This paper presents an innovative framework that synergistically integrates energy-based generative models with morphological attention networks for robust hyperspectral image classification. Despite recent advances in data-driven approaches, existing methods struggle to model complex data distributions while preserving geometric invariance and maintaining stable representations under limited supervision. To address these challenges, we propose a novel architecture that combines three key components: (1) a morphological attention module that fuses learned structural elements with multi-head attention through parallel dilation and erosion pathways, (2) an uncertainty-aware energy-based learning paradigm that employs adaptive margin constraints and Langevin dynamics sampling, and (3) a multi-scale feature extraction block with parallel convolutional branches for dynamic spectral-spatial feature integration. Extensive experimental validation on benchmark datasets demonstrates our framework’s superior ability over state-of-the-art methods.

Due to the proliferation of devices and the availability of computing servers, mobile edge computing (MEC) has gained popularity in executing various computational tasks. MEC offers computing services at the network’s edge, providing user equipment (UE) with reduced latency for their applications. However, determining a suitable offloading policy for UEs in MEC, considering wireless resource allocation and power management, is computationally demanding. Additionally, the problem is NP-hard, making it challenging to find an optimal solution within a reasonable time frame. In this work, we propose a meta-reinforcement learning (MRL) based computational task offloading and power control mechanism for UEs in a resource-constrained environment of a MEC network to tackle the NP-hardness of the problem. We first develop an optimization problem to maximize UE computation efficiency by minimizing their power consumption for local computing and uplink transmission of UEs in the MEC network. We propose to use both binary offloading (full offloading or full local computing) and partial offloading schemes in the system. Our proposed MRL algorithm can figure out a suitable offloading policy for UEs within a short time. Unlike the traditional deep-reinforcement learning algorithms, our approach can resolve the issue of obtaining proper solutions in a new environment. Extensive simulation results prove the feasibility of our proposed work.

In the realm of modern wireless networks, the integration of wireless sensing and communication systems is pivotal, especially in the context of the forthcoming 6G Internet of Things (IoT) paradigm. The popularity of integrated sensing and communication (ISAC) stems from its potential to amplify the utilization of existing network infrastructures. This study introduces an innovative fusion of joint communication and sensing (JCAS), a framework of ISAC, combined with end-to-end (E2E) network slicing (NS) techniques. The aim is to meet user Quality-of-Service (QoS) expectations within the ambit of 6G IoT applications. The prime objective is to optimize resource allocation for communication and sensing services within a bespoke network slice tailored for 6G IoT scenarios. This is achieved by minimizing E2E system latency, essential for real-time decision making in 6G IoT environments. The optimization challenge is tackled by using deep reinforcement learning (DRL) in the form of a deep$Q$network (DQN) algorithm, which is adept at addressing nonlinear integer programming (NLIP) issues intrinsic to 6G IoT settings. Comprehensive simulations validate the approach, demonstrating its effectiveness in the context of 6G IoT networks. The amalgamation of ISAC with E2E NS emerges as a potent strategy for furnishing enhanced services customized for 6G IoT applications, successfully fulfilling consumers’ QoS requisites. This integrated approach holds substantial promise as a robust solution for addressing the exacting demands of forthcoming wireless networks, particularly those underpinned by the strides in 6G IoT technologies.

An end-to-end (E2E) network slicing (NS) scheme for heterogeneous network (HetNet) is proposed in which the number of slices and instances of various network functions (NFs) are optimized contingent on the number of users (UEs) and their quality of service (QoS) requirements. The objective of the scheme is to empower future generation networks by considering control signaling in the control plane as well as the UE traffic in the user plane of the core network (CN). We formulate a joinT UE assOciation, wiReless bandwidth allocation, sliCe formation, slice assignment, virtual network function (VNF) placement, computing resource allocation, link assignment, and link bandwidtH allocation (TORCH) problem to minimize the E2E task completion time of all UEs while considering both control signaling and UEs’ traffic. Since TORCH is a mixed-integer nonlinear problem, to tackle the problem, we decompose it into two sub-problems: the link assignment problem and the UE Association, resource allocation, Slice formation, Slice AssIgnment, and VNF pLacement (ASSAIL) problem. The ASSAIL problem comprises both the core network (CN) and radio access network (RAN), and they do not compete for resources, so we decompose it into two sub-problems: the RAN problem and the CN problem. We use Dijkstra’s algorithm and a deep Q-learning network (DQN) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the TORCH problem.

The 5G network presents a new horizon with tremendous opportunities for future generation wireless networks. Mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing (NS) are some of the key enablers for 5G. MEC reduces the latency to a great extent for a wireless network, while NOMA gives access to more users with resource constraints. NS provides users with a better quality of service and network operators with more flexibility. In this work, we propose an NS technique enabled with NOMA for a MEC network. The proposed NS technique improves service latency for MEC users and reduces unnecessary allocation of radio resources in NOMA. The saved resources can be leveraged to accommodate more users, thus increasing the spectral efficiency of the network. We consider different types of services based on the task completion time of users in this work. The primary focus is to optimize the total energy consumption for wireless uplink transmission for the NOMA-enabled sliced MEC network. We also propose a heuristic algorithm as an alternative to reduce the time and computational complexities of the optimization algorithm and simulate the results extensively to show the effectiveness of our proposed algorithm.

Meeting huge traffic demand with resource constraints imposes a significant challenge for future generation wireless networks. In this work, we propose to utilize limited resources in a dense mobile edge computing (MEC) network to compute user equipment (UE) tasks through a novel hybrid multiple access (HYMA) scheme that employs both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). The main purpose of using HYMA is to reduce the co-channel interference incurred in NOMA by selectively deploying OMA while maintaining the required signal-to-interference ratio. We adopt partial offloading of computing tasks in the MEC network. We also employ network slicing to efficiently utilize the resources of the MEC network to meet different types of application requirements. We prioritize NOMA to increase spectral efficiency as well as energy efficiency. We first formulate a mixed integer nonlinear programming (MINLP) optimization problem to minimize the total energy consumption for both local computing and wireless transmission of the MEC network and propose an algorithm consisting of three parts (UE association, computing resource allocation, and wireless resource and uplink transmission power allocation) to solve the MINLP problem with less computational complexity. We demonstrate the viability of our solution via extensive simulations.

We propose a novel multi-tier (top, intermediate, and bottom tiers) architecture at the edge of a heterogeneous network (HetNet) where non-orthogonal multiple access (NOMA) provides access to user equipment (UE) to participate in federated edge learning (FEL). The HetNet consists of a macro base station (MBS) and several small base stations (SBSs) where each BS is equipped with an edge server (ES). SBSs use the same system bandwidth to increase the system capacity. The top tier consists of the MBS-ES which works as the global model aggregator while ESs of SBSs and UEs connected with MBS reside in the intermediate tier. Similarly, UEs connected with an SBS-ES of the intermediate tier occupy the bottom tier. ESs of SBSs work as the intermediate model aggregators between the ES of the top tier and the UEs of the bottom tier. To minimize the total energy consumption (EC) for local computing (LC) and uplink transmission (UT) of UEs, we formulate a non-linear programming (NLP) optimization problem, present our solution by decomposing the problem into sub-problems, and propose two sequential algorithms to estimate EC for both LC and UT with less complexity. Our extensively simulated results demonstrate the viability of our proposed work.

This work proposes a dynamic numerology scheme assignment framework to provision mobile-edge computing (MEC) for massive Internet of Things (IoT) networks via unmanned aerial vehicles (UAVs). IoT devices (IoTDs) usually lack computational power; thus, they offload their computational tasks to MEC servers. To enhance their battery lives, an optimal assignment of a numerology scheme to each IoTD is imperative; it also enhances the system spectral efficiency. In this work, we bring MEC services closer to a massive IoT network by deploying a UAV-MEC and allocate communication resources of the sub-6-GHz band dependent on the numerology schemes to each IoTD. We formulate a multiobjective optimization problem (MOOP) with two countering objectives to maximize the uplink spectral efficiency while minimizing the IoTDs’ energy consumption. We solve a series of sequential subproblems, which are convex approximations of the MOOP and propose a novel algorithm to allocate computational resources, assign numerology schemes, and communication resources for each of the IoTDs. Our extensive simulation results validate the proposed aims herein.

Cell tower locations are not publicly available due to business interests of wireless providers. Very often wireless providers provide exaggerated coverage maps that may mislead the public. In addition to providing a neutral check on the coverage maps, prediction of cell tower locations hosting multiple operators’ access nodes could also be helpful in disaster communications and public safety in general. The localization of the disaster-affected towers can be very conducive to respond and reach to the victims. Further, victims’ devices could utilize this knowledge to initiate device-to-device (D2D) or unmanned aerial vehicular (UAV) communications as alternatives to the damaged cellular infrastructure. Publicly available crowdsourced cell (base station) locations and FCC’s sites can be used to predict the cell tower/site locations in the United States. In this work, we utilized a weighted k-means algorithm to predict cell tower locations from OpenCellid crowdsourced dataset and implemented a mapping algorithm to locate nearest physical towers. We map the predicted towers to two different sources of physical towers. Our comparison shows a significant accuracy in predicting tower locations regardless of sources of physical towers. The technique can be used to predict the tower locations in other countries as well.

Device-to-device (D2D) links enable direct communication between mobile devices without using the cellular network or the Internet. This type of communication can be helpful in situations when there is a partial or complete failure of the network infrastructure. However, effective use of D2D links for a multi-hop D2D communication system requires quick and practical quantification of their quality from user space of devices. We developed an Android application to search and measure the quality of available D2D links nearby. This application can search for heterogeneous (i.e., Bluetooth and WiFi Direct) D2D links simultaneously and measure the link quality from the user space. We run indoor and outdoor experiments to examine how line-of-sight or non-line-of-sight D2D links perform.