# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Non Orthogonal Multiple Access
#### 3.4.3 Signalling Issues

Table 12: Summary of power allocation algorithms and joint user selection and PA algorithms in NOMA systems. interference management. In the literature, many authors use optimization techniques as well as analytical techniques to address all of these issues. * SIC Issues: The concept of SIC in NOMA was introduced to decode the signals of paired users at the receiver [147]. The authors used a PD-NOMA for multiplexing the users into one RB, and SIC was used to decode the users at the received PD-NOMA for multiplexing the users into one RB, and SIC was used to decode the users at the receiver. The SIC hardware implementation is very complex at high powers, but theoretical implementation is possible using Moore's law. The performance gain of NOMA has been affected by SIC characteristics such as receiver complexity, decoding order, imperfect channel estimation, and so on [148]. The SIC receiver has been affected by the decoding of users. If the decoding order of the users does not match the multiplexed sequence, then it will cause imperfect SIC implementation. In this case, a re-transmission request is forwarded to the BS, and the whole process has been repeated again to get the perfect SIC results from the receiver. However, it takes more time to decode the data at the receiver end [149]. Therefore, optimization algorithms have been proposed to avoid SIC imperfections [150; 151; 152; 153; 154]. In Ref. [150], the authors proposed an efficient PA algorithm for multi-carrier NOMA (MC-NOMA). The optimal SIC decoding value has been determined by the outage threshold of the carrier-to-noise ratio. Two algorithms, i.e., complementary geometric programming (CGP) and arithmetic geometric mean approximation (AGMA), have been proposed to optimise the total power [151]. A two-phase algorithm has been proposed to solve the non-convex resource allocation problems in NOMA [152]. Using a matching theory algorithm, the user scheduling is optimised first, and then the branch and bound technique is used to optimise PA. In Ref. [153], an efficient successive convex approximation (SCA) algorithm is proposed to optimise the sum rate. The sum rate and system utility are maximised by the SNR and the number of iterations in the MIMO-NOMA system. The iterative PA algorithm provides a maximum weighted sum rate in the NOMA system [154]. The analytical techniques used to overcome signalling issues such as outage probability and CSI of NOMA in UL and DL are addressed by the authors in Refs. [155; 156; 157]. In Ref. [155], the authors proposed a dynamic SIC receiver concept based on the users' received power, and the outage probability is estimated theoretically. SIC analytically estimates the statistical CSI to improve the outage probability [156]. In Ref. [157], a new decoding algorithm is used to decode the signal information at the SIC receiver, and its outage performance is verified analytically. * CSI Issues: In literature, most of the authors assumed a perfect CSI at the receiver. However, in NOMA, the concepts of SIC decoding, user pairing, and PA depend on imperfect CSI. This leads to computational complexity, overhead signalling, and delayed feedback. To overcome these issues, optimization and analytical methods are identified, and with these methods, the outage probabilities are increased by assuming imperfect CSI and also reducing the number of feedback bits [158; 159; 160; 161; 162]. The minimum outage probability is obtained from a minimum feedback rate. An optimal power allocation scheme is used to improve the total sum rate of a NOMA system [158]. The outage probability is minimised by optimising the dynamic PA using the one-bit CSI feedback method [159]. Imperfect CSI and perfect CSI models are used to estimate the system throughput and bandwidth [160]. The outage probability based on channel feedback is estimated analytically [161; 162]. As summarised in Table 13, various optimization and analytical algorithms have been proposed in recent research to resolve signalling issues and improve coverage and capacity in 5G networks.