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Grant-free access, in which channels are accessed without undergoing assignment through a handshake process, is a promising solution to support massive connectivity needed for Internet-of-Things (IoT) networks. In this paper, we consider uplink grant-free massive access for an IoT network with multiple channels. To be specific, the IoT devices generate short packets and have grant-free non-orthogonal access to a channel to transmit the generated packets to a base station (BS). With the aim of keeping the information fresh at the BS, we first derive the age of information (AoI) for grant-free short-packet communications, and then formulate the AoI minimization problem. However, the problem is challenging as the number of users involved is large, and to tackle this problem efficiently, we propose a mean-field evolutionary game-based approach. In this approach, the average behavior of the IoT devices is considered rather than their individual behaviors, and the dynamics of the strategies of the IoT devices are modeled by an evolutionary process. Simulation results verify the effectiveness of the proposed mean-field evolutionary game-based approach.

Wireless network virtualization is basically the abstraction, isolation, and sharing of wireless resources among different entities. Consequently, virtualization provides great flexibility and higher network efficiency, and enables easier migration to new technologies in wireless networks. Traditionally, a wireless network virtualization controller manages the virtual resources (including radio resources and infrastructure resources) known as slices which are available to the Service Providers (SPs). The SPs then allocate their purchased resources to serve their subscribed mobile users. Such a centralized allocation decouples the Quality-of-Service (QoS) management by the SPs from the virtual resource management by the controller. In this paper, we propose a matching based wireless network virtualization resource allocation mechanism: a distributed three-sided (3D) matching between radio resources, physical infrastructure and mobile users. The Restricted Three-sided Matching with Size and Cyclic preference model (R-TMSC) is implemented to obtain a stable solution. Simulation results show that our proposed spectrum-oriented and user-oriented algorithms outperform the traditional resource allocation schemes. The spectrum-oriented algorithm enhances the user throughput and the system performance, within a lesser run time. Furthermore, for an increasing number of users, the proposed algorithms serve more users than traditional methods.

In this paper, we consider the radio access network (RAN) slicing in heterogeneous communication systems. Typically, the infrastructure provider (InP) allocates the radio resources to the mobile virtual network operators (MVNOs) as RAN slices, and then the MVNOs use the allocated resources to provide services for their users. As the RAN being heterogeneous, the users are generally with different quality of service (QoS) requirements. Besides, the MVNOs also have different intrinsic interests in terms of collecting revenue and satisfying users QoS demand and thus require different amount of resources. In this regard, it is challenging for the InP to determine the resource allocation strategy. To deal with this issue, we propose a three-layer Stackelberg game (TLSG) to jointly solve the frequency and power allocation for the InP. We derive the unique equilibrium for the special case where each MVNO uniform users and propose a bi-level differential evolution algorithm for more general cases. Simulation results reveal that both users and the InP can be better off when the RAN is sliced for different types of users, and that the MVNOs focusing more on revenue collecting result in the InP and the users obtaining less payoff.

Recently, the ultra-dense low earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for terrestrial data offloading owing to its seamless coverage and high-capacity backhaul. In this paper, we consider an integrated ultra-dense LEO-based satellite-terrestrial network where the terrestrial operator (TO) can offload its subscribed users to the LEO satellite network owned by the satellite operator (SO) for satellite-backhauled network access. However, data offloading consumes extra resources of the SO and degrades the quality-of-service of the SO’s original users. Therefore, we aim to design a pricing mechanism based on the Stackelberg game to motivate both operators for data offloading, and the Stackelberg equilibrium is achieved by jointly optimizing the C-band user association, Ka-band spectrum allocation, and data service pricing. Simulation results show that our proposed pricing mechanism can motivate two operators for offloading efficiently. The influence of available frequency resources, data service prices, and the number of LEO satellites on the system performance are also discussed.

In this paper, we consider an Internet of unmanned aerial vehicles (UAVs) over cellular networks, where UAVs work as aerial users to collect various sensory data, and send the collected data to their transmission destinations over cellular links. Unlike the terrestrial users in the conventional cellular networks, different UAVs have various communication requirements due to their sensing applications, and a more flexible communication framework is in demand. To tackle this problem, we propose a UAV-to-Everything (U2X) networking, which enables the UAVs to adjust their communication modes full dimensionally according to the requirements of their sensing applications. In this article, we first introduce the concept of U2X communications, and elaborate on its three communication modes. Afterwards, we discuss the key techniques of the U2X communications, including joint sensing and transmission protocol, UAV trajectory design, and radio resource management. A reinforcement learning-based mathematical framework for U2X communications is then proposed. Finally, the extensions of the U2X communications are presented.

With limited wireless resources but increasing demands for wireless applications, non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless networks. Nevertheless, it remains challenging on motivating the mobile users (MU) to join NOMA networks, scheduling MUs on the same sub-bands, reducing interference among the MUs and setting optimal transmit power in the NOMA networks. In this paper, first we consider the quantization error due to the limitation of receivers’ equipments and the decoding error during the successive interference cancellation (SIC) procedure. Second, we model the resource allocation problems as the four-level equilibrium problems with equilibrium constraints (EPEC), where the wireless operator, on behalf of each MU, first predicts the utilities and assign the MU as a NOMA user (NMU) or a traditional MU (TMU). For the NMUs, the interference penalty pricing strategy of other NMUs are proposed. Based on the announced prices of all the NMUs, the sub-channel allocation is managed in either distributed or collaborative ways. According to the announced interference prices of other NMUs and the coalition results, the optimal transmit power to gain the maximum utility is considered. Through extensive simulation analysis, the superior performance obtained by using the proposed scheme is demonstrated.

Driven by an increasing number of mobile applications, mobile edge computing (MEC) has been considered as a promising candidate to support the huge amount of data processing services. However, the conventional MEC suffers from the insufficient utilization of computing and transmission resources through the entire network, resulting in inevitably long processing and transmission latency especially to computation-intensive applications in the 6 G era. In this article, we propose a heterogeneous multi-layer mobile edge computing (HetMEC), where different devices, ranging from edge devices (EDs), i.e., the mobile devices that generate raw data of computing tasks in the radio access networks, to the cloud center (CC), are inherently involved different layers of the network and collaborate for data processing. To support a low-latency service, a reinforcement learning-based framework is constructed to adapt to the unstable wireless environments as well as the dynamically varying data generation speed of each ED. Under this framework, key research issues and solutions including task offloading, cognitive radio based spectrum access, pricing scheme design, and network congestion control are presented. Some further research directions and opening issues are also discussed in the perception of network planning and optimization, network control, and application-specific issues.

As distributed energy resources (DERs) are widely deployed, DC packetized power microgrids have been considered as a promising solution to incorporate DERs effectively. In this paper, we consider a DC packetized power microgrid, where the energy is dispatched in the form of power packets with the assistance of a power router. However, the benefits of the microgrid can only be realized when energy subscribers (ESs) equipped with DERs actively participate in the energy market. Therefore, peer-to-peer (P2P) energy trading is necessary in the DC packetized power microgrid to encourage the usage of DERs. Different from P2P energy trading in AC microgrids, the dispatching capability of the router needs to be considered in DC microgrids, which will complicate the trading problem. To tackle this challenge, we formulate the P2P trading problem as an auction game, in which the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. Analysis of the proposed scheme is provided, and its effectiveness is validated through simulation.

Unmanned aerial vehicles (UAVs) are powerful Internet-of-Things components to provide sensing and communications in the air due to their advantages in mobility and flexibility. As aerial users, UAVs are envisioned to support various sensing applications in the next generation cellular systems, which have been studied by the Third Generation Partnership Project (3GPP). However, the Quality-of-Services (QoS) of the cellular link between the UAV and the base station may not be guaranteed when UAVs are at the cell edge or experiencing deep fading. In this article, we first introduce the non-cooperative cellular Internet of UAVs. Then we propose a cooperative sense-and-send protocol, in which a UAV can upload sensory data with the help of a UAV relay, to provide a better communication QoS for the sensing tasks. Key techniques including trajectory design and radio resource management that support the cooperative cellular Internet of UAVs are presented in detail. Finally, the extended cooperative cellular Internet of UAVs is discussed for QoS improvement with some open issues, such as massive multiple-input multiple-output systems, millimeter-wave, and cognitive communications., Comment: This paper has been accepted by IEEE Wireless Communications

Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing (MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer some disadvantages such as limited computing capacity, preventing and computation-intensive tasks to be processed on time. To relief this issue, we propose the heterogeneous multi-layer MEC (HetMEC) where data that cannot be timely processed at the edge are allowed to be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We aim to minimize the system latency, i.e., the total computing and transmission time on all layers for the data generated by the EDs. We design the latency minimization algorithm by jointly coordinating the task assignment, computing, and transmission resources among the EDs, multi-layer MEC servers, and the CC. The simulation results indicate that our proposed algorithm can achieve a lower latency and higher processing rate than the conventional MEC scheme.

With a large penetration of distributed energy resources, DC power packet transmission has emerged as a promising technique to achieve efficient peer-to-peer (P2P) power dispatching. In this paper, a multirouter local area packetized power network (LAPPN) is considered, consisting of multiple power routers employed to dispatch power packets among demander and supplier subscribers connected to different routers. To realize the efficient P2P power transmission in the LAPPN, a power packet dispatching protocol is developed to determine the optimal routes for power packets to maximize the power packets utilization efficiency. The transmission schedule is then determined by allocating different power packets on different power channels simultaneously to accommodate the urgency requirements of different demander energy subscribers (ESs). Simulation results demonstrate the effectiveness of the proposed LAPPN power dispatching protocols in achieving high power packet utilization and meeting the urgency requirements of different demander ESs in P2P power delivery.

In this paper, we study the platoon cooperation in the multi-lane cooperative platoon scenario, where platoons move cooperatively and communicate with each other by cellular vehicle-to-everything (V2X) communication. The platoon cooperation is important for the interference management and communication reliability enhancement, yet it is difficult, since the platoon formation, subchannel allocation, and power control of each platoon interact with each other and influence those of other platoons. To increase the number of vehicles in the platoon and reduce the power consumption, we propose a two-step resource allocation strategy in consideration of platoon formation, i.e., the resource allocation at the base station (BS) and within each platoon. A branch and bound algorithm is utilized for the resource allocation at the BS. We then design a distributed dynamic programming-based subchannel allocation and power control algorithm for the joint optimization of platoon formation, subchannel allocation, and power control. The simulation results evaluate the impact of the penalty factor and the latency requirement on the system performance.

In this paper, we propose a multilayer data flow processing system, i.e., EdgeFlow, to integrally utilize the computing capacity throughout the whole network, i.e., the cloud center (CC) on the top layer, the mobile edge computing (MEC) servers on the middle layer, and the edge devices (EDs) on the bottom layer. To realize the efficient data processing in EdgeFlow, we optimally assign the tasks to multiple layers, and allocate the wireless transmission resources between the MEC servers and EDs as well as the wired transmission resources between the CC and MEC servers. We prove that the system is naturally classified into two states, the nonblocking state and the blocking state, according to various data generation speed at the EDs. The system latency is minimized for the nonblocking state even though the problem is nonconvex. As for the blocking state, the recovery time is minimized through solving a min-max problem. Based on the analytical results, the EdgeFlow system is implemented on the universal software radio peripheral and the Intel next units of computing. A typical Internet of Things application, photo recording and face recognition, is used for the simulation and the experiment, and indicates that the EdgeFlow can achieve a low latency and recovery time than the previous distributed frameworks, e.g., the Cloudlet and the Markov decision process.

To support the explosive growth of wireless devices and applications, various access techniques need to be developed for future wireless systems to provide reliable data services in vast areas. With recent significant advances in ultra-dense low Earth orbit (LEO) satellite constellations, satellite access networks (SANs) have shown their significant potential to integrate with 5G and beyond to support ubiquitous global wireless access. In this article, we propose an enabling network architecture for dense LEO-SANs in which the terrestrial and satellite communications are integrated to offer more reliable and flexible access. Through various physical-layer techniques such as effective interference management, diversity techniques, and cognitive radio schemes, the proposed SAN architecture can provide seamless and high-rate wireless links for wireless devices with different quality of service requirements. Three extensive applications and some future research directions in both the physical layer and network layer are then discussed.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra-dense low earth orbit (LEO) networks and the terrestrial networks to achieve efficient data offloading. In TSN, each ground user can access the network over C-band via a macro cell, a traditional small cell, or a LEO-backhauled small cell (LSC). Each LSC is then scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate and the number of accessed users while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite based backhaul links. The optimization problem is then decomposed into two closely connected subproblems and solved by our proposed matching algorithms. Simulation results show that the integrated network significantly outperforms the non-integrated ones in terms of the sum data rate. The influence of the traffic load and LEO constellation on the system performance is also discussed., Accepted by IEEE Transactions on Wireless Communications

The Emerging unmanned aerial vehicles (UAVs) have been widely exploited for sensing purposes due to the larger service coverage compared with the conventional fixed sensor nodes. However, due to the limited computation capability of UAVs, real-time sensory data needs to be transmitted to the BS/server for real-time data processing. In this regard, the cellular networks are necessary to support the data transmission for UAVs, which is called the Internet of UAVs. Very recently, 3GPP has approved a study item on the enhanced support to seamlessly integrate UAVs into future cellular networks.

In this paper we propose a cluster-based platoon handoff protocol (CPHP), in which the platoon is divided into clusters to minimize its handoff delay between two adjacent cells. Via the vehicle-to-vehicle (V2V) communications, multiple clusters are constructed within one platoon and only the cluster head (CH) communicates with the base station (BS) via the vehicle-to-infrastructure (V2I) communications. In this way, the number of V2I links can be greatly saved and the signaling overhead is reduced, thereby shortening the handoff delay. Specifically, we formulate a delay minimization problem based on a discrete-time Markov chain and then optimize the number of clusters and spectrum resource allocation. Simulation results demonstrate a significant decrease in the handoff delay of the platoon. The optimal expected delay under a platoon with 50 vehicles by utilizing the proposed CPHP is 15% smaller than that of the traditional handoff scheme.

In this paper, we study the harmonious coexistence of cellular and WiGig users around 60 GHz unlicensed millimeter-wave bands. To guarantee the fairness of two types of users, we propose a sensing based adaptive unlicensed channel sharing protocol. Jointly considering the frequency and spatial resource allocation, an iterative channel allocation and hybrid beamforming algorithm is designed to maximize the sum rate of all cellular users while minimizing the interference to WiGig networks. Simulation results show that our proposed scheme achieves a significant sum rate enhancement compared with that when utilizing only the licensed band.

The multi-path transmission techniques enable multiple paths to maximize resource usage and increase throughput in transmission, which have been installed over mobile devices in recent years. For video streaming applications, compared to the single-path transmission, the multi-path techniques can establish multiple subflows simultaneously to extend the available bandwidth for streaming high-quality videos in mobile devices. Existing adaptive video streaming systems have difficulty in harnessing multi-path scheduling and balancing the tradeoff between the quality of experience (QoE) and quality of service (QoS) concerns. In this paper, we propose an actor-critic network based on Periodical Experience Replay for Multi-path video streaming (PERM). Specifically, PERM employs two actor modules and a critic module: the two actor modules respectively assign the path usage of each subflow and select bitrates for the next chunk of the video, while the critic module predicts the overall objectives. We conduct trace-driven emulation and real-world testbed experiment to examine the performance of PERM, and results show that PERM outperforms state-of-the-art multi-path and single path streaming systems, with an improvement of 10%- 15% on the QoE and QoS metrics.

In this paper, we consider the distributed energy saving for heterogeneous multi-layer mobile edge computing (HetMEC), where the edge devices (EDs) upload computing tasks to the mobile edge computing (MEC) servers and the cloud center (CC) for processing. To reduce the energy consumption, task offloading and resource allocation are performed by each device independently to distribute the computation load. However, due to the partial information, the offloading strategies of devices on different layers can hardly be aligned, which may lead to network congestion. To address this problem, we propose a smart pricing mechanism to coordinate the strategies of multi-layer devices, where the CC charges the MEC servers and EDs for computing services and network congestion. The pricing based distributed task offloading and resource allocation algorithm is designed to minimize the total energy consumption subject to latency requirements. Simulation results indicate that our algorithm achieves lower energy consumption and congestion probability compared with the traditional distributed method.

Unmanned aerial vehicle (UAV) has been recognized as a promising way to assist future wireless communications due to its high flexibility of deployment and scheduling. In this paper, we focus on temporarily deployed UAVs that provide downlink data offloading in some regions under a macro base station (MBS). Since the manager of the MBS and the operators of the UAVs could be of different interest groups, we formulate the corresponding spectrum trading problem by means of contract theory, where the manager of the MBS has to design an optimal contract to maximize its own revenue. Such contract comprises a set of bandwidth options and corresponding prices, and each UAV operator only chooses the most profitable one from all the options in the whole contract. We analytically derive the optimal pricing strategy based on fixed bandwidth assignment, and then propose a dynamic programming algorithm to calculate the optimal bandwidth assignment in polynomial time. By simulations, we compare the outcome of the MBS optimal contract with that of a socially optimal one and find that a selfish MBS manager sells less bandwidth to the UAV operators.

DC power packet transmission enables a large penetration of distributed energy resources and reliable end-to-end power dispatching. In this paper, we consider a local area packetized power network (LAPPN), where a multi-channel power router links all the subscribers and allows multiple demander-supplier pairs to interchange power packets simultaneously. To realize an efficient power dispatching in a dc packetized power network, effective subscriber matching, and transmission scheduling need to be developed. We design a packetized power dispatching protocol to regulate the LAPPN operation, where the involved subscribers are matched into demander-supplier pairs first, and the matched pairs’ transmission tasks are then orderly scheduled onto multiple power channels of the router. Based on the matching theory, we formulate the subscriber matching as a standard matching problem to maximize each subscriber’s benefit and develop a deferred acceptance algorithm to solve the problem. We also formulate the transmission scheduling problem and propose a heuristic solution to obtain the real-time balance between load and generation within an LAPPN, taking into account the urgency of power transmission and the fairness among the subscribers. Simulation results confirm the effectiveness of the proposed protocol in realizing a highly utilized and efficient LAPPN power dispatching.

With the increasing demand for vehicular data transmission, limited dedicated cellular spectrum becomes a bottleneck to satisfying the requirements of all cellular vehicle-to-everything (V2X) users. To address this issue, unlicensed spectrum is considered to serve as the complement to support cellular V2X users. In this paper, we study the coexistence problem of cellular V2X users and vehicular ad hoc network (VANET) users over the unlicensed spectrum. To facilitate the coexistence, we design an energy sensing-based spectrum sharing scheme, where cellular V2X users are able to access the unlicensed channels fairly while reducing the data transmission collisions between cellular V2X and VANET users. In order to maximize the number of active cellular V2X users, we formulate the scheduling and resource allocation problem as a two-sided many-to-many matching with peer effects. We then propose a dynamic vehicle-resource matching algorithm and present the analytical results on the convergence time and computational complexity. Simulation results show that the proposed algorithm outperforms existing approaches in terms of the performance of the cellular V2X system when the unlicensed spectrum is utilized.

Caching popular files in the storage of edge networks, namely edge caching, is a promising approach for service providers (SPs) to reduce redundant backhaul transmission to edge nodes (ENs). It is still an open problem to design an efficient incentive mechanism for edge caching in 5G networks with a large number of ENs and mobile users. In this paper, an edge network with one SP, a large number of ENs and mobile users with time-dependent requests is investigated. A convergent and scalable Stackelberg game for edge caching is designed. Specifically, the game is decomposed into two types of sub-games, a storage allocation game (SAG) and a number of user allocation games (UAGs). A Stackelberg game-based alternating direction method of multipliers (Stackelberg game-based ADMM) is proposed to solve either the SAG or each UAG in a distributed manner. Based on both analytical and simulation results, the convergence speed, the optimum of the entire game, and the amount of information exchange are linearly (or sublinearly) related to the network size, which indicates that this framework can potentially cope with large-scale caching problems. The proposed approach also requires less backhaul resource than the existed approaches.

Recently, there has been an increasing interest in offloading the 3GPP LTE data by using device-to-device (D2D) communications between devices. However, the peer discovering is challenging, especially in the indoor environment, since traditional users use a cellular signal to find peers, leading to incurring interference to other cellular users. In this paper, we propose ROOMMATEs, a novel approach for indoor peer discovery process, which is the enabler for indoor D2D communications in long term evolution (LTE) networks. It is a centralized approach utilizing, but not limited to, the ubiquitous WiFi network/femtocell network, combining with eNodeB in order to deliver the best results. ROOMMATEs is an unsupervised, yet energy efficient algorithm that can find surrounding user equipments (UEs) while minimizing interference and consuming much less energy. Based on the results, ROOMMATEs proves to be highly energy efficient, saving on average 24% per UE and improving signal-to-interference-plus-noise ratio in order of tens of decibels compared to other approaches. Moreover, ROOMMATEs provides indoor place identification for UEs with high accuracy using a small number of observations. ROOMMATEs is robust to missing and noisy data, works with various UEs’ brands/models, and requires no user interactions.

Recently, owing to the advances in the self-interference cancellation technology, the in-band full-duplex (FD) capability has been demonstrated at Wi-Fi range. However, the simultaneous uplink (UL) and downlink (DL) transmission may lead to inter-user interference (IUI) and result in decoding failure. Spectrum efficiency should also be considered in the construction process of the FD transmission. In this paper, we propose a hybrid half-duplex/FD MAC protocol based on a two-fold RTS/CTS contention resolution mechanism, in order to fully exploit the channel access opportunities provided by the simultaneous UL and DL transmissions. The noteworthy features of the proposed protocol lie in the following two aspects. First, the protocol provides the flexibility for the AP to decide the probability of constructing FD transmission, and then it adopts a second-fold of RTS/CTS mechanism to prevent the constructed transmission from being affected by the IUI. The second-fold contention and the probability of constructing FD transmission are optimized separately to maximize the spectrum efficiency given different transmission demands. Simulation results show that the proposed MAC protocol achieves higher capacity compared with previous works, and the hybrid characteristic enables the FD Wi-Fi networks to meet with different system requirements.

Benefited from the widely deployed infrastructure, the LTE network has recently been considered as a promising candidate to support the vehicle-to-everything (V2X) services. However, with a massive number of devices accessing the V2X network in the future, the conventional OFDM-based LTE network faces the congestion issues due to its low efficiency of orthogonal access, resulting in significant access delay and posing a great challenge especially to safety-critical applications. The non-orthogonal multiple access (NOMA) technique has been well recognized as an effective solution for the future 5G cellular networks to provide broadband communications and massive connectivity. In this article, we investigate the applicability of NOMA in supporting cellular V2X services to achieve low latency and high reliability. Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed to tackle the technical hurdles in designing high spectral efficient scheduling and resource allocation schemes in the ultra dense topology. We then extend it to a more general V2X broadcasting system. Other NOMA-based extended V2X applications and some open issues are also discussed., Comment: Accepted by IEEE Wireless Communications Magazine

Recent improvements in vehicular ad hoc networks are accelerating the realization of intelligent transportation system (ITS), which not only provides road safety and driving efficiency, but also enables infotainment services. Since data dissemination plays an important part in ITS, recent studies have found caching as a promising way to promote the efficiency of data dissemination against rapid variation of network topology. In this paper, we focus on the scenario of roadside unit (RSU) caching, where multiple content providers (CPs) aim to improve the data dissemination of their own contents by utilizing the storages of RSUs. To deal with the competition among multiple CPs for limited caching facilities, we propose a multi-object auction-based solution, which is sub-optimal and efficient to be carried out. A caching-specific handoff decision mechanism is also adopted to take advantages of the overlap of RSUs. Simulation results show that our solution leads to a satisfactory outcome.

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we exploit non-orthogonal multiple access to reduce the access latency and to improve the packet reception probability. In the proposed two-fold scheme, the BS performs semi-persistent scheduling and allocates time-frequency resources in a non-orthogonal manner while the vehicles autonomously perform distributed power control with iterative signaling control. We formulate the centralized scheduling and resource allocation problem as equivalent to a multi-dimensional stable roommate matching problem, in which the users and time/frequency resources are considered as disjoint sets of objects to be matched with each other. We then develop a novel rotation matching algorithm, which converges to an $L$ -rotation stable matching after a limited number of iterations. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the access latency and reliability.

Non-orthogonal multiple access (NOMA) schemes have been proposed for the next generation of mobile communication systems to improve the access efficiency by allowing multiple users to share the same spectrum in a non-orthogonal way. Due to the strong co-channel interference among mobile users introduced by NOMA, it poses significant challenges for system design and resource management. This article reviews resource management issues in NOMA systems. The main taxonomy of NOMA is presented by focusing on the following two categories: power-domain NOMA and code-domain NOMA. Then a novel radio resource management framework is presented based on game-theoretic models for uplink and downlink transmissions. Finally, potential applications and open research directions in the area of resource management for NOMA are provided., 15 pages, 3 figures, IEEE Network, 2017

LTE-Unlicensed has recently captured intense attention from both academic and industrial fields. By integrating the unlicensed spectrum with the licensed spectrum, using carrier aggregation, LTE-Unlicensed users can experience enhanced transmission while maintaining the seamless mobility management and predictable performance. However, due to different transmission regulations, the coordination between LTE and Wi-Fi systems requires careful design. It is especially important to understand how to guarantee the transmission quality for LTE users and reduce Wi-Fi users’ performance degradation, under the impact of the co-channel interference. In other words, how can we solve the unlicensed resource allocation problem under both LTE and Wi-Fi transmission requirements? In this paper, we propose a matching theory framework to tackle this problem. Specifically, the coexistence between LTE and Wi-Fi systems, i.e., the interaction between LTE and Wi-Fi users, is modeled as a stable marriage game. The coexistence constraints are interpreted as the preference lists. Two semi-distributed solutions, namely, the Gale–Shapley and the random path to stability algorithms are proposed. In addition, to address the external effect in matching, the inter-channel cooperation algorithm is introduced. Last but not least, the resource allocation problem is studied with network dynamics and the proposed mechanisms are evaluated under two typical user mobility models.

Device-to-device (D2D) communication, which enables direct communication between nearby mobile devices, is an attractive add-on component to improve spectrum efficiency and user experience by reusing licensed cellular spectrum in 5G system. In this paper, we propose to enable D2D communication in unlicensed spectrum (D2D-U) as an underlay of the uplink LTE network for further booming the network capacity. A sensing-based protocol is designed to support the unlicensed channel access for both LTE and D2D users. We further investigate the subchannel allocation problem to maximize the sum rate of LTE and D2D users while considering their interference to the existing Wi-Fi systems. Specifically, we formulate the subchannel allocation as a many-to-many matching problem with externalities, and develop an iterative user-subchannel swap algorithm. Analytical and simulation results show that the proposed D2D-U scheme can significantly improve the system sum rate.

Explosive demand for wireless internet services has posed critical challenges for wireless network due to its limited capacity. To tackle this hurdle, wireless Internet service providers (WISPs) take the smart data pricing to manage data traffic loads. Meanwhile, from the users' perspective, it is also reasonable and desired to employ mobile data traffic management under the pricing policies of WISPs to improve the economic efficiency of data consumption. In this paper we introduce a concept of cost efficiency for user's mobile data management, defined as the ratio of user's mobile data consumption benefits and its expense. We propose an integrated cost-efficiency-based data traffic management scheme including long-term data demand planning, short-term data traffic pre-scheduling and real-time data traffic management. The real-time data traffic management algorithm is proposed to coordinate user's data consumption to tailor to the pre-scheduled data traffic profile. Numerical results demonstrate the effectiveness of cost efficiency framework in indicating and motivating mobile user's data consumption behavior. The proposed management scheme can effectively motivate the user to adjust its data consumption profile to obtain the optimal data consumption cost efficiency., 23 pages, 13 figures

In wireless ad hoc networks, each node participates in routing by forwarding data to other nodes without a pre-existing infrastructure. Particularly, with the wide adoption of smart devices, the concept of smartphone ad hoc networks (SPANs) has evolved to enable alternate means for information sharing. Using unlicensed frequency spectrum and short-range wireless technologies, a SPAN enables a new paradigm of applications and thus is seen as an attractive component in future wireless networks. In a SPAN, smartphones form local peer-to-peer networks to cooperate and share information efficiently. Recent studies have shown that if the users’ social relations are considered while designing cooperation schemes and protocols in SPANs, the cooperation initialization and content dissemination can be notably improved to increase the overall network efficiency and communications reliability. In this article, we present a social-aware framework for optimizing SPANs by exploiting two layers: users’ relationships in the online social network layer and users’ offline connections and interactions in the physical wireless network layer. The online content popularity distribution is also studied as a result of the users’ online interaction profiles. In the end, we integrate both online and offline layers, and discuss possible applications to further enhance the network performance.

Cloud radio access network (C-RAN) has emerged as a promising solution to support exponentially increasing demand in data rate. The attractive capacity enhancement mainly comes from centralized and coordinated processing, which poses great challenges on computing capability in the baseband unit pool. This requires the efficient allocation of computing resources to minimize the hardware and energy costs of C-RANs. Therefore, in this letter, we first model the computing resource consumption of joint downlink transmissions from remote radio heads (RRHs) to users. Then, we investigate the computing resource minimization problem on how much computing capability is needed given certain number of RRHs and user density. Numerical results show that the computing resource consumption increases non-linearly with the user density. In particular, the required computing resource drastically increases when densely populated hotspots are present in the system.

It is known that the capacity of the cellular network can be significantly improved when cellular operators are allowed to access the unlicensed spectrum. Nevertheless, when multiple operators serve their user equipments (UEs) in the same unlicensed spectrum, the inter-operator interference management becomes a challenging task. In this paper, we develop a multi-operator multi-UE Stackelberg game to analyze the interaction between multiple operators and the UEs subscribed to the services of the operators in unlicensed spectrum. In this game, to avoid intolerable interference to the Wi-Fi access point (WAP), each operator sets an interference penalty price for each UE that causes interference to the WAP, and the UEs can choose their sub-bands and determine the optimal transmit power in the chosen sub-bands of the unlicensed spectrum. Accordingly, the operators can predict the possible actions of the UEs and hence set the optimal prices to maximize its revenue earned from UEs. Furthermore, we consider two possible scenarios for the interaction of operators in the unlicensed spectrum. In the first scenario, referred to as the non-cooperative scenario, the operators cannot coordinate with each other in the unlicensed spectrum. A sub-gradient approach is applied for each operator to decide its best-response action based on the possible behaviors of others. In the second scenario, referred to as the cooperative scenario, all operators can coordinate with each other to serve UEs and control the UEs’ interference in the unlicensed spectrum. Simulation results have been presented to verify the performance improvement that can be achieved by our proposed schemes.

Dedicated UAVs can be used as communication platforms in the way as wireless access points or relays nodes, to further assist the terrestrial communications. This type of applications can be referred to as UAV assisted cellular communications. UAV assisted cellular communications have numerous use cases, including traffic offloading, wireless backhauling, swift service recovery after natural disasters, emergency response, rescue and search, information dissemination/broadcasting, and data collection from ground sensors for machine-type communications. However, different from traditional cellular networks, how to plan the time-variant placements of the UAVs served as BS/relay is very challenging due to the complicated 3D propagation environments as well as many other practical constraints such as power and flying speed. In addition, spectrum sharing with existing cellular networks is another interesting topic to investigate.

As distributed energy resources (DERs) are widely deployed, the DC packetized power microgrid is a promising solution to incorporate DERs effectively and steadily. In this paper, we consider a DC microgrid, where the energy is dispatched by a power router in the form of power packets. Since energy subscribers (ESs) with DERs in the microgrid can generate surplus electricity, the peer-to-peer (P2P) energy trading is an effective way in order to balance the energy. Different from the P2P trading in AC smart grids, the dispatching capability of the router in the DC microgrid needs to be considered, which will make the trading problem more complicated. To tackle this challenge, we formulate the P2P trading problem as an auction game, where the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. The effectiveness of the proposed scheme is validated through simulations.

Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing~(MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer some disadvantages of limited computing capacity, preventing computation-intensive tasks to be processed on time. To relief this issue, we propose a heterogeneous multi-layer MEC (HetMEC) network where data that cannot be timely processed at the edge are allowed to be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We design the latency minimization algorithm by jointly coordinating the task assignment, computing and transmission resources among the EDs, multi-layer MEC servers, and the CC. Simulation results indicate that our proposed algorithm can achieve a lower latency and stronger robustness than the conventional MEC schemes.

In this poster, we consider the Peer-to-Peer (P2P) energy trading in a local area packetized power network (LAPPN), where demander energy subscribers (ESs) can buy energy from supplier ESs or the utility grid (UG). Since the energy is transmitted in the form of power packets in the power channels in a time division multiplex (TDM) manner, the limited channel resources should be considered in the trading. The trading problem is formulated, in which selfish demander ESs compete for power packets and channels to maximize their own utilities, while the controller maximizes the total revenue. To tackle this problem, an iterative auction scheme is proposed, and its effectiveness is validated by simulation.

In this paper, we consider a device-to-device (D2D) communications underlaying cellular network where Long Term Evolution (LTE) and D2D users are allowed to communicate over both licensed and unlicensed bands for spectrum efficiency improvement. LTE users utilize spectrum orthogonally and share it with D2D users. To maximize the total throughput of this D2D system, we leverage stochastic geometry to derive the throughput for each kind of users by modeling the deployment of users as Poisson point processes (PPPs), and investigate the mode selection problem for D2D users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP) based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm.

The DC power packet transmission contributes to a reliable integration of distributed energy resources (DERs) in power grids and reduces the load fluctuation. In this paper, we consider a DC packetized-power microgrid for the integration of DERs and propose a power packet dispatching protocol to regulate the peer to peer power interchange within the microgrid. We formulate the joint subscriber matching and energy allocation problem to optimize the subscribers' benefits, which is proved to be NP-hard. Based on the matching theory, we associate the problem equivalent to a many-to-many matching problem and design a two-sided matching algorithm to solve it. We then design a graph coloring based algorithm to schedule the energy transmissions of the matched energy subscribers. Simulation results validate the effectiveness of the proposed protocol in achieving a steady and efficient microgrid power dispatching.

In this paper, we consider an uplink cellular Internet-of-Things (IoT) network, where a cellular user (CU) can serve as the mobile data aggregator for a cluster of IoT devices. To be specific, the IoT devices can either transmit the sensory data to the base station (BS) directly by cellular communications, or first aggregate the data to a CU through Machine-to-Machine (M2M) communications before the CU uploads the aggregated data to the BS. To support massive connections, the IoT devices can leverage the unlicensed spectrum for M2M communications, referred to as IoT Unlicensed (IoT-U). Aiming to maximize the number of scheduled IoT devices and meanwhile associate each IoT devices with the right CU or BS with the minimum transmit power, we first introduce a single-stage formulation that captures these objectives simultaneously. To tackle the NP-hard problem efficiently, we decouple the problem into two subproblems, which are solved by successive linear programming and convex optimization techniques, respectively. Simulation results show that the proposed IoT-U scheme can support more IoT devices than that only using the licensed spectrum., Accepted by IEEE Transactions on Wireless Communications

In this paper, we consider device-to-device (D2D) communications as an underlay to the cellular networks over both licensed and unlicensed spectrum, where Long Term Evolution (LTE) users utilize the spectrum orthogonally while D2D users share the spectrum with LTE users. In the system, each LTE and D2D user can access the licensed or unlicensed band for communications. To maximize the total throughput of the system, we leverage stochastic geometry to derive the throughput for each kind of users by modeling the deployment of users as Poisson point processes (PPPs), and investigate the spectrum access problem for these users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP) based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm., Comment: 14 pages, 9 figures, Journal

Integrating LTE-A into unlicensed spectrum using carrier aggregation, LTE-Unlicensed is considered as a promising solution with the benefits of enhanced transmission and seamless user experience. However, it is still facing some major design challenges, especially the coexistence of multiple systems (LTE/Wi-Fi) within the shared unlicensed spectrum. In this work, a matching game framework is presented to tackle the coexistence issues. Specifically, wireless users (i.e., cellular users and Wi-Fi users), which interfere with each other when they operate on the same unlicensed band, are modeled as matching players. The coexistence issues between the LTE/Wi-Fi systems can thus be modeled as the interactions between LTE/Wi-Fi users using suitable matching games. In addition, the matching framework allows definition of the preference lists and matching optimality as ways to interpret various system requirements and objectives. Together with the low-complexity and semi-distributive implemented algorithms, the matching framework can accommodate many application-oriented coexistence issues in LTE-Unlicensed. Specific implementation scenarios, as well as experimental results, are analyzed to demonstrate the potential of the matching-based approaches in the LTE-Unlicensed design.

Recently proposed wireless proactive caching has opened up a promising avenue for reducing the redundant traffic load of the future wireless network. In addition to infrastructure updating, caching schemes for different wireless caching scenarios are also essential problems of concern. Along with the benefits brought by proactive caching, the cost of proactive caching should also be taken into account, since caching schemes can be influenced by different interest relevant parties in wireless networks. In this article, we discuss several typical caching scenarios and apply corresponding game theoretical models to illustrate how the selfishness of different parties may influence the overall wireless proactive caching. Efficient caching strategies can be designed by carefully considering the relations and interactions among these parties by using game theory. We also outline possible future research directions in this emerging area.

The recent significant progress in realizing FD systems has opened up a promising avenue for increasing the capacity of future wireless networks. In addition to hardware design and signal processing techniques, radio resource management is essential in the design and implementation of practical FD communication networks. FD communications introduce additional dimensions of network resources requiring the development of new resource allocation algorithms. In this article we concentrate on distributed radio resource allocation, and show how game-theoretic models can be used to effectively solve the optimization problems in FD communication systems. Finally, we outline several key open research directions in this emerging area.

Full-duplex wireless communication enables concurrent transmission and reception over the same radio channel. It thus achieves higher efficiency in spectrum utilization compared with its half-duplex counterpart. In recent years, researchers have built several platforms for full-duplex wireless communications. However, none of these platforms provides full support for full-duplex WiFi, arguably the most popular wireless local-area networking technology. In this paper, we present GRT-duplex, a novel full-duplex wireless platform for the WiFi family, which possesses both flexibility and real-time features. We have also prototyped a full-duplex WiFi instance on this platform, which delivered throughput up to 92.45Mbps, and the frame interaction intervals can be as small as 9.85us.

The device in conventional half-duplex (HD) WiFi networks cannot perform carrier sensing while in data transmission, thus it suffers from long collision duration. To mitigate this problem, this letter presents a new WiFi cross-layer protocol design based on CSMA/CD, which facilitates simultaneous carrier sensing and data transmission by using the full-duplex (FD) technology. As the FD technology introduces the residual self-interference (RSI), we study two types of errors caused by the RSI against the FD-WiFi sensing (i.e., false alarm and miss detection). Meanwhile, we derive the normalized throughput when considering the sensing performance in the proposed FD-WiFi CSMA/CD protocol. Both analytical and simulated results show that sensing threshold should be properly designed to balance the two types of sensing errors, and our proposed cross-layer protocol can achieve significant throughput improvement.