Low-delay high-gain optimal multi-hop routing path is crucial to guarantee both the latency and reliability requirements for infotainment services in the high mobility internet of vehicles (IoVs) subject to queue stability. The high mobility in multi-hop IoVs reduces reliability and energy efficiency, and becomes bottleneck for the optimal route solution using classical optimization methods. To a great extent, deep reinforcement learning (DRL)-based method is not applicable in IoVs environment because of the continuously changing topology and space complexity, which grows exponentially with the number of state variables as well as the relaying hops. Usually, in multi-hop scenario, network reliability and latency are affected by mobility as well as average hop count, which limit the vehicle-to-vehicle (V2V) link connectivity. To cope with this problem, in this paper, we formulate a minimum hop count delay-sensitive buffer-aided optimization problem in a dynamic complex multi-hop vehicular topology using a digital twin-enabled dynamic coordination graph (DCG). Particularly, for the first time, a DCG-based multi-agent deep deterministic policy gradient (DCG-MADDPG) decentralized algorithm is proposed that combines the advantage of DCG and MADDPG to model continuously changing topology and find the optimal routing solutions by cooperative learning in the aforementioned communications. The proposed DCG-MADDPG coordinated learning trains each agent towards highly reliable and low latency optimal decision-making path solutions while maintaining queue stability and convergence on the way to a desired state. Experimental results reveal that the proposed coordinated learning algorithm outperforms the existing learning in terms of energy consumption and latency at less computational complexity.

Caching at the network edge can reap significant advantages to improve service quality by reducing the transmission cost and network congestion. Edge caching with device-to-device (D2D) communication helps offload cellular traffic during a surge in network traffic. This article considers the classic clustering problem for D2D users, followed by an incentive mechanism designed for successful D2D communication. The proposed clustering scheme merges D2D users with similar interests into one cluster. Specifically, a hierarchical agglomerative clustering algorithm is applied, where clusters are formed based on the similarity in users’ social interests. The cache hit probability is then optimized for each cluster, and the performance is examined for a varying number of clusters. We then propose a monetary incentive-based mechanism based on a points system to increase user participation for successful D2D communication by keeping track of a user’s content-providing history. Using this approach, devices with a good participation rate in D2D communication are identified and rewarded for their performance. Through simulations, we show that the hit rate improves by more than 40% after ensuring incentive-based methods in the D2D network.

The idea of co-operative caching in a cache-enabled wireless network has gained much interest due to its services in terms of short service delay and improved transmission rate at the user end. In this article, we consider a co-operative caching mechanism for a fog-enabled Internet of Things (IoT) network. We propose a delay-minimizing policy for fog nodes (FNs), where the goal is to reduce the service delay for the IoT nodes, also known as terminal nodes (TNs). To this end, a novel smart clustering mechanism is proposed, aiming to efficiently assign FNs to the TNs while improving the network benefit by finding a tradeoff between the delay and the network's energy consumption. We perform mathematical analysis and extensive simulations to highlight the potential gain and the proposed policy.

Optimal content caching has been an important topic in dense small cell networks. Due to spatial and temporal variation in the popularity of data, most content requests cannot be directly served by the lower tiers of the network, increasing the chances of congestion at the core network. This raises the issues of what to cache and where to cache, especially for content with different popularity patterns in a given region. In this work, we focus on the issue of redundant caching of popular files in a cluster when designing a content allocation scheme. We formulate the considered problem as a stable matching theory problem, where the preferences of each cache entity are sent to the Macro Base Station (MBS) for stable matching. The caches share their request lists with the MBS, which subsequently uses Irving One-Sided matching algorithm to generate a unique preference list for each caching entity such that every preference list is a representative of the popular data in that region. The algorithm achieves the desired goal of efficient caching with few but smartly planned repetitions of the popular files. Results show that our proposed scheme provides better performance in terms of cache hit ratio with increasing number of requests as compared to a popularity based scheme.

This paper presents an integrating concept of de-noising convolutional neural networks (DnCNN) with quadrature amplitude modulation (QAM) for symbol denoising. DnCNN is used to estimate and denoise the Gaussian noise from the received constellation symbols of QAM with unknown noise level. Proposed system shows a significant gain in terms of peak signal-to-noise ratio, system throughput and bit-error rate; in comparison with conventional QAM systems. The basic concept, system level integration, and simulated performance gains are presented to elucidate the concept.

With rapid increase in the use of traffic-intensive applications, approaches that improve users experience by reducing delay are recently receiving enormous attention. Caching in Device-to-Device (D2D) networks, in particular, is considered as an effective technique to improve the service quality of the network. In this paper, an agglomerative hierarchical clustering algorithm is proposed for a cache-assisted D2D communication network. The algorithm considers users preferences and groups them into the same cluster based on the similarity of their requested content. An optimal caching strategy has been applied and the cache hit probability has further being optimized within each cluster. Performance of the algorithm has been examined in different clusters, considering both sparse and dense user environments. Simulation results show that the cache hit probability within each cluster is higher for higher Zipf parameter, denser domains, and larger number of participating devices. The D2D cache hit probability has also been examined with changing number of clusters under a base station. In this scenario, the results show that the clustering based D2D cache hit probability is higher than the non-clustered case, and the cache hit probability increases with increasing number of clusters.