Showing papers on "Channel allocation schemes published in 2017"
TL;DR: In this article, a logical architecture for network-slicing-based 5G systems is introduced, and a scheme for managing mobility between different access networks, as well as a joint power and subchannel allocation scheme in spectrum sharing two-tier systems based on network slicing, where both the co-tier interference and crosstier interference are taken into account.
Abstract: 5G networks are expected to be able to satisfy users' different QoS requirements. Network slicing is a promising technology for 5G networks to provide services tailored for users' specific QoS demands. Driven by the increased massive wireless data traffic from different application scenarios, efficient resource allocation schemes should be exploited to improve the flexibility of network resource allocation and capacity of 5G networks based on network slicing. Due to the diversity of 5G application scenarios, new mobility management schemes are greatly needed to guarantee seamless handover in network-slicing-based 5G systems. In this article, we introduce a logical architecture for network-slicing-based 5G systems, and present a scheme for managing mobility between different access networks, as well as a joint power and subchannel allocation scheme in spectrum-sharing two-tier systems based on network slicing, where both the co-tier interference and cross-tier interference are taken into account. Simulation results demonstrate that the proposed resource allocation scheme can flexibly allocate network resources between different slices in 5G systems. Finally, several open issues and challenges in network-slicing-based 5G networks are discussed, including network reconstruction, network slicing management, and cooperation with other 5G technologies.
585 citations
TL;DR: In this paper, a novel framework for optimizing the performance of such UAV-based wireless systems in terms of the average number of bits (data service) transmitted to users as well as the UAVs' hover duration (i.e. flight time) is proposed.
Abstract: In this paper, the effective use of flight-time constrained unmanned aerial vehicles (UAVs) as flying base stations that provide wireless service to ground users is investigated. In particular, a novel framework for optimizing the performance of such UAV-based wireless systems in terms of the average number of bits (data service) transmitted to users as well as the UAVs’ hover duration (i.e. flight time) is proposed. In the considered model, UAVs hover over a given geographical area to serve ground users that are distributed within the area based on an arbitrary spatial distribution function. In this case, two practical scenarios are considered. In the first scenario, based on the maximum possible hover times of UAVs, the average data service delivered to the users under a fair resource allocation scheme is maximized by finding the optimal cell partitions associated to the UAVs. Using the powerful mathematical framework of optimal transport theory, this cell partitioning problem is proved to be equivalent to a convex optimization problem. Subsequently, a gradient-based algorithm is proposed for optimally partitioning the geographical area based on the users’ distribution, hover times, and locations of the UAVs. In the second scenario, given the load requirements of ground users, the minimum average hover time that the UAVs need for completely servicing their ground users is derived. To this end, first, an optimal bandwidth allocation scheme for serving the users is proposed. Then, given this optimal bandwidth allocation, optimal cell partitions associated with the UAVs are derived by exploiting the optimal transport theory. Simulation results show that our proposed cell partitioning approach leads to a significantly higher fairness among the users compared with the classical weighted Voronoi diagram. Furthermore, the results demonstrate that the average hover time of the UAVs can be reduced by 64% by adopting the proposed optimal bandwidth allocation scheme as well as the optimal cell partitioning approach. In addition, our results reveal an inherent tradeoff between the hover time of UAVs and bandwidth efficiency while serving the ground users.
321 citations
TL;DR: In this article, the authors investigated the optimal power allocation with given channel assignment over multiple channels under different performance criteria, namely, maximin fairness, weighted sum rate maximization, sum rate minimization with quality of service (QoS) constraints, and energy efficiency maximization with weights or QoS constraints in downlink NOMA systems.
Abstract: Non-orthogonal multiple access (NOMA) enables power-domain multiplexing via successive interference cancellation (SIC) and has been viewed as a promising technology for 5G communication. The full benefit of NOMA depends on resource allocation, including power allocation and channel assignment, for all users, which, however, leads to mixed integer programs. In the literature, the optimal power allocation has only been found in some special cases, while the joint optimization of power allocation and channel assignment generally requires exhaustive search. In this paper, we investigate resource allocation in downlink NOMA systems. As the main contribution, we analytically characterize the optimal power allocation with given channel assignment over multiple channels under different performance criteria. Specifically, we consider the maximin fairness, weighted sum rate maximization, sum rate maximization with quality of service (QoS) constraints, and energy efficiency maximization with weights or QoS constraints in NOMA systems. We also take explicitly into account the order constraints on the powers of the users on each channel, which are often ignored in the existing works, and show that they have a significant impact on SIC in NOMA systems. Then, we provide the optimal power allocation for the considered criteria in closed or semi-closed form. We also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.
254 citations
TL;DR: A framework based on stochastic geometry for D2D multichannel overlaying uplink cellular networks is presented, able to model and analyze how different parameters affect the coverage probability and ergodic rate of users in the cellular network.
Abstract: Device-to-device (D2D) communication, which enables two closely located users to communicate with each other without traversing the base station (BS), has become an emerging technology for network engineers to optimize network performance. This paper presents a framework based on stochastic geometry for D2D multichannel overlaying uplink cellular networks. In this framework, a part of mobile devices and machines (namely cellular users) can upload data to the nearest BSs directly through cellular channels, the other mobile devices and machines (namely D2D users) must upload data to their own relays through D2D channels, and then, the relays communicate with the nearest BSs through cellular channels. D2D users upload data with a fixed transmit power, while cellular users and D2D relays do so by adopting the channel inversion power control with maximum transmit power limit. This tractable framework is able to model and analyze how different parameters affect the coverage probability and ergodic rate of users in the cellular network. As validated by extensive numerical results, the framework can help us to determine the optimal channel allocation to achieve the best network performance efficiently.
71 citations
TL;DR: Numerical results demonstrate that the proposed greedy algorithm can achieve close-to-optimal performance and that the heuristic algorithm provides good performance, even though it is inferior than that of the greedy, with much lower complexity.
Abstract: In this paper, we present a framework for resource allocations for multicast device-to-device (D2D) communications underlaying the uplink of a Long-Term Evolution (LTE) network. The objective is to maximize the sum throughput of active cellular users (CUs) and feasible D2D multicast groups in a cell, while meeting a certain signal-to-interference-plus-noise ratio (SINR) constraint for both the CUs and the D2D groups. We formulate the general problem of power and channel allocation as a mixed integer nonlinear programming (MINLP) problem, where one D2D group can reuse the channels of multiple CUs and where the channel of each CU can be reused by multiple D2D groups. Distinct from existing approaches in the literature, our formulation and solution methods provide an effective and flexible means to utilize radio resources in cellular networks and share them with multicast groups without causing harmful interference to each other. The MINLP problem is transformed so that it can be solved optimally by a variant of the generalized Bender decomposition method with provable convergence. A greedy algorithm and a low-complexity heuristic solution are then devised. The performance of all schemes is evaluated through extensive simulations. Numerical results demonstrate that the proposed greedy algorithm can achieve close-to-optimal performance and that the heuristic algorithm provides good performance, even though it is inferior than that of the greedy, with much lower complexity.
70 citations
01 May 2017
TL;DR: A Link Quality Estimator (LQE) for Industrial WSN, and a new type of node, the LQE node, that estimates the link quality in real-time, using the Received Signal Strength Indication (RSSI), and information obtained from received data packets are proposed.
Abstract: Adaptive mechanisms, such as dynamic channel allocation or adaptive routing, are used to deal with the variations in the link quality of Wireless Sensor Networks (WSN). In both cases, the first step is to estimate the link quality, so that the network nodes can decide if a channel or route change is needed. This paper proposes a Link Quality Estimator (LQE) for Industrial WSN, and a new type of node, the LQE node, that estimates the link quality in real-time, using the Received Signal Strength Indication (RSSI), and information obtained from received data packets. The proposed LQE is capable of capturing the effects of multipath, interference, and link asymmetry. Experiments were performed in a real industrial environment using IEEE 802.15.4 radios, and models were developed to allow the use of RSSI samples to proper estimate the link quality. A comparison was performed with a state-of-the-art LQE, the Opt-FLQE, and the results showed that the proposed estimator is more accurate and reactive for the type of environment in study. Different from other LQEs in literature, in the proposed LQE the sensor nodes do not need to send broadcast probe packets. Besides, using the LQE node, the other nodes of the WSN do not need to stop their operation to monitor the link quality.
67 citations
TL;DR: Numerical results reveal that the optimal spectrum allocation rules can significantly vary for IBFD and OBFD backhauling, and proposes and comparatively analyzes the performance of two distributed backhaul spectrum allocation schemes, namely, maximum received signal power (max-RSP) and minimum received signalPower (min-R SP) schemes.
Abstract: In-band full-duplex (IBFD) backhauling is a potential technique for wireless backhauling of small cells that allows the use of same spectrum for the backhaul and access links of the small cell base stations (SBSs) concurrently, however, at the expense of backhaul interference and self-interference (SI). This paper investigates the problem of optimal access/backhaul spectrum allocation considering IBFD backhauling, out-of-band full-duplex (OBFD) backhauling (in which the access and backhaul transmissions take place on different spectrum), and the SBSs with the provisioning for hybrid IBFD/OBFD backhauling. We first formulate a problem to maximize the minimum achievable rate (i.e., minimum of the rates in the backhaul link and the access link) at the SBSs in a hybrid IBFD/OBFD setting. The solution of the centralized spectrum allocation problem, which serves as a benchmark for any sub-optimal solution, is provided by transforming the original problem into an epigraph form. As a special case of the formulated problem, we derive closed-form optimal solutions for the access/backhaul spectrum allocation of OBFD backhauling as well as IBFD backhauling. We then propose and comparatively analyze the performance of two distributed backhaul spectrum allocation schemes, namely, maximum received signal power (max-RSP) and minimum received signal power (min-RSP) schemes. For these schemes, we theoretically derive the number of allocated backhaul channels, minimum rate coverage probability, and average achievable rate of each SBS given its distance from the centralized wireless backhaul hub (WBH) for both IBFD and OBFD backhauling. Numerical results reveal that the optimal spectrum allocation rules can significantly vary for IBFD and OBFD backhauling. Optimal OBFD backhauling favors more backhaul spectrum for SBSs located far-away from the WBH. With IBFD backhauling, spectrum allocation for SBSs strongly depends on SI. With the reduction in SI, the optimal backhaul spectrum increases/decreases for nearby/farther SBSs. Simulation results comparing the optimal solution with the distributed spectrum allocation solutions based on max-RSP and min-RSP schemes are also presented.
66 citations
TL;DR: A channel resource allocation scheme based on a semi-Markov decision policy to provide a solution for the problem of the channel resource shortage in vehicular ad hoc networks (VANETs) under a cognitive enabled VANET environment is proposed.
Abstract: An efficient channel allocation scheme for vehicular networks is required due to the popularization and rapid growth of the corresponding applications in recent years. We propose a channel resource allocation scheme based on a semi-Markov decision policy to provide a solution for the problem of the channel resource shortage in vehicular ad hoc networks (VANETs). In this paper, we consider the channel allocation problem under a cognitive enabled VANET environment. By a semi-Markov decision process (SMDP), the channel allocation decision is made to maximize the overall system rewards. Besides, we consider services from two categories: primary users (PUs) and secondary users. On the top of the overall rewards maximization, we give priority to PU services without blocking any PU requests via cooperation between the roadside units and the base station. Numerical results and evaluations are presented to illustrate the desired performance of the proposed channel allocation scheme.
65 citations
TL;DR: A novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission and demonstrates a superior video transmission performance compared with the existing methods.
Abstract: Video transmission is an indispensable component of most applications related to the mobile cloud networks (MCNs). However, because of the complexity of the communication environment and the limitation of resources, attempts to develop an effective solution for video transmission in the MCN face certain difficulties. In this paper, we propose a novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission. A video clustering model is designed based on game theory to classify the different video parts stored in mobile devices. Using the results of video clustering, the GVT algorithm provides the function of channel assignment, and its assignment process depends on the content of the video to improve channel utilization in the MCN. Extensive simulations are carried out to evaluate the GVT with several performance criteria. Our analysis and simulations show that the proposed GTV demonstrates a superior video transmission performance compared with the existing methods.
64 citations
TL;DR: This paper considers the case of a two-tier cellular network with IBFD-enabled small cells, wirelessly backhauling themselves with conventional macro cells, and introduces an end-to-end joint analysis of backhaul (or fronthaul) and access links, in contrast to the largely available access-centric studies.
Abstract: With the successful demonstration of in-band full-duplex (IBFD) transceivers, a new research dimension has been added to wireless networks. This paper proposes a use case of this capability for IBFD self-backhauling heterogeneous networks (HetNets). IBFD self-backhauling in a HetNet refers to IBFD-enabled small cells backhauling themselves with macro cells over the wireless channel. Owing to their IBFD capability, the small cells simultaneously communicate over the access and backhaul links, using the same frequency band. The idea is doubly advantageous, as it obviates the need for fiber backhauling small cells every hundred meters and allows the access spectrum to be reused for backhauling at no extra cost. This paper considers the case of a two-tier cellular network with IBFD-enabled small cells, wirelessly backhauling themselves with conventional macro cells. For clear exposition, the case considered is that of the Frequency Division Duplexing (FDD) network, where within access and backhaul links, the downlink (DL) and uplink are frequency duplexed ( $f1$ , $f2$ respectively), while the total frequency spectrum used at access and backhaul ( $f1+f2$ ) is the same. Analytical expressions for coverage and average DL rate in such a network are derived using tools from the field of stochastic geometry . It is shown that DL rate in such networks could be close to double that of a conventional TDD/FDD self-backhauling network, at the expense of reduced coverage due to higher interference in IBFD networks. For the proposed IBFD network, the conflicting aspects of increased interference on one side and high spectral efficiency on the other are captured into a mathematical model. The mathematical model introduces an end-to-end joint analysis of backhaul (or fronthaul) and access links, in contrast to the largely available access-centric studies.
63 citations
TL;DR: This paper develops a priority-based channel allocation scheme to assign channels to the mobile stations based on their QoE requirements, and proposes a handoff management technique to overcome the interruptions caused by the handoff.
Abstract: Cognitive radio (CR) is among the promising solutions for overcoming the spectrum scarcity problem in the forthcoming fifth-generation (5G) cellular networks, whereas mobile stations are expected to support multimode operations to maintain connectivity to various radio access points. However, particularly for multimedia services, because of the time-varying channel capacity, the random arrivals of legacy users, and the on-negligible delay caused by spectrum handoff, it is challenging to achieve seamless streaming leading to minimum quality of experience (QoE) degradation. The objective of this paper is to manage spectrum handoff delays by allocating channels based on the user QoE expectations, minimizing the latency, providing seamless multimedia service, and improving QoE. First, to minimize the handoff delays, we use channel usage statistical information to compute the channel quality. Based on this, the cognitive base station maintains a ranking index of the available channels to facilitate the cognitive mobile stations. Second, to enhance channel utilization, we develop a priority-based channel allocation scheme to assign channels to the mobile stations based on their QoE requirements. Third, to minimize handoff delays, we employ the hidden markov model (HMM) to predict the state of the future time slot. However, due to sensing errors, the scheme proactively performs spectrum sensing and reactively acts handoffs. Fourth, we propose a handoff management technique to overcome the interruptions caused by the handoff. In such a way that, when a handoff is predicted, we use scalable video coding to extract the base layer and transmit it during a certain interval time before handoff occurrence to be shown during handoff delays, hence providing seamless service. Our simulation results highlight the performance gain of the proposed framework in terms of channel utilization and received video quality.
TL;DR: A wireless powered wideband CR network is considered, and a practical non-linear energy harvesting model is adopted to maximize the sum throughput of the secondary users, and the energy harvesting time, channel allocation, and transmit power are jointly optimized.
Abstract: Wireless powered techniques have been recognized as promising techniques in future wireless communication systems, especially in cognitive radios (CRs) with energy-limit devices. However, most of the existing works focus on CRs with an ideal linear energy harvesting model. In this paper, a wireless powered wideband CR network is considered, and a practical non-linear energy harvesting model is adopted. To maximize the sum throughput of the secondary users, the energy harvesting time, channel allocation, and transmit power are jointly optimized. The closed-form expressions for the optimal transmit power and channel allocation are given. Simulation results show that there is a tradeoff between the harvesting energy and the sum throughput of the secondary users. It is also shown that the performance achieved under the non-linear energy harvesting model may equal to that achieved under the linear energy harvesting model.
01 Dec 2017
TL;DR: Numerical results show that the proposed hybrid network with optimized spectrum sharing and cyclical multiple access design significantly improves the spatial throughput over the conventional cellular network with the GBS only.
Abstract: In conventional terrestrial cellular systems, mobile terminals (MTs) at the cell edge often pose the performance bottleneck due to their long distance from the ground base station (GBS), especially in hotspot areas. This paper proposes a new hybrid network architecture by leveraging the use of unmanned aerial vehicle (UAV) as an aerial mobile base station, which flies cyclically along the cell edge to serve the cell- edge MTs and help offloading the traffic from the GBS. To achieve user fairness, we aim to maximize the minimum throughput of all MTs in a single cell by jointly optimizing the UAV's trajectory, as well as the bandwidth allocation and user partitioning between the UAV and GBS. Numerical results show that the proposed hybrid network with optimized spectrum sharing and cyclical multiple access design significantly improves the spatial throughput over the conventional cellular network with the GBS only.
TL;DR: Numerical results show that compared with current LTE networks, the hybrid system with C/U split can achieve approximately 40% and 80% EE improvement in sparse and ultra-dense networks respectively, and greatly enhance the coverage.
Abstract: In order to improve the manageability and adaptability of future 5G wireless networks, the software orchestration mechanism, named software defined networking (SDN) with control and user plane (C/U-plane) decoupling, has become one of the most promising key techniques. Based on these features, the hybrid satellite terrestrial network is expected to support flexible and customized resource scheduling for both massive machinetype- communication (MTC) and high-quality multimedia requests while achieving broader global coverage, larger capacity and lower power consumption. In this paper, an end-to-end hybrid satellite terrestrial network is proposed and the performance metrics, e. g., coverage probability, spectral and energy efficiency (SE and EE), are analysed in both sparse networks and ultra-dense networks. The fundamental relationship between SE and EE is investigated, considering the overhead costs, fronthaul of the gateway (GW), density of small cells (SCs) and multiple quality-ofservice (QoS) requirements. Numerical results show that compared with current LTE networks, the hybrid system with C/U split can achieve approximately 40% and 80% EE improvement in sparse and ultra-dense networks respectively, and greatly enhance the coverage. Various resource management schemes, bandwidth allocation methods, and on-off approaches are compared, and the applications of the satellite in future 5G networks with software defined features are proposed.
TL;DR: Simulation results demonstrate that, compared with heuristic algorithm and stochastic algorithm, the proposed D2D multicast scheme can increase the throughput of the overall social-aware network by about 5% and 50%, respectively.
Abstract: With the ever-increasing demands for popular content sharing among humans, device-to-device (D2D) multicast communication, as a promising technology to support wireless services within a local area, is introduced in a 5G cellular network. However, the existing resource allocation approaches for D2D multicast communication usually consider only physical domain constraints but neglect social domain factors, which would result in ineffective D2D links between users unwilling to share interests. Different from existing works, the D2D multicast scheme proposed in this paper will produce effective D2D multicast links by sufficiently utilizing both the physical and social properties of mobile users, with the goal to maximize the throughput of the overall social-aware network and guarantee fairly allocation of the channel between different D2D multicast clusters. The scheme mainly consists of two parts, the formation of D2D multicast clusters and joint optimization of power and channel allocation. In the formation of D2D multicast clusters, members and head in each cluster are selected by taking into account both social attributes and physical factors, such as community, ties, and geographical closeness. In the joint optimization, a two-step scheme is designed to first calculate the optimal power allocation by geometric proximity and then select suitable cellular channels for each D2D multicast cluster utilizing an extended one-to-many bipartite graphs matching algorithm. Simulation results demonstrate that, compared with heuristic algorithm and stochastic algorithm, the proposed scheme can increase the throughput of the overall social-aware network by about 5% and 50%, respectively.
TL;DR: This letter divides the original problem into two subproblems and proposes an iterative algorithm with low complexity to solve it and shows that the proposed algorithm converges rapidly and the EE of D2D links can be significantly improved compared with existing methods especially for an increasing number of CUs.
Abstract: In this letter, we study the joint channel allocation and power control problem to maximize the energy efficiency (EE) of device-to-device (D2D) links in a D2D underlaid cellular network. Due to the location dispersion of D2D pairs and short-distance D2D transmission, it should be preferred that multiple D2D pairs can simultaneously share the resource with cellular users (CUs). To address the nonconvexity of the EE maximization problem, we divide the original problem into two subproblems and propose an iterative algorithm with low complexity to solve it. Simulation results show that the proposed algorithm converges rapidly and the EE of D2D links can be significantly improved compared with existing methods especially for an increasing number of CUs.
TL;DR: The theoretical analysis and the experimental results show that eBA not only guarantees the bandwidth for VMs, but also provides fast convergence to efficiency and fairness, as well as smooth response to bursty traffic.
Abstract: Datacenter networks suffer unpredictable performance due to a lack of application level bandwidth guarantees. A lot of attention has been drawn to solve this problem such as how to provide bandwidth guarantees for virtualized machines (VMs), proportional bandwidth share among tenants, and high network utilization under peak traffic. However, existing solutions fail to cope with highly dynamic traffic in datacenter networks. In this paper, we propose eBA , an efficient solution to bandwidth allocation that provides end-to-end bandwidth guarantee for VMs under large numbers of short flows and massive bursty traffic in datacenters. eBA leverages a novel distributed VM-to-VM rate control algorithm based on the logistic model under the control-theoretic framework. eBA ’s implementation requires no changes to hardware or applications and can be deployed in standard protocol stack. The theoretical analysis and the experimental results show that eBA not only guarantees the bandwidth for VMs, but also provides fast convergence to efficiency and fairness, as well as smooth response to bursty traffic.
TL;DR: In this paper, an opportunistic cooperation strategy for D2D transmission by exploiting the caching capability at the users to control the interference among D2DM links is proposed, and the closed-form expression of the bandwidth allocation factor is obtained.
Abstract: To achieve the potential in providing high throughput for cellular networks by device-to-device (D2D) communications, the interference among D2D links should be carefully managed. In this paper, we propose an opportunistic cooperation strategy for D2D transmission by exploiting the caching capability at the users to control the interference among D2D links. We consider overlay inband D2D, divide the D2D users into clusters, and assign different frequency bands to cooperative and nonco operative D2D links. To provide high opportunity for cooperative transmission, we introduce a caching policy. To maximize the network throughput, we jointly optimize the cluster size and bandwidth allocation, where the closed-form expression of the bandwidth allocation factor is obtained. Simulation results demonstrate that the proposed strategy can provide 400%–500% throughput gain over traditional D2D communications when the content popularity distribution is skewed and can provide 60%–80% gain even when the content popularity distribution is uniform.
01 Dec 2017
TL;DR: This paper formulate channel assignment as a many-to-one matching game by treating LoRa users and channels as two sets of selfish players aiming to maximize their own utilities, and proposes a low-complexity matching channel assignment algorithm (MCAA) through distributing the channel access decision making local to Lo Ra users.
Abstract: In this paper, we investigate the resource efficiency of uplink transmission for low-power wide-area (LPWA) networks. LoRa is adopted as an example network of focus, however the work can be easily generalized to other radios. We first formulate resource allocation in LPWA networks as a joint optimization problem of channel assignment and power allocation, with guaranteeing throughput fairness among LoRa users with limited spectrum resources, especially for the case with a large number of connected devices in LPWA networks. Specifically, we formulate channel assignment as a many-to-one matching game by treating LoRa users and channels as two sets of selfish players aiming to maximize their own utilities. We then propose a low-complexity matching channel assignment algorithm (MCAA) through distributing the channel access decision making local to LoRa users. For LoRa users assigned to the same channel, we further develop an optimal power allocation algorithm to maximize the achieved minimal transmission rate in LPWA networks. Moreover, simulation results demonstrate that the proposed MCAA can achieve near-optimal performance with much lower computational complexity.
TL;DR: This work considers distributed optimization over orthogonal collision channels in spatial random access networks where users are spatially distributed and each user is in the interference range of a few other users.
Abstract: We consider distributed optimization over orthogonal collision channels in spatial random access networks. Users are spatially distributed and each user is in the interference range of a few other users. Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability. We study both the non-cooperative and cooperative settings. In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users. In the latter, the goal is to achieve proportionally fair rates among users. Simple distributed learning algorithms are developed to solve these problems. The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.
TL;DR: In this paper, a scenario consisting of multiple WLANs using DCB and operating within carrier-sensing range of one another is considered, and an analytical framework for evaluating the performance of such networks is presented.
Abstract: Dynamic Channel Bonding (DCB) allows for the dynamic selection and use of multiple contiguous basic channels in Wireless Local Area Networks (WLANs). A WLAN operating under DCB can enjoy a larger bandwidth, when available, and therefore achieve a higher throughput. However, the use of larger bandwidths also increases the contention with adjacent WLANs, which can result in longer delays in accessing the channel and consequently, a lower throughput. In this paper, a scenario consisting of multiple WLANs using DCB and operating within carrier-sensing range of one another is considered. An analytical framework for evaluating the performance of such networks is presented. The analysis is carried out using a Markov chain model that characterizes the interactions between adjacent WLANs with overlapping channels. An algorithm is proposed for systematically constructing the Markov chain corresponding to any given scenario. The analytical model is then used to highlight and explain the key properties that differentiate DCB networks of WLANs from those operating on a single shared channel. Furthermore, the analysis is applied to networks of IEEE 802.11ac WLANs operating under DCB–which do not fully comply with some of the simplifying assumptions in our analysis–to show that the analytical model can give accurate results in more realistic scenarios.
TL;DR: A joint channel allocation and adaptive video streaming algorithm that makes the vehicles compete for channel access opportunities and to request video data with a proper visual quality according to their utilities is proposed.
Abstract: Video services in vehicular networks play an important role in future intelligent transportation systems and vehicular infotainment systems. Yet, at the presence of other services with high priorities, the remaining radio resources for video services are highly dynamic. To support video service of multiple vehicles in vehicular networks, we propose a joint channel allocation and adaptive video streaming algorithm that makes the vehicles compete for channel access opportunities and to request video data with a proper visual quality according to their utilities. A vehicle's request is determined by taking several key factors into consideration, including the location and the velocity of the vehicle, the activity of the high-priority services, the intensity of the competition among multiple vehicles, and the smoothness requirement of visual quality. Simulation results show that the proposed algorithm is superior to the existing algorithms in both interruption ratio and visual quality.
TL;DR: This paper proposes a delay-minimized routing (DMR) protocol for multichannel multiflow MCADNs, and designs the minimized path delay as a routing metric, and proposes a heuristic joint routing and channel assignment algorithm to solve the DMR problem.
Abstract: Cognitive radio significantly mitigates the spectrum scarcity for various applications built on wireless communication. Current techniques on mobile cognitive ad hoc networks (MCADNs), however, cannot be directly applied to time-critical applications due to channel interference, node mobility as well as unexpected primary user activities. In multichannel multiflow MCADNs, it becomes even worse because multiple links potentially interfere with each other. In this paper, we propose a delay-minimized routing (DMR) protocol for multichannel multiflow MCADNs. First, we formulate the DMR problem with the objective of delay minimization. Next, we propose a delay prediction model based on a conflict probability. Finally, we design the minimized path delay as a routing metric, and propose a heuristic joint routing and channel assignment algorithm to solve the DMR problem. Our DMR can find out the path with a minimal end-to-end (e2e) delay for time-critical data transmission. NS2-based simulation results demonstrate that our DMR protocol significantly outperforms related proposals in terms of average e2e delay, throughput, and packet loss rate.
TL;DR: Compared with EE, SE is much less sensitive to the variation of the tradeoff factor, which indicates that the proposed scheme can achieve high EE while guaranteeing a fairly large value of SE with a properly chosen EE–SE tradeoffs factor.
Abstract: We investigate the tradeoff between energy efficiency (EE) and spectrum efficiency (SE) in the heterogeneous network composed of a macro base station (BS), several pico BSs, and device-to-device (D2D) communication pairs during the uplink transmission. A utility function of the tradeoff between EE and SE is defined first. Then, we formulate the tradeoff utility maximization problem as a joint channel allocation and power control problem for cellular and D2D users. The original problem is transformed into a more tractable subtractive form, and we further decompose the problem into several subproblems that can be solved separately. Numerical results confirm the effectiveness of the proposed scheme and offer valuable insights. Compared with EE, SE is much less sensitive to the variation of the tradeoff factor, which indicates that our proposed scheme can achieve high EE while guaranteeing a fairly large value of SE with a properly chosen EE–SE tradeoff factor.
TL;DR: Simulation results demonstrate that the proposed algorithms can improve the weighted sum-rate of full-duplex D2D communications significantly both in perfect CSI and statistical CSI scenarios and confirm the accuracy of the derived closed-form expressions.
Abstract: In this paper, we investigate the resource allocation problem for multi-user full-duplex device-to-device (D2D) underlay communication, considering both perfect channel state information (CSI) and statistical CSI scenarios. In perfect CSI scenario, the weighted sum-rate maximization problem under cellular users’ minimum rate constraints is formulated as a mixed integer programming problem. To solve the challenging problem, we decouple it into two subproblems as power allocation and channel assignment. Then we proposed a power allocation algorithm based on difference of two convex functions programming and a channel assignment algorithm based on Kuhn–Munkres algorithm, respectively. In statistical CSI scenario, we formulate the resource allocation problem as an outage probability constrained weighted ergodic sum-rate maximization problem. To solve the problem, the closed-form expressions of outage probability and weighted ergodic sum-rate are derived first. Then we decouple resource allocation problem into power allocation and channel assignment. An optimization solution that consists of a 2-D global searching and Kuhn–Munkres algorithm is then developed. Simulation results demonstrate that the proposed algorithms can improve the weighted sum-rate of full-duplex D2D communications significantly both in perfect CSI and statistical CSI scenarios and confirm the accuracy of our derived closed-form expressions.
TL;DR: This paper addresses the channel allocation problem for multi-channel cognitive vehicular networks with the objective of system-wide throughput maximization and proposes a probabilistic polynomial-time approximation algorithm based on linear programming.
Abstract: Many studies show that the dedicated short range communication band allocated to vehicular communications is insufficient to carry the wireless traffic generated by emerging vehicular applications. A promising bandwidth expansion possibility presents itself through the release of large TV band spectra (i.e., the TV white space spectrum) by the Federal Communications Commission for cognitive access. One primary challenge of the so-called TV white space (TVWS) spectrum access in vehicular networks is the design of efficient channel allocation mechanisms in face of spatial-temporal variations of TVWS channels. In this paper, we address the channel allocation problem for multi-channel cognitive vehicular networks with the objective of system-wide throughput maximization. We show that the problem is an NP-hard non-linear integer programming problem, to which we present three efficient algorithms. We first propose a probabilistic polynomial-time $(1-1/e)$ -approximation algorithm based on linear programming. Next, we prove that the objective function can be written as a submodular set function, based on which we develop a deterministic constant-factor approximation algorithm with a more favorable time complexity. Then, we further modify the second algorithm to improve its approximation ratio without increasing its time complexity. Finally, we show the efficacy of our algorithms through numerical examples.
TL;DR: Numerical results confirm that the proposed method can improve the uplink EE performance for SUs and the original problem is formulated as a non-convex and mixed-integer optimization problem.
Abstract: We consider a heterogeneous network (HetNet) containing primary users (PUs) and secondary users (SUs). Ordinary cellular users are characterized as PUs, while SUs are the unlicensed users, sensors, or some other Internet of Things equipments. The PUs occupy all the channels in the HetNet and the SUs try to reuse the channels of PUs. We consider two transmission modes for SUs, i.e., the SU can associate with the base station (BS) directly or through the help of its cooperative relay. The optimization of energy efficiency (EE) of SUs is considered. Particularly, we focus on user association (BS selection, channel allocation, and mode selection) and power control to optimize the uplink EE of the communication between the SU and the BS. The original problem is formulated as a non-convex and mixed-integer optimization problem. To get a tractable solution, we propose an iterative optimization algorithm. The alterative optimization method decomposes the original problem into three subproblems. In each iteration, the three subproblems are solved by using the sum-of-ratios programming algorithm, the parametric Dinkelbach algorithm, and convex optimization. Then, the proposed scheme repeats the iteration until convergence. Numerical results confirm that the proposed method can improve the uplink EE performance for SUs.
TL;DR: It is shown that SCSE-OFDM/OFDMA offers a much higher SET than traditional OFDM/ OFDMA systems in frequency-selective fading channels.
Abstract: The use of cyclic prefix (CP) allows all existing orthogonal frequency-division multiplexing or multiple access (OFDM/OFDMA) systems to realize high-data-rate transmission in frequency-selective fading channels at the expense of losses in spectrum efficiency, energy efficiency, and transmission rate (SET). This paper proposes a CP-free OFDM/OFDMA design. Unlike traditional OFDM/OFDMA systems, the proposed CP-free OFDM/OFDMA does not insert CPs between symbols at the transmitter side. The neighboring symbols in frequency-selective fading channels will be overlapped at a receiver, and decision feedback equalization (DFE) is performed before FFT operation for intersymbol interference (ISI) removal. After DFE, each symbol consists of multipath returns with different linear shifts (LSs), which may result in intercarrier interference (ICI). Therefore, the output symbols from the DFE will be sent to a CP restoration unit for LS-to-cyclic-shift (CS) conversion. We call this a CP-free OFDM/OFDMA system symbol CS equalizing OFDM/OFDMA (SCSE-OFDM/OFDMA), whose system design is introduced in detail in this paper. We will show that SCSE-OFDM/OFDMA offers a much higher SET than traditional OFDM/OFDMA systems in frequency-selective fading channels.
TL;DR: This paper investigates the problem of QoE and energy aware SBS management, which consists of power selection, load management, and channel allocation, and proposes a two-dimensional-action extended weakly acyclic game theoretical scheme to optimize the two subproblems distributedly and iteratively.
Abstract: With the ever-growing number of mobile users and the rapid growth of wireless data service requirement, quality of experience (QoE) has emerged as an essential indicator for users, service providers, and operators. Meanwhile, to improve coverage and serve users, a lot of small cell base stations (SBSs) must be installed, and a great amount of energy is consumed. However, as far as is known, there are few works that have studied the combinatorial problem of QoE and energy aware SBS management, which jointly implements power selection, load management (SU allocation), and channel allocation. This paper investigates the problem of QoE and energy aware SBS management, which consists of power selection, load management, and channel allocation. In this paper, we resort to cloud technologies to solve such a complicated combinatorial problem and employ an iterative approach in which two subproblems are alternatively assigned and optimized at each iteration, i.e., 1) transmission power and load joint management and 2) channel allocation. We propose a two-dimensional-action extended weakly acyclic game theoretical scheme to optimize the two subproblems distributedly and iteratively. We define a novel two-dimensional-action pure strategy Nash equilibrium (2D-NE) and prove that at least one 2D-NE exists in the proposed game. With the help of cloud, we propose two kinds of better response algorithms to achieve 2D-NE of the proposed game $G_w$ . Moreover, simulation results show that the proposed approach could achieve a good QoE-energy utility performance and a high QoE energy efficiency.
TL;DR: This paper studies a distributed resource allocation that consists of subchannel- and power-level allocation in the uplink of the two-tier CFN comprised of a conventional macrocell and multiple femtocells using underlay spectrum access and proposes an autonomous framework, in which the cognitive femtocell users self-organize into disjoint groups (DJGs).
Abstract: The cognitive femtocell network (CFN) integrated with cognitive-radio-enabled technology has emerged as one of the promising solutions to improve wireless broadband coverage in indoor environments for next-generation mobile networks. In this paper, we study a distributed resource allocation that consists of subchannel- and power-level allocation in the uplink of the two-tier CFN comprised of a conventional macrocell and multiple femtocells using underlay spectrum access. The distributed resource allocation problem is addressed via an optimization problem, in which we maximize the uplink sum rate under constraints of intratier and intertier interference while maintaining the average delay requirement for cognitive femtocell users. Specifically, the aggregated interference from cognitive femtocell users to the macrocell base station (MBS) is also kept under an acceptable level. We show that this optimization problem is NP-hard and propose an autonomous framework, in which the cognitive femtocell users self-organize into disjoint groups (DJGs). Then, instead of maximizing the sum rate in all cognitive femtocells, we only maximize the sum rate of each DJG. After that, we formulate the optimization problem as a coalitional game in partition form, which obtains suboptimal solutions. Moreover, distributed algorithms are also proposed for allocating resources to the CFN. Finally, the proposed framework is tested based on the simulation results and shown to perform efficient resource allocation.