###### Abstract  
In this paper, a cognitive radio engine platform is proposed for exploiting available frequency channels for a tactical wireless sensor network while aiming to protect incumbent communication devices, known as the primary user (PU), from undesired harmful interference. In the field of tactical communication networks, there is an urgent need to identify available frequencies for opportunistic and dynamic access to channels on which the PU is active. This paper introduces a cognitive engine platform for determining the available channels on the basis of a case-based reasoning technique deployable as a core functionality on a cognitive radio engine to enable dynamic spectrum access (DSA) with high fidelity. To this end, a plausible learning engine to characterize the channel usage pattern is introduced to extract the best channel candidate for the tactical cognitive radio node (TCRN). The performance of the proposed cognitive engine was verified by simulation tests that confirmed the reliability of the functional aspect, which includes the learning engine, as well as the case-based reasoning engine. Moreover, the efficacy of the TCRN with regard to the avoidance of collision with the PU operation, considered the etiquette secondary user (SU), was demonstrated. t actical cognitive radio sensor network; case-based reasoning; cognitive radio engine; channel occupancy probability; military tactical communications [1] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32]  
be fulfilled, subject to the satisfaction of stable management and quality of service (QoS) requirements. In this context, this paper proposes a cognitive radio engine platform that embeds a learning engine and a case-based reasoning engine. The proposed platform is capable of effective frequency allocation without producing harmful interference, which is a prime concern for the secure coexistence of tactical cognitive radio nodes (TCRNs) in the presence of a primary user (PU) in a military tactical wireless communication environment. Figure 1 shows the future military spectrum management environment to support the network-centric warfare. There is a relevant study on a cognitive radio (CR) engine that is similar to the one proposed in this paper, which presents a technique for the dynamic exploitation of available channels not occupied by the primary user for the CR node user by observing the channel environment via spectrum sensing [7]. This paper shows the method's ability to avoid collision with the primary user on the specific channel currently used by the CR node user. However, the exclusion of a learning engine means that if the primary user utilizes frequency hopping (FH), the reliability of the spectrum sensing is naturally lowered because the hopping pattern is not acquired, so the chance of collision between the primary user and the CR node becomes obviously considerable. Moreover, the next-generation (XG) project conducted by the Defense Advanced Research projects Agency (DARPA) for the performance evaluation of the CR engine was carried out as a field test that varied the primary user's channel utilization [8]. A primary traffic pattern was generated on the basis of mathematical modeling, and the CR-based routing protocol was tested to determine whether it was applicable to an ad hoc network environment [9]. This paper focuses on the application scenarios, design methods, and implementation of the CR engine. The CR engine platform proposed in this paper consists of a cognitive engine, learning engine, reasoning engine, and optimization engine. We also designed a function and operation method for the individual engines and then conducted a performance analysis through simulation. This paper introduces a PU traffic model, which describes the activity pattern of a PU channel occupancy and proposes a case-based reasoning method deployable to tactical sensor networks.