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In the wireless communication, the shortage of bandwidth has motivated the investigation and study of the wireless access technology called massive Multiple-Input Multiple-Output (MIMO). In multi-tier heterogeneous Fifth Generation (5G) networks, energy efficiency is a severe concern as the power utilization of macro base stations' is comparatively higher and proportional to their traffic load. In this paper, a novel African Vulture Shepherd Optimization Algorithm (AVSOA) is established that relies on macro cells and small cell system load information to determine the highly energy-efficient traffic offloading system. The proposed AVSOA model is a combination of the African Vulture Optimization Algorithm (AVOA) and the Shuffled Shepherd Optimization Algorithm (SSOA). The system load is predicted here by exploiting a Deep Quantum Neural Network (DQNN) algorithm to perform the conditional traffic offloading in that every macro-Base Station (BS) conjectures the offloading systems of other macro cells. The experimental evaluation of the adopted model is contrasted with the conventional models considering the energy efficiency, spectral efficiency, throughput, and system load. Finally, the performance analysis of the proposed model achieved better energy efficiency, spectral efficiency, and throughput of 0.250598, 0.184527, and 0.820354 Mbps and a minimum system load of 697. [ABSTRACT FROM AUTHOR]

Summary: In postdisaster rescue scenario, unmanned aerial vehicles (UAVs) are effective tools to help ground users in disaster areas to transmit rescue‐critical data to the relief center in time due to their flexible mobilities and fast deployment. However, how UAVs choose disaster areas to take part in traffic offloading with limited bandwidth resources is challenging. This article investigates UAVs' access selection and disaster areas' bandwidth allocation scheme by jointly optimizing network throughput, bandwidth and UAVs' energy cost. The UAVs form coalitions to participate in traffic offloading cooperatively, where each UAV selects a disaster area independently. Specifically, the bandwidth resources are dynamically allocated to all disaster areas when a new coalition partition forms. The UAVs adjust their access choices for disaster areas when the bandwidth allocation changes. When no UAVs migrate to other disaster areas, the network achieves a stable state. The UAVs fly to the final selected disaster area and provide traffic offloading services. To resolve traffic offloading competitions yet enable cooperations among UAVs, we address the UAVs' access selection issue by coalition formation game. A gradient projection method is then proposed to allocate bandwidth resources which maximizes the benefit of the network. We demonstrate that the UAVs' access selection and disaster areas' bandwidth allocation algorithms are convergent. The simulation results demonstrate that our proposed cooperation order is better than the pareto and selfish orders, thereby increasing the benefit of the network. [ABSTRACT FROM AUTHOR]

Future smart cities will undoubtedly generate and process an immense volume of data. Developing a smart network infrastructure is thus becoming a key component for the better development and efficient governance of smart cities. The main objective of this article is to examine the feasibility of utilizing moving vehicles to develop an efficient Vehicular-based Virtual Network Infrastructure (VVNI) for smart cities. The basic idea here is to divide the whole city into distinct cells, then predict and evaluate the vehicle eligibility to act efficiently as a centralized communication and information hub for each cell. Specifically, we present the basic framework of the proposed VVNI, and identify the corresponding efficiency requirements in terms of service availability, cost, and quality; then we introduce novel metrics to characterize the vehicle's ability to cope with these requirements. Several simulation scenarios were conducted to assess the efficiency of the proposed VVNI. The simulation results demonstrate how the proposed model outperforms existing models and improves system performance in three benchmark areas: throughput, packet delivery ratio, and latency, especially in medium- and high-density network scenarios with slower vehicle velocities. The simulation results are promising to further enhance the development of the proposed network infrastructure.

Device-to-device communication attracts the research community due to its diversifying range of applications. Device-to-device communication occurs between two devices without any aid from the base station. Thus, it helps in providing connectivity in low connectivity areas. In this article, authors proposed a new technique named as Clustering Based Opportunistic Traffic Offloading (CBOT), for Device-to-device communication. The proposed CBOT technique divided the network into small clusters and used a hybrid scheme for data transmission. To improve the energy consumption and throughput of the system, authors have proposed cluster formation, cluster head selection, and rotation techniques. The simulation results show that CBOT improves network lifetime by achieving high energy efficiency and increased system performance by achieving higher throughput. Further, the performance of CBOT tested under opportunistic networks scenario and simulation results demonstrate that the proposed approach improves energy consumption and throughput in a better way than the existing approaches. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the future era of intelligent networks, communication technology and network architecture need to be further developed to provide users with high-quality services. The Space-Air-Ground Integrated Networks (SAGIN) is seen as a potential architecture to provide ubiquitous communication and drive the era of the intelligent global network. The space and air segments in SAGIN can assist in offloading traffic from the ground segment. However, in a highly dynamic and heterogeneous network like SAGIN, offloading decisions are easily affected by the incorporated/malicious nodes. How to ensure security and improve network performance becomes a critical problem. In this paper, we address the above problem by jointly using blockchain and federated reinforcement learning (FRL). Firstly, we propose a blockchain-based secure federated learning framework that combines topology information chain and model chain to assist traffic offloading. Then, we propose a node security evaluation and an enhanced practical byzantine fault tolerance (EPBFT) algorithm to secure the traffic offloading process. Furthermore, we describe the traffic offloading problem as a Markov decision problem (MDP) and employ the Blockchain-based Federated Asynchronous Advantage Actor-Critic (BFA3C) algorithm to solve this problem. Finally, the simulation results show that the BFA3C-based algorithm used in SAGIN with/without malicious nodes achieves superior performance in terms of latency and security. [ABSTRACT FROM AUTHOR]

Recently, we have witnessed an expeditious growth in the intelligence and processing ability of user equipment accompanied by the explosive increase of wireless data and traffic. With such a huge demand, and a shortage of resources to fulfill that need, device-to-device (D2D) communication has surfaced as a propitious solution to reduce costs associated with backhaul links by collaboratively caching files. This paper explores the existing content caching frameworks in D2D communication environments. Further, it proposes two novel frameworks - one leveraging a combination of recurrent and deep neural networks and another based on the latest deep language models that use attention mechanisms known as transformers. The developed frameworks require minimum apriori knowledge about the environment and utilize the growth of user data to achieve performance enhancement. Our experiments show that the proposed frameworks are adaptive in nature and learn from the historical data of the environment. Further, we achieve an overall 25% increase in the D2D cache hit rate over a recently proposed framework that uses neural networks for collaborative filtering (NCF) to make caching decisions.

The air ground integrated networks can leverage unmanned aerial vehicle (UAV) communications to tackle the ever-increasing and unbalanced traffic load in future communication systems. This paper investigates the UAV enabled traffic offloading problem in air ground integrated networks with mixed user traffic. The problem jointly maximizes the system load balance and the total UAV reward, which can be formulated under a two-layer network graph model. In the cellular network graph, the association between the delay-sensitive users and the access points (APs) as well as the association between the UAVs and the APs are formulated. In the UAV network graph, the association between the delay-insensitive users and the UAVs is formulated. By observing the coupling relationship of the decision variables, we decouple the problem into three sub-problems and solve the first two sub-problems with reduced complexity. Then, we devise a Deep Neural Network (DNN) empowered genetic algorithm to solve the last sub-problem. The DNN can be leveraged to filter out the non-optimal solutions in the initialization operator of the genetic algorithm for improving the efficiency. Performance comparisons are provided between the proposed traffic offloading scheme and the existing ones, which validate the advantages of the DNN empowered genetic algorithm regarding its convergence, accuracy, and robustness. [ABSTRACT FROM AUTHOR]

The integration between the satellite network and the terrestrial network will play a key role in the upcoming sixth-generation (6G) of mobile cellular networks thanks to the wide coverage and bandwidth offered by satellite networks. To leverage this integration, we propose a proactive traffic offloading scheme in an integrated multi-satellite terrestrial network (IMSTN) that considers the future networks’ heterogeneity and predicts their variability. Our proposed offloading scheme hinges on traffic prediction to answer the stringent requirements of data-rate, latency and reliability imposed by heterogeneous and coexisting services and traffic namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable low latency communication (URLLC). However, the fulfilment of these requirements during offloading in dynamic IMSTN comes at the expense of significant energy consumption and introduces inherently supplementary latency. Therefore, our offloading scheme aims to balance the fundamental trade-offs first between energy consumption and the achievable data-rate and second between energy consumption and latency while meeting the respective needs of the present traffic. Our findings prove the importance of the cooperation between the multi-satellite network and the terrestrial network conditioned by traffic prediction to enhance the performance of IMTSN in terms of latency and energy consumption. [ABSTRACT FROM AUTHOR]

Offloading traffic from terrestrial areas to low-orbit satellite networks, the ultra-long-distance and ultra-large-range connectivity can be achieved by on-board routing and transmission. This paper investigates the relay-assisted NOMA-enabled uplink traffic offloading in the integrated satellite-terrestrial network. The joint energy efficiency maximum problem is formulated and then decomposed into two subproblems, i.e., the decoding sequence and power allocation (DS-PA) problem, and the users-satellites-subchannels association and transmit power allocation (USSA-TPA) problem. For DS-PA problem, the decoding sequence constraint is removed, and then the successive convex approximation method is utilized to solve it. To tackle the USSA-TPA issue, we reformulate it into two subproblems and resort to the alternate iteration method to deal with them iteratively. The numerical results show that the proposed joint optimization of energy efficiency for uplink traffic offloading cooperated with NOMA scheme outperforms other methods and their combinations. The performance of proposed USSA scheme and power allocation method is also discussed. [ABSTRACT FROM AUTHOR]

While the 5-th generation of communication networks (5G) is taking its first deployment steps, high doubts still concern the fulfilment of the stringent requirements of its slices. To meet these complex requirements, robust access networks should support the current 5G air interface. In this regard, space and air networks, namely satellites and unmanned aerial vehicles (UAVs) are expected to play a key role due to wide coverage and flexible deployment, respectively. Currently, the integration of these platforms in the terrestrial networks is weak though. Therefore, we suggest bridging this gap by designing a heterogeneous traffic offloading approach in the space-air-ground integrated network (SAGIN). Our innovative offloading approach covers the co-existing requirements of two heterogeneous slices of 5G by offloading smartly the traffic to the appropriate segment of SAGIN. Specifically, the ultra-reliable low-latency communications (URLLC) traffic is offloaded to the UAV link and to the terrestrial link to satisfy its stringent requirements in terms of latency. However, the enhanced mobile broadband (eMBB) traffic is offloaded to the UAV link, to the terrestrial link and to the satellite link because it is less sensitive to delay but needs high data rates. Our offloading approach boosts the network’s availability and reduces the latency experienced in SAGIN through an efficient resource allocation and an optimized design of the UAVs trajectory. Our findings highlight the key role that the concrete integration between SAGIN segments plays to achieve a better quality of service (QoS) for different slices with heterogeneous requirements.

Space-Air-Ground Integrated Networks (SAGIN) is considered as the key structure of the next generation network. The space satellites and air nodes are the potential candidates to assist and offload the terrain transmissions. However, due to the high mobility of space and air nodes as well as the high dynamic of network traffic, the conventional traffic offloading strategy is not applicable for the high dynamic SAGIN. In this paper, we propose a reinforcement learning based traffic offloading for SAGIN by considering the high mobility of nodes as well as frequent changing network traffic and link state. In the proposal, a double Q-learning algorithm with improved delay-sensitive replay memory algorithm (DSRPM) is proposed to train the node to decide offloading strategy based on the local and neighboring historical information. Furthermore, a joint information collection with hello package and offline training mechanism is proposed to assist the proposed offloading algorithm. The simulation shows that the proposal outperforms conventional offloading algorithms in terms of signaling overhead, dynamic adaptivity, packet drop rate and transmission delay. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Next-generation communication networks are expected to integrate newly-used technologies in a smart way to ensure continuous connectivity in rural areas and to alleviate the traffic load in dense regions. The prospective access network in 6G should hinge on satellite systems to take advantage of their wide coverage and high capacity. However, adopting satellites in 6G could be hindered because of the additional latency introduced, which is not tolerable by all traffic types. Therefore, we propose a traffic offloading scheme that integrates both the satellite and terrestrial networks to smartly allocate the traffic between them while satisfying different traffic requirements. Specifically, the proposed scheme offloads the Ultra-Reliable Low Latency Communication (URLLC) traffic to the terrestrial backhaul to satisfy its stringent latency requirement. However, it offloads the enhanced Mobile Broadband (eMBB) traffic to the satellite since eMBB needs high data rates but is not always sensitive to delay. Our scheme is shown to reduce the transmission delay of URLLC packets, decrease the number of dropped eMBB packets, and hence improve the network's availability. Our findings highlight that the inter-working between satellite and terrestrial networks is crucial to mitigate the expected high load on the limited terrestrial capacity.

The huge amounts of network traffic generated by the ultra-high-definition video playback of super-large-scale video users results in tremendous pressure for front-haul and back-haul in the 5G network, which brings severely negative influence for the large-scale deployment and scalability of video systems and video delivery performance (e.g. transmission delay and packet loss) related to user quality of experience. The direct D2D communications between mobile devices with adjacent position in geographical area can offloading huge video traffic into the underlying networks, which reduces load of cellular base station in the edge networks and relieves traffic pressure in the core networks. In this paper, we propose a novel Social-Aware D2D Video Delivery Method based on Rapid Sample-Efficient Measurement of Mobility Similarity in 5G Ultra-Dense Network (DMSEM). By investigation for one-hop D2D pair relationship, DMSEM builds a social state transition model of user movement, which makes use of encounter duration and shared video length between encountered users to define the state transition condition. A cluster algorithm of encounter events is proposed, which achieves initial clusters of encounter events by calculating similarity between encounter events from the two aspects of both variation rate of geographical distance between mobile users and encounter duration time. DMSEM makes use of the Fuzzy C-Means to refine the initial clusters and extracts encounter patterns of mobile users. DMSEM designs a sample-efficiency rapid recognition algorithm of encounter pattern, which can use small number of encounter distance samples to achieve fast heuristic recognition of encounter pattern. Extensive tests show how DMSEM achieves better results in comparison with other state-of-the-art solutions in terms of packet loss rate, average freeze time, cache utilization, average bitrate, buffer level and control overhead.

The explosive growth of traffic demand challenges the current cellular network. Device-to-Device assisted offloading is a promising solution for alleviating traffic overloading burdens. However, a content transmitter (CT) unwillingly participates in cellular offloading due to the selfish nature and non-cooperation. To this end, an incentive mechanism initiated by mobile users (MUs) is proposed to motivate the CT's participation for maximizing the CT's profit and mobile users' utilities. In particular, we consider the utility of a mobile user by jointly combining effects of social awareness and network congestion. The MUs and content providers offer proper economic payments to compensate the CT's energy consumption cost. The interactions among participants formulate a two-stage Stackelberg game. The main aim of this game is to reach Nash equilibrium (NE) that neither the CT nor MUs have incentives to deviate unilaterally. We theoretically prove the existence and uniqueness of NE. Furthermore, iterative algorithms are designed to calculate NE for obtaining the best strategies of the CT and MUs. Numerical results demonstrate the robustness and feasibility of our proposed model.

Summary: Heterogeneous networks (HetNets) are a practical solution for traffic offloading from a high powered base station (HBS) to a low powered base station (LBS). In the HetNets, uplink (UL)–downlink (DL) decoupled access (UDDa) strategy is an optimal solution that ensures cell association, independently, in the UL and DL. This strategy offloads HBS cell edge mobile device (MD) to the nearby LBS in the UL. However, energy‐efficient cell association for traffic offloading from HBS to LBS, relay, or device to device (D2D) in the UL employing UDDa strategy has not been explored in the past work. We formulate mathematical models which ensure energy‐efficient cell association, power allocation, and traffic offloading employing traditional UL‐DL coupled access (UDCa) and UDDa strategies in the HetNets. The formulated problems are concave fractional programming (CFP) problems. The CFP is changed to a concave optimization problem using Charnes–Cooper transformation (CCT). The transformed problems are solved using an outer approximation algorithm to obtain ϵ=10−3 optimal solution. Simulation results show the effectiveness of UDDa strategy over UDCa strategy in terms of MDs association, traffic offloading in the UL and DL, interference mitigation in the UL, data rate, and energy efficiency in the HetNets. [ABSTRACT FROM AUTHOR]

This letter studies the NBA (National Basketball Association)‐traffic offload mechanism based on in‐network caching and D2D (Device‐to‐Device) communication mode, where the router is enabled with the caching ability and the information exchange is based on the direct communication between two D2D groups. Meanwhile, the DPDK (Data Plane Development Kit) is used to do segmentation for the NBA‐video, which can guarantee that all segments have the same size in order to improve the caching efficiency; the clustering analysis based on maximal tree is adopted to divided into some users into several different D2D groups. Furthermore, for each D2D group, the forwarding ability is quantified based on degree centrality and available bandwidth, and it is used to select the group head. The experimental results show that the proposed novel NBA‐traffic offloading mechanism is feasible and efficient. [ABSTRACT FROM AUTHOR]

In recent years, the rapid growth of traffic has become a serious problem of mobile network operators. For effectively mitigating this traffic explosion problem, there have been many efforts to research on offloading the traffic from cellular links to direct communications among users. In this paper, we are motivated by users’ sharing activities, and hence propose the framework of Traffic Offloading assisted by Social network services (SNS) via opportunistic Sharing in mobile social networks (MSNs), TOSS, to offload SNS-based cellular traffic by user-to-user sharing. First, a subset of users who are to receive the same content was selected as initial population depending on their content spreading impacts in the online SNSs and their mobility patterns in the offline MSNs. Then users move, encounter and share the content via opportunistic local connectivity with each other, the content via opportunistic local connectivity with each other, e.g., Bluetooth, Wi-Fi Direct, Device-to-Device in LTE. Individual users have distinct access patterns, which potentially allow TOSS to exploit the user-dependent access delay between the content generation time and each user’s access time for content sharing purposes. The traffic offloading and content spreading among users are analyzed by taking into account various options in linking SNS and MSN traces. Four mobility traces and online SNS trace for evaluation are analyzed. An extended evaluation over a large-scale data set are further carried out, and the effectiveness of TOSS is further proved. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the impact of the small cell user density on the power consumption and the number of served users by the macro cell and small cells. It is assumed that users are distributed uniformly in all coverage area with different density and small cell user density is related linearly with macro cell user density. In the simulations, three different mean values are chosen, where the power consumption and the number of served users by the macro cell and small cells are investigated according to these values. The simulation results show that small cells with low mean value enable to save more power. When user density of macro cell is and mean value of small cell user density equals to 15, saved power by using the small cells sleeping technique is 695 Watts. When this saved power decreases to 372 Watts. Similarly, as the mean value of the small cell user density increases, the power gain obtained by the algorithm decreases.

Bu çalışmada, küçük hücre kullanıcı yoğunluğunun güç tüketimi ve makro ve küçük hücreler tarafından servis verilen kullanıcı sayısı üzerindeki etkisi analiz edilmektedir. Kullanıcıların tüm kapsama alanında düzgün dağılımlı olarak konumlandığı ve küçük hücre kullanıcı yoğunluğunun makro hücre kullanıcı yoğunluğu ile doğrusal olarak ilişkili olduğu varsayılmıştır. Benzetimlerde, üç farklı ortalama değer seçilmiş ve bu değerlere göre güç tüketimi ve makro hücre ve küçük hücreler tarafından servis verilen kullanıcı sayısı incelenmiştir. Benzetim sonuçları, düşük ortalama değere sahip küçük hücrelerin yüksek tasarruf gücü sağladığını göstermektedir. Makro hücrenin kullanıcı yoğunluğu ve küçük hücre kullanıcı yoğunluğunun ortalama değeri 15 olduğunda, küçük hücre uyutma tekniği kullanılarak elde edilen kazanç 695 Watt’tır. olduğunda ise 372 Watt’a düşmektedir. Benzer olarak küçük hücre kullanıcı yoğunluğunun ortalama değeri arttıkça algoritma ile elde edilen güç kazancı azalmaktadır.

In this article, a joint Radio Access Network selection strategy and a sub-channel resource allocation scheme are introduced, where the downlink of a two-tier HetNet system is deployed in a co-channel deployment. With this scenario in mind, a Semi Markovian Decision Process based-model is proposed to find an optimal policy for user association the channel allocation, where the long-term expected reward is maximized. For the proposed strategy, the sub-channel allocation is only considered for those new and handoff petitions that potentially can be allocated in one of the SCs. In addition, with the aim to contribute to the enhancement of the overall usage of radio resources, a traffic offloading process is considered at the departure of the sessions. In order to motivate the session offloading from the MC to the SCs at departure times, we have defined that two sub-channels will be allocated in the SC for each offloaded session. Analytical results quantify and show the effectiveness of the proposed strategy.

Content caching is an effective technique to alleviate the burden on backhaul links and reduce the traffic in cellular networks. In converged networks, broadcasting networks can push non-real-time popular services to different types of terminals during off-peak hours, while cellular networks are deployed to meet personalized needs. However, the storage capacity of terminals are usually limited. In this context, we study on the converged networks to push and cache the popular services in the router nodes close to the terminators. In this scheme, the most popular services are transmitted by the broadcasting base station and cached in the router nodes in a distributed cache network. Then, users can access the cached services in a more energy efficient manner. Due to the limited storage capacity of the router node, we assume that the user can access the cached services within two hops. Then, we formulate the service scheduling problem as a Markov Decision Process, aiming to maximize equivalent throughput (ET). Due to the large state space involved in the distributed cache network, it is quite challenging to obtain a tractable solution by the classical optimization algorithms. To handle this problem, a deep reinforcement learning based framework is proposed to tackle this problem. The simulation results show that the proposed algorithms are very effective; and they outperform the conventional one in term of the ET, especially when the users in the network subject to Poisson point process distribution. [ABSTRACT FROM AUTHOR]

Due to explosive growth of mobile data over the overburdened cellular network, opportunistic offloading is proposed to transmit the traffic originally transmitted through infrastructure network to delay-tolerant device-to-device networks at the edge of mobile networks. Existing offloading schemes mainly focus on selecting a subset of mobile nodes to work as offloadees, who first download the contents through the infrastructure, and then transmit them to subscriber nodes. However, when there is large amount of contents to be offloaded, and if all contents are disseminated through only one fixed set of offloadees every time, it may result in the risk of extreme cases that only extremely few nodes received the content, even if the expectation is large. This is like putting all of your eggs in one basket. To deal with this problem, we introduce an economic concept, conditional value at risk (CVaR) to measure the risk in offloadee set selection. CVaR is a widely used risk assessment measure for investments, which provides a unified and comprehensive risk evaluation framework for complex investment portfolio. We propose an algorithm to compute a portfolio over multiple offloadee sets with a guarantee on CVaR. Moreover, we prove the feasibility of our methodology from the mathematical point of view. Extensive evaluations with real-world traces show that our proposed offloading scheme can obtain a portfolio with significantly better CVaR, i.e., 91.53 percent performance gain at most, compared with the state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

This paper analyzes reliability and quality gains that can be achieved by a broadcast-unicast convergent platform operating in non-orthogonal multiple access (NOMA)-based cellular networks. The analysis is addressed in network scope, dealing with randomly distributed users and base stations (BSs) within a stochastic geometry framework. Additionally a well-known Zipf-distribution popularity model on user demands is also taken into account, which effectively reflects a broadcasting gain from reducing redundant resource expense toward the users demanding same contents. Based on the stochastic modeling, closed-form delivery success probabilities and the consequent transmission rates are analytically derived, where the derivations are obtained separately for the networks with different access control strategies. Furthermore, the optimal broadcast content selection strategy maximizing the transmission rates is proposed. By means of numerical calculations, NOMA is shown to achieve substantial gain at broadcast/unicast joint transmission compared to the conventional orthogonal multiple access (OMA) network, particularly for the service configuration with popular contents having lower target service rates than less popular contents as long as the number of broadcast programs is restricted to be equal to the number of unicast services in a single frame. [ABSTRACT FROM AUTHOR]

The ever-increasing and unbalanced traffic load in cellular vehicle-to-everything (C-V2X) networks have increased the network congestion and led to user dissatisfaction. To relieve the network congestion and improve the traffic load balance, in this paper, we propose an intelligent software defined C-V2X network framework to enable flexible and low-complexity traffic offloading by decoupling the network data plane from the control plane. In the data plane, the cellular traffic offloading and the vehicle assisted traffic offloading are jointly performed. In the control plane, deep learning is deployed to reduce the software defined network (SDN) control complexity and improve the traffic offloading efficiency. Under the proposed framework, we investigate the traffic offloading problem, which can be formulated as a multi-objective optimization problem. Specifically, the first objective maximizes the cellular access point (AP) throughput with consideration of the load balance by associating the users with the APs. The second objective maximizes the vehicle throughput with consideration of the vehicle trajectory by associating the delay-insensitive users with the vehicles. The two objectives are coupled by the association between the cellular APs and the vehicles. A deep learning based online-offline approach is proposed to solve the multi-objective optimization problem. The online stage decouples the optimization problem into two sub-problems and utilizes the ‘Pareto optimal’ to find the solutions. The offline stage utilizes deep learning to learn from the historical optimization information of the online stage and helps predict the optimal solutions with reduced complexity. Numerical results are provided to validate the advantages of our proposed traffic offloading approach via deep learning in C-V2X networks. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communications incorporated with content caching have been regarded as a promising way to offload the cellular traffic data. In this paper, the caching strategy is investigated to maximize the D2D offloading gain with the comprehensive consideration of user collaborative characteristics as well as the physical transmission conditions. Specifically, for a given content, the number of interested users in different groups is different, and users always ask the most trustworthy user in proximity for D2D transmissions. An analytical expression of the D2D success probability is first derived, which represents the probability that the received signal to interference ratio is no less than a given threshold. As the formulated problem is nonconvex, the optimal caching strategy for the special unbiased case is derived in a closed form, and a numerical searching algorithm is proposed to obtain the globally optimal solution for the general case. To reduce the computational complexity, an iterative algorithm based on the asymptotic approximation of the D2D success probability is proposed to obtain the solution that satisfies the Karush-Kuhn-Tucker conditions. The simulation results verify the effectiveness of the analytical results and show that the proposed algorithm outperforms the existing schemes in terms of offloading gain. [ABSTRACT FROM AUTHOR]

Due to the simplicity of implementation, user-initiated Wi-Fi offloading becomes more and more popular, and naturally the benefits of users become the main optimization goal. We notice the intercontact and intra-contact durations could be uncertain in reality by reason of the user mobility and network dynamics. The two uncertain durations can cause great impact on the benefit of users; however, they were either ignored or simply assumed to be deterministic in most previous works. In this paper, for the first time, we study Wi-Fi offloading problem with uncertain contact durations. The aim is to guarantee the benefit of users (delay and payment) without damaging operator's benefit (amount of the offloaded traffic) at the same time. We propose a multi-armed bandit (MAB)-based online offloading scheme (MABOO) to solve the problem and prove the near-optimality of MABOO in terms of the utility theoretically. Extensive simulations show that MABOO always approaches the optimal scheme and achieves higher utility as well as offloads more traffic compared with the minimal payment and on-the-spot-offloading schemes.

Improving the communication of Internet of Things (IoT) network is a challenging task as it connects a wide-range of heterogeneous mobile devices. With an extended support from cloud network, the mobile IoT devices demand flexibility and scalability in communication. Increase in density of communicating devices and user request, traffic handling and delay-less service are unenviable. This manuscript introduces genetic algorithm based adaptive offloading (GA-OA) for effective traffic handling in IoT-infrastructure-cloud environment. The process of offloading is designed to mitigate unnecessary delays in request process and to improve the success rate of the IoT requests. The fitness process of GA is distributed among the gateways and infrastructure to handle requests satisfying different communication metrics. The process of GA balances between the optimal and sub-optimal solutions generated to improve the rate of request response. Experimental results prove the consistency of the proposed GA-OA by improving request success ratio, achieving lesser complexity, delay and processing time. [ABSTRACT FROM AUTHOR]

In some remote areas under extremely scarce computation and communication resources, unmanned aerial vehicles (UAVs) assisted wireless communications are quite attractive for the more widely communication coverage and powerful computation capacity in contrast to user mobile devices (MDs). In addition to providing relay communication capabilities, the UAV can not only perform user tasks locally, but also offload them to base station or cloudlet for computing, provided that the user tasks require more powerful computation capacities. Considering the fact that the mobility trajectory of UAV could exert a negative impact on mobile offloading, we investigate the mobile offloading problem with a comparative consideration of mobility trajectory, communication, and computation resource allocation, in which the UAV-assisted mobile offloading and trajectory optimization problem (UOTO) is formulated with the objective of maximizing minimum user utility. Unfortunately, the UOTO provided is proven to be non-convex and there is no effective way to solve it. To overcome the difficulties, we develop a near-optimal algorithm by integrating the nonlinear fractional programming (NFP) and successive convex approximation (SCA). The extensive experiments are conducted to illustrate the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

To deal with the explosive traffic demands on the overloaded cellular network, device-to-device (D2D) offloading is proposed to transmit the traffic originally delivered through cellular network by D2D communications. Current D2D offloading schemes are based on direct transmissions or simple relay-assisted transmissions, which is hard to achieve high throughput. Network coding can effectively solve the problem. However, two problems must be addressed, topology formation and resource allocation. We formulate the topology formation problem as a function on network throughput under transmission pair matching and relay selection. we adopt coalition game theory to jointly match devices to form transmission pairs and select relay to form topology. Moreover, we propose a greedy algorithm to allocate the limited spectrum resource for each transmitter to decrease the interference influence. We evaluate our proposed offloading scheme through extensive evaluations under different network environments. The result shows that our scheme can effectively improve the network throughput and enhance the offloading efficiency.

Both complicated interference and frequent handoffs exist in 5G ultra-dense small cell networks (UDNs), which significantly degrade the system performance. Therefore, a novel power control and simple traffic offloading scheme is proposed to handle the interference and handoffs. Owing to the severe interference coupling, this problem is formulated as a dynamic stochastic game (DSG) between small cells, which can characterize the stochastic property of channel states. Nevertheless, it is hard to solve the DSG system when the number of players exceeds 2. Therefore, to solve the DSG system in UDNs, we use the mean field approximation theory and convert the DSG into a mean field game (MFG) in which the game equilibrium is analyzed by solving two tractable partial differential equations, from the global and individual perspectives. In contrast to the conventional MFG works, there usually exists a dominating player, which introduces higher interference due to the location or larger transmit power in UDNs, for assumed generic player. Consequently, we formulate the framework of MFG with a dominating player and lots of generic players. Within the formulated framework, first we design the combined cost function considering both the aggregate interference and signal-to-interference-plus-noise ratio performances for both the generic player and its dominator. Then, we derive the corresponding Fokker–Planck–Kolmogorov and Hamilton–Jacobi–Bellman equations. Next, we propose a novel interference-aware traffic offloading and power control policy. Finally, we present numerical results showing the energy and spectrum efficiency performances of the proposed policy compared with the policy without traffic offloading. [ABSTRACT FROM AUTHOR]

Featured Application: Service-oriented data sharing is one of the big challenges we have to face in the fifth generation (5G) cellular networks scenarios, such as massive Machine Type Communications (mMTC) and Ultra-Reliable and Low Latency Communications (URLLC). It is inefficient for current techniques to directly share data for the increasingly diverse vertical applications due to the difficulty of cross-application interoperability without unified name resolution. ICN is an emerging technology with the advantages of unified naming and name resolution and the ability to share data on the network layer. Our proposed system, called Enhanced Name Resolution System (ENRS), has the potential to be applied to serval vertical fields, for example, the Internet of Things (IoT) and vehicle networking. The topology-independent nature of ICN with the identifier–locator separation and name resolution give us the opportunity to define new solutions to tackle the scalable and low-latency problems to provide open data services through name resolution function. The challenging requirements of the fifth generation (5G) cellular networks motivate the need to explore the feasibility of delivering services over new network architectures. Information-Centric Networking (ICN) is an emerging and promising network to satisfy 5G requirements. The name resolution is at the heart of ICN. We focus on satisfying the delay-sensitive requirement of the Name Resolution Service (NRS) in a 5G-ICN integrated network. We aim to design a local NRS that provides a deterministic low latency name resolution service. In this paper, we propose a temporally hierarchical deployment architecture for an Enhanced Name Resolution System (ENRS) to realize deterministic latency. The ENRS quantifiably organizes the nodes into hierarchical and nested domains by latency constraints. We design demand-aware name registration and resolution schemes to achieve constant forwarding hops in order to realize local resolution and forwarding locality. We introduce a tolerable latency-based peer resolver forwarding algorithm to improve the query hit ratio. We present a proactive name binding replicas distribution approach based on temporal–spatial features to reduce the resolution latency and query traffic. The video streaming monitoring service in Smart Home is used as a typical use case to show the continuity of service guaranteed by ENRS. Analysis demonstrates that ENRS can achieve deterministic latency. Evaluation results show that the average query hit ratio of ENRS outperforms the K-NearestNeighbor-Distributed Name Resolution System (KNN-DNRS) and Random Name Resolution System (Random-NRS) with 23.2% and 18.1%, respectively. The query traffic overhead of ENRS is up to 33.3 times smaller than KNN-DNRS. ENRS can process up to 21 GB/s name resolution traffic when the user nodes are in the magnitude order of 106. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the impact of the small cell user density on the power consumption and the number of served users by the macro cell and small cells. It is assumed that users are distributed uniformly in all coverage area with different density and small cell user density is related linearly with macro cell user density. In the simulations, three different mean values are chosen, where the power consumption and the number of served users by the macro cell and small cells are investigated according to these values. The simulation results show that small cells with low mean value enable to save more power. When user density of macro cell is λ0 = 5x10-4 and mean value of small cell user density equals to 15, saved power by using the small cells sleeping technique is 695 Watts. When λ0 = 10x10-4 this saved power decreases to 372 Watts. Similarly, as the mean value of the small cell user density increases, the power gain obtained by the algorithm decreases. [ABSTRACT FROM AUTHOR]

This paper presents a video quality-aware traffic offloading system that provides seamless video streaming services to users by offloading part of the macrobase station traffic into small cells. The proposed system achieves a good balance between fairness and social welfare in terms of the video quality by allocating the radio resource of the macrobase station. The data flow is split into two parts toward the macrobase station and small cell AP, and the users simultaneously receive their data from both. In the proposed system, fountain code is employed to achieve dual connectivity enhancement by overcoming frequent packet loss in error-prone mobile networks and practical issues in the dual connectivity architecture. Furthermore, software-defined networking (SDN) technology is adopted not only to quickly react to time-varying network status but also to control network resources efficiently. The proposed resource allocation process is designed to require a low running time complexity. The proposed system is implemented using C/C++, Java, and a well-known open source, ONOS. The experimental results verify that the proposed system can achieve much better performance than other offloading algorithms. [ABSTRACT FROM AUTHOR]

The current network architecture scene can be a challenge due to the increasing number of user equipment and its diversity. Furthermore, the demand for greater data rates and lower latency by the users are increasing. Deployment of the ultra dense networks (UDN) described is to support future wireless network and meet user demands in future wireless networks. Combined with other technologies such as device to device (D2D) communication, it will provide benefit to the wireless communication. In this paper, challenges in existing research efforts are analyzed and future avenues for research is presented. Apart from that, this paper considers the features of the D2D and UDN to collaborate later. Generally, by deployment of large small cells, factors such as the traffic offloading, idle mode capability, power consumption, and energy efficiency should be considered. Basically, from the potential challenges that have been identified, it is obvious that the integration between D2D and UDN is a key enabler for potential solution. Finally, the research gaps for research between integration of UDN and D2D can be identified with challenges and open problems that have been presented. [ABSTRACT FROM AUTHOR]

The exponential growth of mobile data traffic and a limited number of spectrum resources has been a big challenge for cellular network providers, henceforth traffic offloading has become one of the most critical issues especially in 5G Heterogeneous Networks (HetNets). Further, network selection plays a vital role for traffic offloading in a cellular network to maintain Quality of Service (QoS), increasing offloading efficiency and throughput. In order to efficiently utilize spectral resources, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm is proposed to be used for ranking a candidate network. The proposed algorithm helps in alleviating the spectrum shortage by offloading the data traffic over Wi-Fi network using unlicensed spectrum. In this work, analysis of the performance of the proposed system model through simulation of an analytical framework has been made. The results have been accumulated in terms of cumulative handover, throughput, the extent of equilibrium & offloading efficiency with respect to residence time and the number of Wi-Fi Access Points (AP’s). Analysis proves that the proposed algorithm improves the equilibrium extent and throughput as compared to traditional Load balancing (LB) and SDN based LB mechanisms. It also shows that offloading efficiency is highly improved over Wi-Fi density and residence time. [ABSTRACT FROM AUTHOR]

In the past decade, network data communication has experienced a rapid growth, which has led to explosive congestion in heterogeneous networks. Moreover, the emerging industrial applications, such as automatic driving put forward higher requirements on both networks and devices. On the contrary, running computation-intensive industrial applications locally are constrained by the limited resources of devices. Correspondingly, fog computing has recently emerged to reduce the congestion of content-centric networks. It has proven to be a good way in industry and traffic for reducing network delay and processing time. In addition, device-to-device offloading is viewed as a promising paradigm to transmit network data in mobile environment, especially for autodriving vehicles. In this paper, jointly taking both the network traffic and computation workload of industrial traffic into consideration, we explore a fundamental tradeoff between energy consumption and service delay when provisioning mobile services in vehicular networks. In particular, when the available resource in mobile vehicles becomes a bottleneck, we propose a novel model to depict the users’ willingness of contributing their resources to the public. We then formulate a cost minimization problem by exploiting the framework of Markov decision progress (MDP) and propose the dynamic reinforcement learning scheduling algorithm and the deep dynamic scheduling algorithm to solve the offloading decision problem. By adopting different mobile trajectory traces, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results show that our proposed algorithms outperform other benchmark schemes in the mobile edge networks. [ABSTRACT FROM AUTHOR]