###### Abstract
Blockchains are further characterized by high persistence, as there is less chance for falsification or high fault recovery capability (because several nodes have copies of the same legitimate transaction or block in their possession), and high transparency, as each user has similar rights and access to the blockchain network [6]. However, blockchain cryptographic vulnerabilities still exist where an attacker can use a side channel attack to leak sensitive information. Furthermore, blockchains are well known for their high propagation delay due to the distributed consensus approach consisting of validation and transmission of transactions; thus, it is advisable to select a consensus approach after a performance analysis [7]. Moreover, they are also susceptible to decentralized refusal of service attacks, which can overwhelm the connections to the point where resources are inaccessible to authorized users [8]. To enhance the functionality of cognitive networks, it has been suggested that blockchain-based approaches be combined with AI and ML techniques. The authors in [9] argue that a smart city can achieve more sustainability with the fusion of blockchain and AI. Furthermore, AI has been proposed to deliver error-free smart contracts for blockchain 2.0, as opposed to the smart contracts developed manually by human programmers, which can contain loopholes and flaws [10]. Blockchain has the potential to be utilized in the medical field to build reliable artificially intelligent models and to display medical records using blockchain to improve service efficiency and reduce costs [11]. Furthermore, the strengths of blockchain for privacy preservation, the capacity to manage massive data amounts, and the ability to cope with the computing demands of intelligent networks have been discussed in [12]. Likewise, deep extreme machine learning has been utilized in smart home architecture along with blockchain to ensure the security goals of integrity and privacy [13]. Some researchers have integrated AI and blockchain to achieve energy-efficient and secure routing, which obviates proof-of-work consensus for control plane communication [14]. However, there are challenges in applying blockchain technology in cognitive networks, such as high energy consumption, resource management difficulties, a lack of standardization and scalability, increased latency, limited throughput, etc., as discussed in detail in this survey (in Section 6.2). Because the controller continuously gathers network usage statistics, Software-Defined Networking (SDN) has an overall overview of the network as a whole and performs traffic manipulation more effectively than network connectivity based on conventional hardware [15]. Moreover, by isolating the primary network governing functionality from the routing components and providing logical network control centralization as opposed to ordinary networking, where the control layer is closely connected with end devices, SDN enables the programming of networks [16]. Due to the conceptual divorcing of the control layer, SDN's three layers--the data/infrastructure layer, control layer, and application layer--offer benefits of agility and network programming capability. Thus, the network's broad perspective makes it feasible to perform dynamic energy assignment, load balancing, motion governance, and other functions [17]. Additionally, network analytics gathering also makes it possible to experiment with and apply innovative protocols more affordably [18]. Additionally, the control plane is able to relate with all of the data plane's components by using a southbound interface. Services like virtual networking, cloud-based services, big data, etc. are possible thanks to SDN [19]. The three modes of control of SDN are combined, decentralized, and centralized, whereas the single controller model has complete logical and physical centralization and explicitly stated flow rules for the data plane elements [20].