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Multifunction phased array radars (MPARs) exploit the intrinsic flexibility of their active electronically steered array (ESA) to perform, at the same time, a multitude of operations, such as search, tracking, fire control, classification, and communications. This paper aims at addressing the MPAR resource allocation so as to satisfy the quality of...
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... actual state, as well as depending on some priorities associated with each task. From a practical point of view the active ESA is composed of many tiles each with a given PAP. They are clustered according to the requirements of the system tasks so that each group realizes an overall PAP value. A pictorial description of the concept can be seen in Fig. 2. The PAP (defined as the product between the average transmitted power and the radar aperture) is considered as the limited resource that must be granted to perform the different tasks. Obviously, if the available PAP overcomes that needed to satisfy the requirements for all the active tasks, enough PAP is given to each of them. ...
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... T HE advent of integrated sensing and communication (ISAC) systems marks a transformative era in military surveillance, essential for modern warfare [1]. The integration of ground, aerial, and space networks in the sixth-generation (6G) communications is a game-changer that delivers unmatched levels of global connectivity, low-latency communication, accurate sensing capabilities, and distributed task offloading [2]- [4]. ...
This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing's parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations , we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively.
... where SNR COM k is the signal to noise ratio (SNR) at the k-th COM user receiver. Hence, indicating with R k,COM the range at which the user is located, the SNR is given by [19] ...
The modern battlefield scenario is strongly influenced by the innovative capabilities of the multifunction phased array radars (MPARs) which can perform sequentially or in parallel a plethora of sensing and communication activities. As a matter of fact, the MPAR can functionally cluster its phased array into bespoke sub-apertures implementing different tasks. Accordingly, a portion of the other available resources, e.g., bandwidth, power-aperture product (PAP), and time, is also assigned to each sub-aperture and the grand challenge is the definition of strategies for an optimal scheduling of the tasks to be executed. In this respect, a rule-based algorithm for task scheduling is proposed in this paper. In a nutshell, in each time window, the procedure first allocates the radar tasks (viz. volume search, cued search, update and confirmation tracking) and then utilize the communication (COM) looks so as to fill the empty intra-slot time left by the radar tasks. When there are two concurrent looks, the allocation is performed according to their priorities. Moreover, if the bandwidth and PAP are sufficient, some of them can be also scheduled in parallel. Interesting results in term of bandwidth and time occupancy efficiency are observed from simulations conducted in challenging scenarios comprising also multiple maneuvering targets.
For the problem of power allocation between a distributed multistatic radar network and a smart jammer, the application of non-cooperative game theory is employed to address the issue in this paper. Consequently, three scenarios of power allocation games are examined. The first two game scenarios, characterized by information asymmetry, are categorized under the Stackelberg game framework, while the final scenario, with information symmetry, is classified as a non-cooperative game. Through the power allocation analyses of the three game scenarios, it is observed that both the radar system and the jammer possess a first-mover advantage. Additionally, the existence and uniqueness of the Nash equilibrium in the games are demonstrated. Based on the best response strategies within the games, three corresponding power allocation game algorithms are proposed. Ultimately, the convergence and performance comparison of the three power allocation game algorithms are validated through simulation experiments.