## 1 Introduction
The basic concept of IRC is to simply suppress the unwanted signal information and enhance the wanted part with the help of multiple antenna branches at the receiver. The suppression is done by estimating the covariance matrix that includes the interference signals, and the desired signal is estimated by the channel matrix estimation of the serving cell [6; 7]. Estimation of the covariance matrix, which includes the signal information of all interferers, is a complex process, and for this purpose, the machine learning mechanisms discussed in [8; 9] can be integrated with the works performed in [6; 7]. Figure 1 shows the basic IRC concept. To date, numerous studies have been conducted using IRC for interference suppression. In [10], a low-complexity minimum mean square error (MMSE)-based IRC algorithm was developed for massive MIMO systems. Similarly, a study on the performance of IRC in fifth-generation (5G) networks was performed in [11; 12]. According to our knowledge, limited work has been performed on IRC related to HetNets, especially considering the hybrid-access mechanism, the studies of which include: Investigation of IRC receiver for LTE HetNets [13] and experimental evaluation of IRC in 5G HetNets [14; 15]. In recent years, NOMA has attracted a substantial amount of research in the field of wireless communication because of its potential to enhance the network capacity manifold [16; 17; 18; 19; 20]. NOMA achieves this by multiplexing multiple user transmission signals into a single transmission stream by exploiting the power domain. At the user end, successive interference cancellation (SIC) is used to obtain the required signal information and the rest is discarded. The order of performing SIC is determined by the increasing channel gain of the users [16], i.e., the user with high channel gain performs SIC, generally known as a cell center user (CCU), to decode and cancel out information of the low-gain user, generally referred to as a cell edge user (CEU). Currently, few works have been performed on the use of NOMA in HetNets, which mainly include: NOMA for device-to-device-enabled HetNets [21], spectrum allocation and power control using NOMA in HetNets [5; 22], and physical layer security for NOMA-based HetNets [23]. In [24], a contract-theory-based solution is derived for optimizing the use of mobile relays in the cooperative NOMA system, and in [25], a similar solution is proposed for optimizing user association and resource allocation for NOMA in HetNets. Similarly, to use NOMA in HetNets, some game theory and Stackelberg game-based economic approaches are proposed for different purposes in [26; 27; 28]. The existing works mainly focus on the integration of NOMA in HetNets or on the user power allocation enhancement with ideal SIC conditions. The main problem with using NOMA in small-cells is that when pairing is performed between the MUs and SUs within a small-cell, depending on the user gain, the performance of paired SUs (considering SUs as high-gain users) may deteriorate in terms of BER, compared to their unpaired state. To address this problem, in this work, WHT is used with NOMA, which overcomes this drawback and ensures better performance gains than the conventional NOMA. The Walsh-Hadamard transform is used as an orthogonal variable spreading factor (OSVF) to increase the diversity in communication systems [29]. It increases the constellation diversity in the modulation schemes, hence resulting in reduced BER performance and enhanced subsequent user throughput. In recent years, a number of studies have been conducted using the WHT in communication systems; a joint investigation on the WHT and Alamouti scheme was performed in [30], a transceiver design for single carrier frequency division multiple access (SC-FDMA) was proposed using the WHT in [31], and similarly, the WHT was applied on spatial modulation in [32] and on NOMA in [33; 34].