greedy algorithms was improved by exploiting the fact that when a node is active it transmits several bits in the current frame, therefore, makes the multiuser signal frame as block sparse  BIB001 . In BIB001 , the OLS is extended to block-wise orthogonal least squares (BOLS) algorithm which detects the active user for a block of transmitted symbols. The activity is detected based on the sum of the minimum Euclidean distance of the spreading sequences from the block of N d symbols of the received signal. In the group orthogonal matching pursuit (GOMP) for CS-MUD  instead of considering only the maximum correlated column of the spreading matrix with the received signal, the sum of the correlations of the N d received signals with the spreading sequence is set as the selection criteria which improves the activity detection. In  to avoid the complexity of matrix inversion for larger N d , the frame is divided into sub-frames and parallel detectors are deployed at the receiver to detect the signal. Furthermore, it is shown that false alarms, which are less critical as compared to miss detections, are reduced by incorporating activity-aware Viterbi decoder, which acts as a decision device for the activity of user on frame level. The activity detection was further improved by deploying a weighted GOMP (wGOMP) algorthim in BIB002 . In wGOMP algorithm, the weights are generated by the channel decoder as BIB002  where ξ C (d k ) is the Euclidean distance of the most likely codeword, the term ξ (d k ) = d k 2 gives an indication about the activity of node k. Γ (k) contains the vector indices corresponding to group k and N d is the length of the vector d. The activity detection is enhanced by multiplying these weights with the correlation in the group selection step of GOMP. The symbol error rate (SER) of the CS-MUD by deploying wGOMP improved by magnitude of one as compared to GOMP and meet the oracle least square performance at SNR=20 dB BIB002 . In BIB003 , MMV-CS model was considered and simultaneous orthogonal matching pursuit algorithm (SOMP) is proposed. The algorithm is similar to GOMP algorithm and detects the support for a group of symbols. It is assumed that the sparsity remains the same for the whole frame.