Jul 2026· International Conference Computing Methodologies and Communication· pp. 336-341· 0 citations· 19 references
Abstract
The wildest boom of wireless devices and the shift to 5G/6G ecosystems contributed to the lack of the spectrum, making the old traditional methods of static allocation less and less efficient. cognitive radio networks provide an alternative with dynamic nature, the current solutions tend to fail because of the sophisticated nature of imperfect channel state information, large-dimensional state space and the rapid mobility of users. This model presents an Attention-Augmented Multi-agent Deep Reinforcement Learning model, which is used to maximize autonomous spectrum sharing by using spatial-temporal awareness. The architecture is based on convolutional neural networks in mapping spatial interference and long short-term memory layers in temporal mobility tracking with a multi-head self-attention mechanism to coordinate interference management between secondary users. To obtain accurate resource mapping, layers of Sinkhorn are incorporated to be bi-stochastic. Simulation shows that the spectral efficiency is 22.14% higher and the collision rate is also 6.82 times lower with the 3GPP channel models than with regular deep Q-networks. The system can be 85.36% efficient even in the presence of serious channel state errors, which is a strong solution to ensure trustworthy ultra-dense urban connectivity.
A Deep Reinforcement Learning (DRL)-based framework for dynamic spectrum access in 6G heterogeneous Cognitive Radio Networks (Het-CRNs), wherein secondary users learn optimal channel selection policies through direct interaction with the radio environment, without requiring explicit statistical channel models is proposed.
Naadir Kamal, R. Kumar· Global Journal of Engineerin...· 0 citations
With the advent of seventh-generation (7G) wireless systems, the spectrum environment is extremely dynamic and heterogeneous, and traditional methods of sensing do not offer reliable and efficient performance. This paper introduces a self-evolving spectrum sensing system to enable adaptive and intelligent spectrum access based on a deep reinforcement-based learning paradigm. The framework depicts the sensing process as a sequence decision problem where an autonomous agent is able to continually refine its policy as it engages with the environment. The multi-objective reward formulation is designed to maximize the combination of the detection accuracy, false alarms, energy usage, and using the spectrum. Moreover, an adaptive representation of state mechanism is also introduced to indicate the temporal changes and short-term changes in the spectrum occupancy. The self-evolution strategy proposed adjusts learning parameters and decision policies in a dynamic manner that gives a robust operating in a non-stationary environment. The overall analysis of the experiment shows that the structure achieves detection probability of 97.1, false alarm rate reduced to minimum of 3.8, spectral usage maximized to above 92 and convergence rate is quicker as compared to the existing techniques. These results confirm the appropriateness of the proposed method in overcoming the issues of the next-generation wireless systems.
A.L Sriram, H. N. Divya, S. Sabarinathan et al.· International Conference on...· 0 citations
The impending deployment of autonomous vehicles has added new requirements about wireless communication in highly dynamic vehicular networks, particularly in terms of reliability and low latency. The allocation of the spectrum is still a difficult problem, as traffic and mobility conditions change continuously, which implies the variation in vehicle density, channel quality, interference, and communication requirements. These variations are not easily handled by the conventional static, rule-based and non-adaptive allocation schemes, affecting the efficient use of the spectrum, communication latency, and packet delivery reliability. To overcome these challenges, this study introduces an Adaptive Deep Reinforcement Learning (ADRL) based dynamic spectrum allocation framework for AVNs. The proposed framework uses a Deep Q-Network (DQN) that, based on observation of the current conditions of the vehicular network (vehicle density, channel quality, level of interference, load of the traffic flow), automatically determines the appropriate spectrum resources. A simulation-based vehicular communication scenario is created to evaluate under various traffic densities and channel-quality levels with dynamic mobility and channel conditions. Experimental analysis takes into account the following factors: spectral efficiency, spectrum utilization, communication latency, packet delivery ratio, reliability, and learning convergence. Results show that the proposed ADRL framework can ensure efficient spectrum allocation and reliable communication in a fast-growing network density and degraded channel environment and has stable convergence characteristics in its training behavior. The results show that adaptive deep reinforcement learning is an effective method to spectrum management in the next generation of autonomous vehicular communication networks that is robust, resource-efficient, and has low latency.
The ability of various isolated devices to sense their surroundings can be improved by 5G millimetre wave (mmWave) communication technology. By jointly supporting data transmission and sensing tasks, the framework improves overall spectrum efficiency in wireless networks. Among them, the Integrated Sensing and Communication (ISAC) has become the standard in wireless communications. Specifically, mmWave technology is highly effective for bandwidth-intensive communication services and delivers improved spatial and temporal accuracy through its large spectrum availability and directional beamforming characteristics. To meet the requirements, a multi-agent-based deep learning technique is proposed for better development. Over this sensing network of 5G mmWave, the resource allocation process is handled by Multi-agent Deep Reinforcement Learning with Prioritized Experience Replay (MDRL-PER), whereas the system is provided based on allocated resource for better communication. Finally, the performance of the system is assessed through distinct evaluation metrics and compared with existing methodologies. Hence, the superior results are obtained to ensure the efficacy of the communication network.
Papisetty Sai Prasad, T. Kavitha· 2026 7th International Confe...· 0 citations
Channel selection is a dynamic and challenging issue in the present wireless network that is shared and suffers interference in a spectrum that is not fully controlled. The traditional heuristic and greedy methods depend on immediate channel measurements and they are not always adapted to non-stationary environments resulting in poor throughput, high packet error rates and unpredictable channel switching behaviour. As a remedy to these shortcomings, this paper will suggest a deep reinforcement learning-based system to perform dynamic channel selection, which provides the formulation of the problem as a Markov Decision Process and uses a Deep Q-Network to train the optimal long-term policies of spectrum access. The given strategy combines spectrum sensing, contextual state representation, and reward-based learning to make adaptive and stable choices to select the channel. The reward function explicitly includes a switching cost in order to strike a balance between the adaptability and the stability. Numerous simulation-based tests have shown that the given technique replicates the best results in terms of mean throughput, packet error rate, and frequency of channel switching, compared to the random, greedy and bandit-based baseline schemes. The findings validate the outcomes of deep reinforcement learning in learning temporal spectrum dynamics and maximizing the long-term communication performance in non-stationary wireless conditions.
D. Shende, Dr. Amruta Nagesh Chitari, Dr. Pooja Mishra et al.· Journal of Intelligent Decis...· 0 citations
This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations
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