To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks ...in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.
Although many proposals have been developed for the sixth-generation (6G) technology, realizing 6G is fraught with numerous fundamental interdisciplinary, multidisciplinary, and transdisciplinary ...challenges. To mitigate some of these challenges, goal-oriented semantic communication (SemCom) has emerged as a promising 6G technology enabler. This enabler employs only semantically-relevant information for successful task execution while minimizing power usage, bandwidth consumption, and transmission delay. On the other hand, 6G is essential for realizing major goal-oriented SemCom use cases such as autonomous transportation. These paradigms of 6G for goal-oriented SemCom and goal-oriented SemCom for 6G call for a tighter integration of 6G and goal-oriented SemCom. To facilitate this purpose, this survey paper exposes the fundamental challenges of 6G; details the notion of goal-oriented SemCom and its state-of-the-art research landscape; presents state-of-the-art trends, use cases, and frameworks of goal-oriented SemCom; exposes the fundamental and major challenges of goal-oriented SemCom; and offers promising future research directions for goal-oriented SemCom. Consequently, this survey article stimulates numerous lines of research on goal-oriented SemCom theories, algorithms, and realization.
In the last years, many European countries have experienced the effects of climate change, in the form of a scarcity of drinking water resources, prolonged periods of drought, and extremely heavy ...rainfall, with unprecedented dramatic environmental, economic, and social costs. Therefore, understanding, modeling, and predicting the movement and distribution of water on Earth, and effectively managing water resources are problems of paramount importance. In this article, we discuss the fundamental role that sensing technologies, data processing algorithms, and inference based on machine learning techniques can have in modern hydrology and the many challenges that still need to be addressed to improve the accuracy and reduce the complexity of current hydrology models. More specifically, we overview the main solutions proposed in the literature to monitor, analyze and predict hydrological processes, and present a selection of results obtained from empirical data sets to ground the main concepts and substantiate the dissertation. Finally, we conclude our article by discussing open problems and possible avenues for future research.
The ongoing deployment of 5G cellular systems is continuously exposing the inherent limitations of this system, compared to its original premise as an enabler for Internet of Everything applications. ...These 5G drawbacks are spurring worldwide activities focused on defining the next-generation 6G wireless system that can truly integrate far-reaching applications ranging from autonomous systems to extended reality. Despite recent 6G initiatives (one example is the 6Genesis project in Finland), the fundamental architectural and performance components of 6G remain largely undefined. In this article, we present a holistic, forward-looking vision that defines the tenets of a 6G system. We opine that 6G will not be a mere exploration of more spectrum at high-frequency bands, but it will rather be a convergence of upcoming technological trends driven by exciting, underlying services. In this regard, we first identify the primary drivers of 6G systems, in terms of applications and accompanying technological trends. Then, we propose a new set of service classes and expose their target 6G performance requirements. We then identify the enabling technologies for the introduced 6G services and outline a comprehensive research agenda that leverages those technologies. We conclude by providing concrete recommendations for the roadmap toward 6G. Ultimately, the intent of this article is to serve as a basis for stimulating more out-of-the-box research around 6G.
Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) ...are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: Velocity, voracity, volume, and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul of-floadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4 Gbyte of storage size (87% of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.
In this paper, we investigate non-cooperative radio resource management in a vehicle-to-vehicle communication network. The technical challenges lie in high-vehicle mobility and data traffic ...variations. Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued data packets, aiming to optimize the expected long-term performance. The frequency allocation at the beginning of each slot at the RSU is regulated by a sealed second-price auction. Such interactions among VUE-pairs are modeled as a stochastic game with a semi-continuous global network state space. By defining a partitioned control policy, we transform the original game into an equivalent stochastic game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium, which characterizes the optimal solution to the equivalent game. The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE solution. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.
Collaborative machine learning is considered as the bedrock of the intelligent B5G networks, where distributed agents collaborate with each other to train learning models in a distributed fashion, ...without sharing data at a central entity. Despite its broad applicability, the main issue of collaborative learning is the need of local computing to build local learning models as well as iterative information exchange among agents, which may lead to high resource overhead unaffordable in many practical resource-limited systems such as unmanned aerial vehicles (UAVs) and Internet of Things (IoT). To alleviate this resource issue, it is essential to devise resource-efficient collaborative learning techniques, that can optimize the resource overhead in terms of communication, computing, and energy cost, and hence achieve satisfactory optimization/learning performance simultaneously.
Software-defined networking (SDN) provides an agile and programmable way to optimize radio access networks via a control-data plane separation. Nevertheless, reaping the benefits of wireless SDN ...hinges on making optimal use of the limited wireless fronthaul capacity. In this paper, the problem of fronthaul-aware resource allocation and user scheduling is studied. To this end, a two-timescale fronthaul-aware SDN control mechanism is proposed in which the controller maximizes the time-averaged network throughput by enforcing a coarse correlated equilibrium in the long timescale. Subsequently, leveraging the controller's recommendations, each base station schedules its users using Lyapunov stochastic optimization in the short timescale, i.e., at each time slot. Simulation results show that significant network throughput enhancements and up to 40% latency reduction are achieved with the aid of the SDN controller. Moreover, the gains are more pronounced for denser network deployments.
This study demonstrates the feasibility of image inpainting using both visual information and radio frequency (RF) signals. Recent developments in imaging and vision-based technologies using RF ...signals have revealed the potential of leveraging multimodal information to enhance image inpainting performance. In this context, we propose RF-Inpainter-a novel inpainting method that integrates visual and wireless information by fusing defective RGB images with the received signal strength indicator (RSSI) using a deep auto-encoder model. The inpainting performance of RF-Inpainter is evaluated using experimentally obtained images and RSSI datasets in an indoor environment. Image-only inpainting and RSSI-only inpainting models are used as baselines to illustrate the superiority of RF-Inpainter over inpainting methods based on a single modality. The results establish that RF-Inpainter generates satisfactory inpainted images in most experimental scenarios, achieving a maximum improvement of 36.4% and 14.6% in terms of mean peak signal-to-noise ratio (PSNR) and mean structural similarity index (SSIM), respectively.
This letter is concerned with the optimal power allocation in femtocell networks using a new technique based on multiobjective optimization. In this letter, we consider a cochannel framework for the ...coexistence of orthogonal frequency division multiple access (OFDMA)-based macrocell and femtocell networks. We jointly maximize the total rate of OFDMA femtocell network and minimize the total transmit power of femtocell base stations while satisfying a target constraint on transmit power when either the cross femtocell interference is included or excluded. Additionally, the results show that the proposed method for power transmission contributes toward improving the energy efficiency of femtocell networks.
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