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Using interference channel, numerous significant wireless communication methods are designed which includes mobile Adhoc network simulation transmission, wireless local area networks, and cellular network base stations. Wireless communications have enabled billions of people to connect to the Internet, allowing them to profit from today's digital economy. Similarly, agreed-upon mobile phone standards enable consumers to use their phones everywhere in the world. Over the last 30 years, there has been a lot of interest in the general capacity of interference channels and the creation of attainable systems that approach the known upper constraints on total rates. The concept prompted the first attempts to describe the capacity area of the interference channel, which had gone focused on two-user interference channels (Ahmadi, 2019). Interference channel customary capacity is still an issue, whereas, very strong and special cases remain deciphered in (Wang et al., 2018). There are divergent attempts formed to describe asymptotic sum capacity behavior known as DoF or maximum achievable multiplexing gain (Villalonga et al., 2020) which focuses on wireless network interference/broadcast characteristics and high SNR. when a statement containing a variable approaches infinity, approaching a particular value taking into account as a variable approaches a limit, generally infinity: asymptotic behaviour; asymptotic property. The Study of DoF creates a significant method of dealing with interference which is known as IA (Interference Alignment). Matrix decoding is an audio method in which just a few separate audio channels are used. Precoding is a generalisation of beamforming used in multi-antenna wireless communication systems to facilitate inter (or multi-layer) transmission. IA is actionable to several approaches such as D2D (Device-to-device), HetNets (Heterogeneous Networks), and CR (Cognitive Radio) (Zhao et al., 2017). Based on the IA strategy, closed-form expression is used to compute decoder and precoder matrices which are difficult in most cases. The main aim of IA is to design all network decoder and precoder matrices in which each user received signal is forecast to some orthogonal space to receiver side decoder matrix. In a 5G environment, MASSIVE MIMO is the most prominent method. In this technique, BS is tutored with innumerable antenna arrays to serve 10s of UTs is the basic concept. Because the BS has a profusion of antennas, it may provide UTs spatial multiplexing with the same time-frequency, resulting in enormous spatial efficiency. Massive MIMO (multiple-input multiple-output) is a multi-user MIMO technology that may supply wireless terminal in elevated scenarios with consistently acceptable service. Massive MIMO base stations are self-contained and do not share payload data or channel condition information with neighbouring cells. It has several benefits like high energy efficiencies data rates as well as a linear transceiver model (Umer et al., 2018). In practice, to reject interference from all interferers in IA, there are three disadvantages.
For addressing problems in IA signaling overhead, and feasibility constraint clustered IA was proposed which offers extra flexibility for achievable rate optimization (Ngo et al., 2017) This paper claims the clustering-based interference alignment integrated with Adam optimizer-based convolutional neural network (CNN). Adam is a supervised neural deep instructional approach training technique that replaces stochastic gradient. Adam blends the finest features of the AdaGrad and RMSProp methods to create an optimization method for noisy issues with patchy gradient. Clustering is done using K-means clustering in a heterogeneous network. The organization of the paper is followed as related works for interference alignment are discussed in Section 2. Section 3 gives a clear explanation about the proposed network model. Experimental analysis and its discussion are done in Section 4, and then finally, Section 5 concludes the paper with future scope.