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The leitmotif of this paper aims at providing an architecture for the age of information (AoI) and cache-assisted hybrid multicast/unicast/device-to-device (D2D) transmission with the promising cell-free massive multiple-input multiple-output (MIMO) technology, for enriching data transmission rate, slackening the burden of high-volume data traffic,...
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... data rates and quality of service (QoS) provisioning [2], [3]. An important feature of the forthcoming 6G is that it provides a massive significant of miscellaneous devices and bears a large amount of data volume. To solve this issue effectively, various innovation directions for diverse OSI levels are conducted in full swing as exhibited in Fig. 3. From the perspective of the OSI stack, in this section, the driving factors for the proposed architecture are ...
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p>Future wireless systems are envisioned to create an endogenously holography-capable, intelligent, and programmable radio propagation environment, that will offer unprecedented capabilities for high spectral and energy efficiency, low latency, and massive connectivity. A potential and promising technology for supporting the expected extreme requir...
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... In the second phase, the UE's data demands can be fulfilled through the cache resources if the requested content is available in the cache. The ability to access data via cache resources can provide robustness and flexibility in data access [15,16]. Moreover, caching content close to the users offers dual benefits. ...
Data caching is a promising technique to alleviate the data traffic burden from the backhaul and minimize data access delay. However, the cache capacity constraint poses a significant challenge to obtaining content through the cache resource that degrades the caching performance. In this paper, we propose a novel two-tier caching mechanism for data caching on mobile user equipment (UE) and the small base station (SBS) level in ultra-dense 6G heterogeneous networks for reducing data access failure via cache resources. The two-tier caching enables users to retrieve their desired content from cache resources through device-to-device (D2D) communications from neighboring users or the serving SBS. The cache-enabled UE exploits millimeter-wave (mmWave)-based D2D communications, utilizing line-of-sight (LoS) links for high-speed data transmission to content-demanding mobile UE within a limited connection time. In the event of D2D communication failures, a dual-mode hybrid system, combining mmWave and microwave μWave technologies, is utilized to ensure effective data transmission between the SBS and UE to fulfill users’ data demands. In the proposed framework. the data transmission speed is optimized through mmWave signals in line-of-sight (LoS) conditions. In non-LoS scenarios, the system switches to μWave mode for obstacle-penetrating signal transmission. Subsequently, we propose a reinforcement learning (RL) approach to optimize cache decisions through the approximation of the Q action-value function. The proposed technique undergoes iterative learning, adapting to dynamic network conditions to enhance the content placement policy and minimize delay. Extensive simulations demonstrate the efficiency of our proposed approach in significantly reducing network delay compared with benchmark schemes.
... In addition, with the wide application of game theory and reinforcement learning in various fields, they have also been applied to solving AoI-related problems. A content resale problem is discussed in [10] which provides a hybrid multicast/unicast/D2D transmission architecture oriented towards age of information and cache assistance to increase the data transmission rate, reduce the burden of large data traffic, and improve system efficiency, where the problem is decomposed into two subproblems and the subproblems are solved through the Stackelberg game and auction framework, respectively. Reinforcement learning is used in the literature [11] to investigate the best update strategy for the age of information (AoI) and the urgency of information (UoI) of real-time status information based on resource constraints. ...
Regarding the issue of information freshness in systems that aid in data collection using unmanned aerial vehicles (UAVs), a data collection algorithm that is based on freshness and UAV assistance is proposed. Under the limitations of wireless sensor node communication distance and UAV parameters, the optimization problem of minimizing the average spatial correlation age of information (SCAoI) of all nodes in the area is set up. This problem is solved by optimizing the number of clusters, UAV flight trajectories, and the order of data collection from cluster member nodes. The maximum communication distance of the nodes is used as the cluster formation radius, and the maximum-minimum distance clustering algorithm is used to cluster the nodes in the region to obtain the minimum number of clusters. After it has been proven that the trajectory optimization problem in this study is NP-hard, the ant colony algorithm is applied to obtain the minimum flight time and the corresponding trajectory. By using the greedy algorithm to determine the member nodes in the sequence of data collection for a cluster, the instantaneous SCAoI of the UAV arriving at the cluster head is solved. Simulation results show that the proposed algorithm in this paper can effectively improve the freshness of data and reduce the average SCAoI of the system compared with the algorithm in the comparative literature, reducing the average SCAoI by about 61%.
