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The advent of the Internet of Things and 5G has further accelerated the growth in devices attempting to gain access to the wireless spectrum. A consequence of this has been the commensurate growth in spectrum conflict and congestion across the wireless spectrum, which has begun to impose a significant impost upon innovation in both the public and private sectors. One potential avenue for resolving these issues, and improving the efficiency of spectrum utilisation can be found in devices making intelligent decisions about their access to spectrum through Dynamic Spectrum Allocation. Changing to a system of Dynamic Spectrum Allocation would require the development of complex and sophisticated inference frameworks, that would be able to be deployed at a scale able to support significant numbers of devices. The development and deployment of these systems cannot exist in isolation, but rather would require the development of tools that can simulate, measure, and predict Spectral Occupancy. To support the development such tools, this work reviews not just the available prediction frameworks for networked systems with sparse sensing over large scale geospatial environments, but also holistically considers the myriad of technological approaches required to support Dynamic Spectrum Allocation.

The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra’s shortest path searching is utilized to accelerate the process of routing in large-scale multi-domain elastic optical networks (EONs). The layered-graph approach is adopted in the spectrum allocation stage. To this end, an efficient heuristic algorithm is proposed, called “Branch-and-Bound based Routing and Layered Graph based Spectrum Allocation algorithm (BBR-LGSA)”, which is an integrated RSA algorithm. Notably, the significant reduction in algorithm time complexity is not only reflected in the pruning method used in the routing stage but also in the construction of auxiliary graphs during the spectrum allocation stage utilizing the Branch-and-Bound method. Simulation results show that the proposed BBR-LGSA significantly reduces the average running time by nearly 78% with higher spectrum utilization in large-scale multi-domain EONs, compared with benchmark algorithms. In addition, the impact of key parameters on performance comparisons of different algorithms is evaluated.

Elastic Optical Network (EON) is gaining a lot of attention these days because of its capability to dynamically utilize the network resources. It is possible to alter data rates in accordance with the required application by adjusting a number of spectrum slots which enhances spectral efficiency. However, it is difficult for new requests to access the usage of spectrum resources during the entire process especially when the spectrum is split into a number of small segments. Another key challenge in EON is to achieve effective routing and spectrum allocation. This entails looking for a specified route and identifying contiguous aligned spectrum slots for allocation to various requests. In this way, EON aims maximizing the connection while using the least amount of available spectrum resources. With this in mind, this research developed a first random fit (FRF) spectrum allocation technique that takes into account dispersion, which is a major physical flaw in optical networks. This scheme includes the effect of dispersion for the spectrum allocation approach. Using a FRF approach, this scheme tends to allocate a greater number of connection requests and spectrum is utilized in more efficient way. Starting with the lowest indexed slot, this scheme uses the First Fit algorithm and the less stable modulation technique like BPSK to search for and assign the spectrum slot. A random fit assignment strategy is used with a more robust modulation technique (QPSK) to exploit the next higher indexed spectrum slot which suffers larger dispersion effect. Longer light path requests are assigned to lower indexed spectrum slot (lower dispersion slots) using BPSK modulation technique, while shorter light path requests are sent to next higher indexed spectrum slot (greater dispersion slots) using QPSK modulation technique capturing the dispersion sensitive technique. The results show that the proposed technique achieves a minimal bandwidth blocking probability (BBP) of 0.048, Eye opening Penalty (EOP) of 45.45 dB and Quality Factor of 3.889 and B.P compared with other First Fit (FF) and Random Fit (RF) spectrum allocation schemes.

With the rapid development of mobile Internet, high-definition video and cloud computing, users’ bandwidth demands are not only larger and larger but also more and more diverse. To solve this problem, there searchers put forward the concept of elastic optical network (EON). EON adopts the transmission mode of elastic grid, which can allocate spectrum resources flexibly and meet high bandwidth and diversity requirements at the same time. Routing and spectrum allocation (RSA) is an important issue in EON. In this paper, we present a heuristic algorithm named constrained-lower-indexed-block (CLIB) allocation algorithm for the RSA problem. The algorithm is based on the candidate paths. When there are available spectrum blocks on multiple candidate paths, if the increase of the path length does not exceed a given threshold, the lower index spectrum would be selected for the connection request on a longer path. The aim of the algorithm is to concentrate the occupied frequency slices on one side of the spectrum and leave another side of the spectrum to the later arrived connection requests as much as possible, to reduce the blocking probability of connection requests. Simulation results show that comparing with the first-last-fit and hybrid grouping algorithms, the CLIB algorithm can reduce the blocking probability of connection requests.

To leverage the existing spectrum and mitigate the global spectrum dearth, the Federal Communications Commission of the United States has recently opened the Citizens Broadband Radio Service (CBRS) spectrum, spanning 3550–3700 MHz, for commercial cognitive operations. The CBRS has a three-tier hierarchical architecture, wherein the incumbents, including military radars, occupy the topmost tier. The priority access licenses (PAL) and general authorized access (GAA) are second and third tier, respectively, facilitating licensed and unlicensed access to the spectrum. This combination of licensed and unlicensed access to the spectrum in a three-tier model has opened novel research directions in optimal spectrum sharing as well as privacy preservation, and hence, several schemes have been proposed for the same. This article provides a detailed survey of the existing literature on the CBRS. We provide an overview of the CBRS ecosystem and discuss the regulation and standardization process and industrial developments on the CBRS. The existing schemes for optimal spectrum sharing and resource allocation in CBRS are discussed in detail. Further, an in-depth study of the existing literature on the privacy of incumbents, PAL devices, and GAA devices in CBRS is presented. Finally, we discuss the open issues in CBRS, which demand more attention and effort.

Sixth-generation (6 G) wireless networks demand cognitive radio systems that simultaneously achieve sub-millisecond latency and sustainable energy consumption – requirements conventional artificial intelligence approaches cannot meet. This paper presents a hardware-software co-design framework integrating neuromorphic computing with cognitive radio to address both constraints through brain-inspired spiking neural networks (SNNs). We systematically analyze five neuromorphic platforms – Intel Loihi 2, IBM TrueNorth, SpiNNaker, SpiNNaker 2, and Intel Hala Point – using standardized benchmarks from the Intel Neuromorphic Deep Noise Suppression (N-DNS) Challenge, demonstrating sub-millisecond spectrum decisions (50-170 s end-to-end latency) with energy consumption reduced by 100-1000 (31 pJ per spike) compared to conventional GPU-based approaches (2.5−12.5 J per operation). Our framework provides three novel contributions: (1) a unified co-design methodology optimizing spike encoding, network topology, and hardware mapping jointly to achieve 3 efficiency gains over independent optimization; (2) quantitative design rules for encoding selection – rate coding for signal-to-noise ratios below -10 dB, temporal coding for latency requirements below 100 s, and population coding for reliability exceeding 99.9%; and (3) experimental validation achieving 97.6% classification accuracy on real-world spectrum data from industrial IoT deployments consuming only 31 mW average power. Through five detailed case studies spanning industrial automation (99.9% uptime over 6 months), vehicle-to-everything communications (98.7% collision avoidance), defense applications (95% reliability under 40 dB jamming), smart cities (100,000 sensors), and healthcare (15-year implant lifetime), we demonstrate neuromorphic cognitive radio’s practical viability. The framework addresses critical deployment barriers including device variability mitigation (±20% threshold compensation), cross-platform algorithm portability, and RF-to-spike conversion interfaces. These results establish neuromorphic computing as a foundational technology for energy-constrained, latency-critical 6 G wireless systems, with implications extending to radar processing, electronic warfare, and satellite communications.

Elastic optical networks (EONs) offer tremendous benefits to deal with the exponential increase of the data center traffic. The granularity offered in spectrum allocation supports efficient management of available bandwidth and accommodates multiple traffic to be routed through common links. However, this brings the inherent challenges of routing and spectrum allocation (RSA) constraints. This becomes more complex for elastic optical data center networks (EODCNs), wherein multiple requests arrive at the same time, requiring identical or different bandwidths and each request may have the same or different destination and paths. Also, data requested by different users could be of varying importance levels. Under such a scenario, maintaining the quality of service (QoS) by minimizing the probability of traffic failure and bandwidth blocking is a major task for service providers. To address these problems, we propose an enhanced methodology using path prediction and link-state analysis for efficient allocation of frequency slots and reuse of bandwidth for data centers connected through EONs. Our proposed strategy intents to minimize the number of blocked requests due to non-availability of resources and reduce the failure probability. We introduce here the concept of connectivity degree and Kuhn-Munkres multi-objective optimization for spectrum allocation. We also evaluate the call request blocking probability varying the number of data centers and traffic load. The obtained results show that the proposed algorithm is highly effective in reducing the traffic failure and blocking probability for EODCNs.

In this paper, we propose a grouping-based dynamic routing, core, and spectrum allocation (RCSA) method for preventing spectrum fragmentation and inter-core crosstalk in elastic optical path networks based on multi-core fiber environments. Multi-core fibers enable us to considerably enhance the transmission capacity of optical links; however, this induces inter-core crosstalk, which degrades the quality of optical signals. We should thus avoid using the same frequency bands in adjacent cores in order to ensure high-quality communications. However, this simple strategy leads to inefficient use of frequency-spectrum resources, resulting in spectrum fragmentation and a high blocking probability for lightpath establishment. The proposed method allows one to overcome this difficulty by grouping lightpath-setup requests according to their required number of frequency slots. By assigning lightpath-setup requests belonging to the same group to cores according to their priority, the proposed method aims to suppress inter-core crosstalk. Furthermore, the proposed method is designed to mitigate spectrum fragmentation by determining the prioritized frequency bandwidth for lightpath-setup requests according to their required number of frequency slots. We show that the proposed method reduces the blocking of lightpath establishment while suppressing inter-core crosstalk through simulation experiments.

The object of this research is resource management and energy consumption processes in optical fiber communication networks with access–metro–core architectures. The study addresses the problem that conventional static and semi-dynamic control methods are unable to simultaneously ensure energy efficiency and QoS stability under conditions of exponentially growing and highly variable traffic. To solve this problem, an AI-based integrated control model was developed that combines traffic prediction, dynamic resource allocation, spectrum management, and power optimization within a unified framework. Traffic prediction is performed using LSTM–BiRNN neural networks (1.2–1.8 million parameters, 300–500 thousand records), while control decisions are generated by an Actor–Critic reinforcement learning algorithm. Simulation results obtained in the Python 3.12 and OptiSystem 17.0 environments demonstrate that, in the Access segment (1–10 Gb/s), latency is stabilized within 1–10 ms; in the Metro segment (40–120 Gb/s), energy consumption is reduced by 18–27%; and in the Core segment (400–1000 Gb/s), the efficiency of RSA algorithms increases by 22–35%. When the EDFA output power is maintained within +17 to +23 dBm, amplifier power consumption decreases by 10–15%, resulting in overall network energy savings of 20–40%. The obtained results are explained by the synergy of accurate traffic prediction provided by the LSTM–BiRNN model and proactive real-time decision-making enabled by the Actor–Critic algorithm. The distinctive feature of the proposed approach is the simultaneous optimization of energy efficiency and QoS across all access, metro, and core segments within a single integrated architecture. The results can be practically applied in the design and modernization of optical fiber communication networks, as well as in the deployment of energy-efficient intelligent network management systems.

The C+L band elastic optical networks (C+L-EONs) increase the network capacity significantly. However, the introduction of an L band enhances the inter-channel stimulated Raman scattering effect (ISRS), consequently deteriorating the quality of transmission (QoT) of the signal. Furthermore, spectrum allocation leads to spectrum fragmentation inevitably, which escalates the bandwidth blocking rate. In addition, in C+L-EONs, a single fiber carries more services, and once one of the links fails, a huge number of requests will be interrupted, resulting in huge economic losses. Therefore, this paper proposes a survivability routing, band, modulation, and spectrum allocation (RBMSA) algorithm that effectively guarantees service survivability and reduces the impact of ISRS and spectrum fragmentation. The algorithm employs shared backup path protection and a band partitioning method, whereby the spectrum resource of the primary path is assigned in the L band and the backup path is assigned in the C band in order to minimize the impact of ISRS on the QoT of the request while ensuring the survivability of the network. Furthermore, a fragmentation metric accounting for both the free and shared spectrum resource is proposed to mitigate both free and shared spectrum fragmentation. The simulation results reveal that the proposed RBMSA algorithm reduces the bandwidth blocking probability (BBP) and the fragmentation rate (FR) by 47.7% and 21.3%, respectively, and improves the optical signal-to-noise ratio (OSNR) by 4.17 dB in NSFNET. In COST239, the BBP, FR, and OSNR are 22.1%, 21.5%, and 4.71 dB, respectively.