Ijaz Ahmad

Ijaz Ahmad, Seokjoo Shin

The traditional communication model based on chain of multiple independent processing blocks is constraint to efficiency and introduces artificial barriers. Thus, each individually optimized block does not guarantee end-to-end performance of the system. Recently, end-to-end learning of communications systems through machine learning (ML) have been proposed to optimize the system metrics jointly over all components. These methods show performance improvements but has a limitation that it requires a differentiable channel model. In this study, we have summarized the existing approaches that alleviates this problem. We believe that this study will provide better understanding of the topic and an insight into future research in this field.

Ijaz Ahmad, Seokjoo Shin

The performance of an edge detector can be improved when assisted with an effective edge map quality measure. Several evaluation methods have been proposed resulting in different performance score for the same candidate edge map. However, an effective measure is the one that can be automated and which correlates with human judgement perceived quality of the edge map. Distance-based edge map measures are widely used for assessment of edge map quality. These methods consider distance and statistical properties of edge pixels to estimate a performance score. The existing methods can be automated; however, they lack perceptual features. This paper presents edge map quality measure based on Just-Noticeable-Difference (JND) feature of human visual system, to compensate the shortcomings of distance-based edge measures. For this purpose, we have designed constant stimulus experiment to measure the JND value for two spatial alternative. Experimental results show that JND based distance calculation outperforms existing distance-based measures according to subjective evaluation.

Jude Okwuibe, Juuso Haavisto, Erkki Harjula et al.

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein.

Senthil Kumar Jagatheesaperumal, Ijaz Ahmad, Marko Höyhtyä et al.

Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network's capability to adapt to changing environments and improve the overall system's efficiency and reliability. As the demand for higher data rates and connectivity increases, B5G/6G wireless networks are expected to enable new services and applications significantly. Therefore, the significance of deep learning in addressing cognitive radio network challenges cannot be overstated. This review article provides valuable insights into potential solutions that can serve as a foundation for the development of future B5G/6G services. By leveraging the power of deep learning, cognitive radio networks can pave the way for the next generation of wireless networks capable of meeting the ever-increasing demands for higher data rates, improved reliability, and security.

Ijaz Ahmad, Seokjoo Shin

In the recent years, pixel-based perceptual algorithms have been successfully applied for privacy-preserving deep learning (DL) based applications. However, their security has been broken in subsequent works by demonstrating a chosen-plaintext attack. In this paper, we propose an efficient pixel-based perceptual encryption method. The method provides a necessary level of security while preserving the intrinsic properties of the original image. Thereby, can enable deep learning (DL) applications in the encryption domain. The method is substitution based where pixel values are XORed with a sequence (as opposed to a single value used in the existing methods) generated by a chaotic map. We have used logistic maps for their low computational requirements. In addition, to compensate for any inefficiency because of the logistic maps, we use a second key to shuffle the sequence. We have compared the proposed method in terms of encryption efficiency and classification accuracy of the DL models on them. We have validated the proposed method with CIFAR datasets. The analysis shows that when classification is performed on the cipher images, the model preserves accuracy of the existing methods while provides better security.

Ijaz Ahmad, Ilkka Harjula, Jarno Pinola

5G is enabling different services over the same physical infrastructure through the concepts and technologies of virtualization, softwarization, slicing and cloud computing. Virtual Mobile Networks (VMNs), using these concepts, provide an opportunity to share the same physical infrastructure among multiple operators. Each VMN Operator (VMNO) can have own distinct operating and support systems. However, the technologies used to enable VMNs have their own explicit security challenges and solutions. The integrated environment built upon virtualization, softwarization, and cloudification, thus, will have complex security requirements and implications. In this vain, this article provides an overview of the security challenges and potential solutions for VMNs.

Ijaz Ahmad

Traditional communication networks consist of large sets of vendor-specific manually configurable devices which are hardwired with specific control logic or algorithms. The resulting networks comprise distributed control plane architectures that are complex in nature, difficult to integrate and operate, and are least efficient in terms of resource usage. However, the rapid increase in data traffic requires an integrated use of diverse access technologies and autonomic network operations with increased efficiency. Therefore, the concepts of Software Defined Networking (SDN) are proposed that decouple the network control plane from the data-forwarding plane. The SDN control plane can integrate a diverse set of devices, and tune them at run-time through vendor-agnostic programmable Application Programming Interfaces (APIs). This thesis proposes software defined cognitive networking to enable intelligent use of network resources. Different radio access technologies, including cognitive radios, are integrated through a common control platform to increase the overall network performance. The architectural framework of software defined cognitive networking is presented alongside the experimental performance evaluation. Since SDN enables applications to change the network behavior and centralizes the network control plane to oversee the whole network, it is highly important to investigate security of SDNs. Therefore, this thesis finds potential security vulnerabilities in SDN, studies proposed security platforms and architectures for those vulnerabilities, and presents future directions for unresolved security vulnerabilities. Furthermore, this thesis also investigates the potential security challenges and their solutions for the enabling technologies of 5G, such as SDN, cloud technologies, and virtual network functions, and provides key insights into increasing the security of 5G networks.

Ijaz Ahmad, Shahriar Shahabuddin, Tanesh Kumar et al.

The Internet of Things (IoT), hailed as the enabler of the next industrial revolution, will require ubiquitous connectivity, context-aware and dynamic service mobility, and extreme security through the wireless network infrastructure. Artificial Intelligence (AI), thus, will play a major role in the underlying network infrastructure. However, a number of challenges will surface while using the concepts, tools and algorithms of AI in wireless networks used by IoT. In this article, the main challenges in using AI in the wireless network infrastructure that facilitate end-to-end IoT communication are highlighted with potential generalized solution and future research directions.