Showing papers on "Channel allocation schemes published in 2016"

Power and Channel Allocation for Non-Orthogonal Multiple Access in 5G Systems: Tractability and Computation

Abstract: A promising multi-user access scheme, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is currently under consideration for 5G systems. NOMA allows more than one user to simultaneously access the same frequency-time resource and separates multi-user signals by SIC. These render resource optimization in NOMA different from orthogonal multiple access. We provide theoretical insights and algorithmic solutions to jointly optimize power and channel allocation in NOMA. We mathematically formulate NOMA resource allocation problems, and characterize and analyze the problems’ tractability under a range of constraints and utility functions. For tractable cases, we provide polynomial-time solutions for global optimality. For intractable cases, we prove the NP-hardness and propose an algorithmic framework combining Lagrangian duality and dynamic programming to deliver near-optimal solutions. To gauge the performance of the solutions, we also provide optimality bounds on the global optimum. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance in both throughput and fairness over orthogonal multiple access as well as over a previous NOMA resource allocation scheme.

Power and Channel Allocation for Non-orthogonal Multiple Access in 5G Systems: Tractability and Computation

Abstract: Network capacity calls for significant increase for 5G cellular systems. A promising multi-user access scheme, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is currently under consideration. In NOMA, spectrum efficiency is improved by allowing more than one user to simultaneously access the same frequency-time resource and separating multi-user signals by SIC at the receiver. These render resource allocation and optimization in NOMA different from orthogonal multiple access in 4G. In this paper, we provide theoretical insights and algorithmic solutions to jointly optimize power and channel allocation in NOMA. For utility maximization, we mathematically formulate NOMA resource allocation problems. We characterize and analyze the problems' tractability under a range of constraints and utility functions. For tractable cases, we provide polynomial-time solutions for global optimality. For intractable cases, we prove the NP-hardness and propose an algorithmic framework combining Lagrangian duality and dynamic programming (LDDP) to deliver near-optimal solutions. To gauge the performance of the obtained solutions, we also provide optimality bounds on the global optimum. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance in both throughput and fairness over orthogonal multiple access as well as over a previous NOMA resource allocation scheme.

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Abstract: The cognitive radio is an emerging technology that enables dynamic spectrum access in wireless networks. The cognitive radio is capable of opportunistically using the available portions of a licensed spectrum to improve the application performance for unlicensed users. The opportunistic use of the available channels in the wireless environment requires dynamic channel assignment to efficiently utilize the available resources while minimizing the interference in the network. A challenging aspect of such algorithms is the incorporation of the channels' diverse characteristics, highly dynamic network conditions with respect to primary users' activity, and different fragmented sizes of the available channels. This paper presents a comprehensive survey on the state-of-the-art channel assignment algorithms in cognitive radio networks. We also classify the algorithms by presenting a thematic taxonomy of the current channel assignment algorithms in cognitive radio networks. Moreover, the critical aspects of the current channel assignment algorithms in cognitive radio networks are analyzed to determine the strengths and weaknesses of such algorithms. The similarities and differences of the algorithms based on the important parameters, such as routing dependencies, channel models, assignment methods, execution model, and optimization objectives, are also investigated. We also discuss open research issues and challenges of channel assignment in the cognitive radio networks.

Collaborative Multi-hop Routing in Cognitive Wireless Networks

Abstract: The collaboration of nodes in cognitive wireless networks is a large challenge This paper studies the collaborative multi-hop routing in cognitive networks We propose a new algorithm to construct the collaborative routing in multi-hop cognitive networks Our algorithm takes into account the interference among nodes including primary and secondary users The clustering and collaboration are exploited to improve the performance of collaborative routing in multi-hop cognitive wireless networks with multiple primary and secondary users By analyzing the maximum transmission distance, collaborations, transmission angle control and power control, and channel allocation, we propose a new clustering-based collaborative multi-hop cognitive routing algorithm to attain better network performance Simulation results show that our approach is feasible and effective

Exploiting mobility in proportional fair cellular scheduling: measurements and algorithms

Abstract: Proportional Fair (PF) scheduling algorithms are the de facto standard in cellular networks. They exploit the users' channel state diversity (induced by fast-fading) and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a nonstationary channel, due to slow-fading (on the order of seconds), and are associated with base stations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that exploit the prediction mechanism. Our trace-based simulations consider instances of the ((PF)2S) Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15%-55% compared to traditional PF schedulers, while improving fairness.

Fair network bandwidth allocation in iaas datacenters via a cooperative game approach

Abstract: With wide application of virtualization technology, tenants are able to access isolated cloud services by renting the shared resources in Infrastructure-as-a-Service (IaaS) datacenters. Unlike resources such as CPU and memory, datacenter network, which relies on traditional transport-layer protocols, suffers unfairness due to a lack of virtual machine (VM)-level bandwidth guarantees. In this paper, we model the datacenter bandwidth allocation as a cooperative game, toward VM-based fairness across the datacenter with two main objectives: 1) guarantee bandwidth for VMs based on their base bandwidth requirements, and 2) share residual bandwidth in proportion to the weights of VMs. Through a bargaining game approach, we propose a bandwidth allocation algorithm, Falloc, to achieve the asymmetric Nash bargaining solution (NBS) in datacenter networks, which exactly meets our objectives. The cooperative structure of the algorithm is exploited to develop an online algorithm for practical real-world implementation. We validate Falloc with experiments under diverse scenarios and show that by adapting to different network requirements of VMs, Falloc can achieve fairness among VMs and balance the tradeoff between bandwidth guarantee and proportional bandwidth sharing. Our large-scale trace-driven simulations verify that Falloc achieves high utilization while maintaining fairness among VMs in datacenters.

Network Selection and Channel Allocation for Spectrum Sharing in 5G Heterogeneous Networks

Abstract: The demand for spectrum resources has increased dramatically with the advent of modern wireless applications. Spectrum sharing, considered as a critical mechanism for 5G networks, is envisioned to address spectrum scarcity issue and achieve high data rate access, and guaranteed the quality of service (QoS). From the licensed network’s perspective, the interference caused by all secondary users (SUs) should be minimized. From secondary networks point of view, there is a need to assign networks to SUs in such a way that overall interference is reduced, enabling the accommodation of a growing number of SUs. This paper presents a network selection and channel allocation mechanism in order to increase revenue by accommodating more SUs and catering to their preferences, while at the same time, respecting the primary network operator’s policies. An optimization problem is formulated in order to minimize accumulated interference incurred to licensed users and the amount that SUs have to pay for using the primary network. The aim is to provide SUs with a specific QoS at a lower price, subject to the interference constraints of each available network with idle channels. Particle swarm optimization and a modified version of the genetic algorithm are used to solve the optimization problem. Finally, this paper is supported by extensive simulation results that illustrate the effectiveness of the proposed methods in finding a near-optimal solution.

Joint Caching, Routing, and Channel Assignment for Collaborative Small-Cell Cellular Networks

Abstract: We consider joint caching, routing, and channel assignment for video delivery over coordinated small-cell cellular systems of the future Internet. We formulate the problem of maximizing the throughput of the system as a linear program, in which the number of variables is very large. To address channel interference, our formulation incorporates the conflict graph that arises when wireless links interfere with each other due to simultaneous transmission. We utilize the column generation method to solve the problem by breaking it into a restricted master subproblem that involves a select subset of variables and a collection of pricing subproblems that select the new variable to be introduced into the restricted master problem, if that leads to a better objective function value. To control the complexity of the column generation optimization further, due to the exponential number of independent sets that arise from the conflict graph, we introduce an approximation algorithm that computes a solution that is within $\epsilon $ to optimality, at much lower complexity. Our framework demonstrates considerable gains in average transmission rate at which the video data can be delivered to the users, over the state-of-the-art Femtocaching system, of up to 46%. These operational gains in system performance map to analogous gains in video application quality, thereby enhancing the user experience considerably.

On the Interactions Between Multiple Overlapping WLANs Using Channel Bonding

Abstract: Next-generation wireless local area networks (WLANs) will support the use of wider channels, which is known as channel bonding, to achieve higher throughput. However, because both the channel center frequency and the channel width are autonomously selected by each WLAN, the use of wider channels may also increase the competition with other WLANs operating in the same area for the available channel resources. In this paper, we analyze the interactions between a group of neighboring WLANs that use channel bonding and evaluate the impact of those interactions on the achievable throughput. A continuous-time Markov network model that is able to capture the coupled dynamics of a group of overlapping WLANs is introduced and validated. The results show that the use of channel bonding can provide significant performance gains, even in scenarios with a high density of WLANs, although it may also cause unfair situations in which some WLANs receive most of the transmission opportunities while others starve.

Hybrid Centralized–Distributed Resource Allocation for Device-to-Device Communication Underlaying Cellular Networks

Abstract: The basic idea of device-to-device (D2D) communication is that pairs of suitably selected wireless devices reuse the cellular spectrum to establish direct communication links, provided that the adverse effects of D2D communication on cellular users are minimized and that cellular users are given higher priority in using limited wireless resources. Despite its great potential in terms of coverage and capacity performance, implementing this new concept poses some challenges, particularly with respect to radio resource management. The main challenges arise from a strong need for distributed D2D solutions that operate in the absence of precise channel and network knowledge. To address this challenge, this paper studies a resource allocation problem in a single-cell wireless network with multiple D2D users sharing the available radio frequency channels with cellular users. We consider a realistic scenario where the base station (BS) is provided with strictly limited channel knowledge, whereas D2D and cellular users have no information. We prove a lower bound for the cellular aggregate utility in the downlink with fixed BS power, which allows for decoupling the channel allocation and D2D power control problems. An efficient graph-theoretical approach is proposed to perform channel allocation, which offers flexibility with respect to allocation criteria (aggregate utility maximization, fairness, and quality-of-service (QoS) guarantee). We model the power control problem as a multiagent learning game. We show that the game is an exact potential game with noisy rewards, which is defined on a discrete strategy set, and characterize the set of Nash equilibria. Q-learning better-reply dynamics is then used to achieve equilibrium.

Channel Assignment Techniques for Multi-Radio Wireless Mesh Networks: A Survey

Abstract: With the advent of multiple radio interfaces on a single device, wireless mesh networks start to achieve significant improvement in network capacity, latency, and fault tolerance. The improvement is achieved through concurrent transmissions over different channels utilizing the multiple radio interfaces. However, the introduction of different channels over multiple radios on single mesh node compels to retrospect different issues such as interference, channel diversity, and channel switching from novel perspectives. Due to these novel perspectives, conventional channel assignment techniques proposed for single-radio wireless mesh networks are not generally applicable to the multi-radio cases. Consequently, we have to reconsider the different issues while making a tradeoff among all the available channel assignment options to extract the best performance from a multi-radio wireless mesh network. There are a number of research studies that propose various channel assignment techniques to extract the best performance. In this paper, we present a comprehensive survey on these studies. First, we point out various design issues pertinent to the techniques presented in the studies, and adopt the issues as the basis of our further discussion. Second, we briefly describe several important already-proposed channel assignment techniques. Third, we present a number of channel assignment metrics that are exploited by the already-proposed techniques. Then, depending on the considerations in these techniques, we categorize the techniques and present an exhaustive comparison among them. Nevertheless, we point out a number of real deployments and applications of these techniques in real scenarios. Finally, we identify several open issues for future research with their current status in the literature.

Joint Power and Bandwidth Allocation in Wireless Cooperative Localization Networks

Abstract: Cooperative localization can enhance the accuracy of wireless network localization by incorporating range information among agent nodes in addition to those between agents and anchors. In this paper, we investigate the optimal allocation of the restricted resources, namely, power and bandwidth, to different nodes. We formulate the optimization problems for both synchronous networks and asynchronous networks, where one way and round trip measurements are applied for range estimation, respectively. Since the optimization problems are nonconvex, we develop an iterative linearization-based technique, and show by comparison with brute-force search that it provides near-optimal performance in the investigated cases. We also show that especially in the case of inefficient anchor placement and/or severe shadowing, cooperation among agents is important and more resources should be allocated to the agents correspondingly.

Interference-Aware Cooperative Communication in Multi-Radio Multi-Channel Wireless Networks

Abstract: There are a lot of recent interests on cooperative communication (CC) in wireless networks. Despite the large capacity gain of CC in small wireless networks with its capability of mitigating fading taking advantage of spatial diversity, cooperative communication can result in severe interference in large networks and even degraded throughput. The aim of this work is to concurrently exploit multi-radio and multi-channel (MRMC) technique and cooperative transmission technique to combat co-channel interference and improve the performance of multi-hop wireless network. Our proposed solution concurrently considers cooperative routing, channel assignment, and relay selection and takes advantage of both MRMC technique and spatial diversity in cooperative wireless networks to improve the throughput. We propose two important metrics, contention-aware channel utilization routing metric (CACU) to capture the interference cost from both direct transmission and cooperative transmission, and traffic aware channel condition metric (TACC) to evaluate the channel load condition. Based on these metrics, we propose three algorithms for interference-aware cooperative routing, local channel adjustment, and local path and relay adaptation respectively to ensure high performance communications in dynamic wireless networks. Our algorithms are designed to be fully distributed and can effectively mitigate co-channel interference and achieve cooperative diversity gain. To our best knowledge, this is the first distributed solution that supports cooperative communications in MRMC networks. Our performance studies demonstrate that our proposed algorithms can efficiently support cooperative communications in multi-radio multi-hop networks to significantly increase the aggregate throughput.

Radio Resource Allocation for Device-to-Device Underlay Communication Using Hypergraph Theory

Abstract: Device-to-device (D2D) communication has been recognized as a promising technique to offload the traffic for the evolved Node B (eNB). However, D2D transmission as an underlay causes severe interference to both the cellular and other D2D links, which imposes a great technical challenge to radio resource allocation. Conventional graph based resource allocation methods typically consider the interference between two user equipments (UEs), but they cannot model the interference from multiple UEs to completely characterize the interference. In this paper, we study channel allocation using hypergraph theory to coordinate the interference between D2D pairs and cellular UEs, where an arbitrary number of D2D pairs are allowed to share the uplink channels with the cellular UEs. Hypergraph coloring is used to model the cumulative interference from multiple D2D pairs, and thus, eliminate the mutual interference. Simulation results show that the system capacity is significantly improved using the proposed hypergraph method in comparison to the conventional graph based one.

Device-to-Device Communications in Unlicensed Spectrum: Mode Selection and Resource Allocation

Abstract: In this paper, a novel technology, namely, device-to-device communications in the unlicensed spectrum (D2D-U) is proposed, which can allow D2D users to transmit on the unlicensed spectrum and coexist with the incumbent Wi-Fi networks. In D2D-U networks, D2D users can share the licensed spectrum with the existing cellular users or share the unlicensed spectrum with legacy Wi-Fi networks. Therefore, mutual interference across different networks and different users should be properly coordinated to optimize the system performance. In this paper, within the framework of D2D-U, we propose a joint mode selection and resource allocation algorithm to minimize the overall interference that cellular and Wi-Fi users suffer from the D2D communications while guaranteeing the signal-to-noise-and-interference ratio requirements of all users, including those of cellular, D2D, and Wi-Fi. Through theoretical analysis and numerical simulation, we show that using unlicensed spectrum can significantly mitigate the interference to both cellular and Wi-Fi users. Moreover, the duty cycle-based unlicensed spectrum access method achieves better system throughput than the listen-before-talk-based access method in most of the cases.

You Can Jam But You Cannot Hide: Defending Against Jamming Attacks for Geo-Location Database Driven Spectrum Sharing

Abstract: The emerging paradigm for dynamic spectrum sharing is based on allowing secondary users (SUs) to exploit white space frequency that is not occupied by primary users. White space database provides an opportunity for SUs to obtain spectrum availability information by submitting a location-based query. However, this new paradigm can also be exploited by the attackers to significantly enhance their jamming capability due to the available channel information from spectrum queries, which is expected to increasingly block SUs. The challenge is that the unique characteristics (e.g., lack of the wide range frequencies or continuous broadband) make existing anti-jamming techniques (e.g., direct-sequence spread spectrum and frequency hopping spread spectrum) difficult to be applied. In this paper, we present a novel Jammer Inference-based Jamming Defense (jDefender) framework. The main idea of jDefender is inferring the likelihood of a user being a jammer based on the observed jamming events and then utilizing the inferred attack likelihood to enhance the effectiveness of a series of the proposed anti-jamming strategies. Specifically, we first propose the Channel Allocation-based Jammer Inference scheme to infer the likelihood of an SU being a jammer based on the channels occupied by SUs even under the collusion attack performed by multiple jammers. The strength of the anti-jamming strategies (e.g., puzzle difficulties, available spectrum resources) will be correlated with the possibility of an SU being a jammer to achieve the tradeoff between system performance and jamming tolerance. We then implement the proposed scheme on Universal Software Radio Peripheral and PC. Extensive evaluations are performed to validate the effectiveness of the attacks and countermeasures.

Resource Allocation for Video Streaming in Heterogeneous Cognitive Vehicular Networks

Abstract: In future fifth-generation (5G) communication systems, driven by the evolution of today's most demanding applications, video streaming over vehicle networks will play an increasingly significant role in our daily lives. In this paper, based on a heterogeneous architecture consisting of both cellular base stations (BSs) of wide coverage and cognitive-radio-enabled roadside infrastructures, a semi-Markov decision process (SMDP)-based resource-allocation scheme is proposed to facilitate video streaming application in terms of peak signal-to-noise ratio (PSNR) and smooth playback. In addition to improving video quality of vehicle users, quality of service (QoS) provisioning for background users that can originally exist in the cellular network is also considered in the proposed scheme. Specifically, based on the states of the background users, vehicle users, and the availability of the cognitive bands, the optimal resource allocation, aiming at maximizing the video streaming quality while guaranteeing the call-level performance of the background users, is achieved by addressing two interrelated joint call admission control (CAC) and channel allocation problems for cellular and roadside infrastructure networks, respectively. Simulation results show that the proposed scheme can well protect background users when coexisting with bandwidth-hungry vehicle video users and is effective in not only improving video streaming quality for vehicle users but boosting bandwidth utilization of the whole network as well.

Methods and apparatuses for use of simultaneous multiple channels in the dynamic frequency selection band in wireless networks

Abstract: The present invention relates to wireless networks and more specifically to systems and methods for selecting available channels free of radar signals from a plurality of 5 GHz radio frequency channels. In non-limiting embodiments, exemplary systems, methods, and apparatuses are provided that can facilitate reducing false detections and/or network downtime in exemplary mesh networks employing dynamic frequency selection (DFS) channels. In a non-limiting aspect, radar information can be propagated among exemplary mesh nodes, including location information, to facilitate reducing false detections and/or network downtime in exemplary mesh networks. In addition, in further non-limiting aspects, exemplary embodiments can transmit signals to facilitate silencing one or more DFS channels and/or collaborative mesh node identification and/or discrimination of radar signals and false detections, among other non-limiting aspects provided.

Cognitive radio spectrum allocation using genetic algorithm

Abstract: This paper presents the problem formulation, development, and use of a robust dynamic genetic algorithm (GA) for channel allocation in cognitive radio. This approach offers an efficient way to access available spectrum for both primary and secondary users. The proposed dynamic genetic algorithms based on the new sophisticated crossover and mutation operators ensure the validity of channels and the fast convergence to the best solution in a highly dynamic environment. Compared with existing methods, simulation results demonstrate that our approach algorithm produces satisfactory results with reduced network interference and enhance efficiently the spectrum throughput.

Energy-Efficient User Association and Resource Allocation for Multistream Carrier Aggregation

Abstract: Multistream carrier aggregation (MSCA) is a promising technique to boost data rates by enabling users to connect with multiple base stations (BSs) simultaneously. In this paper, we investigate joint user association and resource allocation for MSCA systems to achieve energy efficiency (EE) balance among different BSs. We aim at maximizing the weighted summation of EE values for different BSs, which is modeled as a nonconvex combinatorial sum-of-ratios optimization problem. To develop an upper bound algorithm, we first relax the combinatorial variables and then transform the problem into a series of convex optimization problems by the successive convex approximation (SCA) method. We also propose a low-complexity suboptimal algorithm, which divides the original optimization problem into two steps: user association and channel allocation, as well as power allocation. Numerical results show that the proposed algorithms can achieve flexible EE tradeoff among BSs, and the EE can be significantly improved with MSCA.

Joint Beamforming, Power, and Channel Allocation in Multiuser and Multichannel Underlay MISO Cognitive Radio Networks

Abstract: In this paper, we consider joint beamforming, power, and channel allocation in a multiuser and multichannel underlay multiple-input–single-output (MISO) cognitive radio network (CRN). In this system, the primary users' spectrum can be reused by secondary-user transmitters (SU-TXs) to maximize spectrum utilization, whereas intrauser interference is minimized by implementing beamforming at each SU-TX. After formulating the joint optimization problem as a nonconvex mixed-integer nonlinear programming problem, we propose a solution that consists of two stages. In the first stage, a feasible solution for power allocation and beamforming vectors is derived under a given channel allocation by converting the original problem into a convex form with an introduced optimal auxiliary variable and a semidefinite relaxation approach. In the second stage, two explicit searching algorithms, i.e., genetic algorithm (GA) and simulated annealing (SA)-based algorithm, are proposed to determine suboptimal channel allocations. Simulation results show that the beamforming and power and channel allocation with SA algorithm can achieve a close-to-optimal sum rate while having lower computational complexity compared with the beamforming and power and channel allocation with the GA algorithm. Furthermore, our proposed allocation scheme has significant improvement in achievable sum rate compared with the existing zero-forcing beamforming.

Optimal QoS-Aware Channel Assignment in D2D Communications With Partial CSI

Abstract: In this paper, we propose effective channel assignment algorithms for network utility maximization in a cellular network with underlaying device-to-device (D2D) communications. A major innovation is the consideration of partial channel state information (CSI), i.e., the base station (BS) is assumed to be able to acquire “partial” instantaneous CSI of the cellular and D2D links, as well as, the interference links. In contrast to the existing works, multiple D2D links are allowed to share the same channel, and the quality of service (QoS) requirements for both the cellular and D2D links are enforced. We first develop an optimal channel assignment algorithm based on dynamic programming, which enjoys a much lower complexity compared with exhaustive search and will serve as a performance benchmark. To further reduce complexity, we propose a cluster-based sub-optimal channel assignment algorithm. New closed-form expressions for the expected weighted sum rate and the successful transmission probabilities are also derived. Simulation results verify the effectiveness of the proposed algorithms. Moreover, by comparing different partial CSI scenarios, we observe that the CSI of the D2D communication links and the interference links from the D2D transmitters to the BS significantly affects the network performance, while the CSI of the interference links from the BS to the D2D receivers only has a negligible impact.

Bandwidth allocation scheme based on simple statistical traffic analysis for TDM-PON based mobile fronthaul

Abstract: We propose a bandwidth allocation scheme utilizing simple statistical traffic analysis for TDM-PON based mobile fronthaul. Experiments show that excess bandwidth allocation can be reduced while achieving latency under 50 μs.

BIGAP — Seamless handover in high performance enterprise IEEE 802.11 networks

Abstract: Enterprise IEEE 802.11 networks need to provide high network performance to operate a large number of diverse clients like laptops, smartphones and tablets as well as capacity hungry and delay sensitive novel applications like mobile HD video & cloud storage efficiently. Moreover, such devices and applications require much better mobility support and higher QoS/QoE. Existing solutions can either provide high network performance or seamless mobility but not both. We present BIGAP, a novel architecture achieving both of the above goals. The former is achieved by assigning different channels to co-located APs in order to fully utilize the available radio spectrum. The latter is achieved by providing a mechanism for below MAC-layer handover through exploiting the Dynamic Frequency Selection capability in 802.11. In essence BIGAP forces clients to change AP whilst they ‘believe’ they are simply changing channel. BIGAP is fully compatible with 802.11 and requires no modifications to the wireless clients. Testbed results demonstrate a significant improvement in terms of network outage duration (which is 32 x smaller as compared to state-of-the-art solutions) and negligible throughput degradation during handover operation. In this way frequent and seamless handover operations can take place thus supporting both seamless mobility and efficient load balancing.

Energy Management in Heterogeneous Networks With Cell Activation, User Association, and Interference Coordination

Abstract: The densification and expansion of wireless network pose new challenges on interference management and reducing energy consumption. This paper studies energy-efficient resource management in heterogeneous networks by jointly optimizing cell activation, user association and multicell multiuser channel assignment, according to the long-term average traffic and channel conditions. The proposed framework is built on characterizing the interference coupling by predefined interference patterns, and performing resource allocation among these patterns. In this way, the interference fluctuation caused by (de)activating cells is explicitly taken into account when calculating the user achievable rates. A tailored algorithm is developed to solve the formulated problem in the dual domain by exploiting the problem structure, which gives a significant complexity saving. Numerical results show a huge improvement in energy saving achieved by the proposed scheme. The user association derived from the proposed joint resource optimization is mapped to standard-compliant cell selection biasing. This mapping reveals that the cell-specific biasing for energy saving is quite different from that for load balancing investigated in the literature.

Multichannel Analysis of Cell Range Expansion and Resource Partitioning in Two-Tier Heterogeneous Cellular Networks

Abstract: Cellular heterogeneous networks (HetNets) can improve capacity by offloading users from congested macro cells to lightly loaded small cells through biased association known as cell range expansion (CRE). However, the offloaded (range-expanded) users must be protected from macro interference through time/frequency resource partitioning. In this paper, we develop an analytical framework to evaluate the performance gain due to CRE further supported by resource partitioning in two-tier (macro-pico) networks with multichannel downlinks, e.g., those based on orthogonal frequency division multiple access (OFDMA). By exploiting the flexibility in subchannel allocation offered by OFDMA, frequency-domain resource partitioning is proposed in which the macro tier is muted on a fraction of total subchannels, which are allocated exclusively to range-expanded pico users. The load perceived by a base-station is a key factor in determining its interference contribution over the network and is directly affected by user offloading and resource partitioning. Thus, the analysis of such systems must incorporate cell load. While previous studies mostly rely on full-load assumption, in this paper, we properly characterize cell load as the function of user density, association bias and resource partitioning fraction. We then, evaluate the performance in terms of average user data rate over the entire network, and also investigate the optimal choice of association bias and resource partitioning fraction.

DAPA: Capacity Optimization in Wireless Networks Through a Combined Design of Density of Access Points and Partially Overlapped Channel Allocation

Abstract: Dense wireless local area networks (WLANs) have emerged as a promising design paradigm recently. While partially overlapped channels (POCs) have proved to be able to improve network capacity significantly in the dense network, they are usually considered the fixed number of access points (APs) in traditional research works. The network capacity can be scaled by providing additional APs in a given area since the expected distance from users to the associated APs becomes shorter. On the other hand, the additional increment of network capacity by means of deploying more APs is limited. It can be accredited to the substantial interference among the high number of deployed APs assigned with POCs. Furthermore, the impact of the POC model makes the design more complex. To cope with these challenges in this work, we study the problem of interaction between density of APs and POC assignment with parameter tuning and propose the approach called DAPA, in which we derive that the optimal density of APs lies in the feasible region consisting of the lower bound and upper bound density. Thus, the solution can be obtained by searching for the feasible region by means of the proposed POC assignment. Through analysis and numerical results in DAPA, we provide recommendations on the optimal dimension of high density and POC assignment, with consideration of the network configuration.

Learning Temporal–Spatial Spectrum Reuse

Abstract: We formulate and study a multi-user multi-armed bandit problem that exploits the temporal–spatial opportunistic spectrum access (OSA) of primary user (PU) channels, so that secondary users (SUs) who do not interfere with each other can make use of the same PU channel. We first propose a centralized channel allocation policy that has logarithmic regret, but requires a central processor to solve an NP-complete optimization problem at exponentially increasing time intervals. To overcome the high computation complexity at the central processor, we also propose heuristic distributed policies that, however, have linear regrets. Our first distributed policy utilizes a distributed graph coloring and consensus algorithm to determine SUs’ channel access ranks, while our second distributed policy incorporates channel access rank learning in a local procedure at each SU at the cost of a higher regret. We compare the performance of our proposed policies with other distributed policies recently proposed for temporal (but not spatial) OSA. We show that all these policies have linear regrets in our temporal–spatial OSA framework. Simulations suggest that our proposed policies have significantly smaller regrets than the other policies when spectrum temporal–spatial reuse is allowed.

Resource Allocation for D2D Links in the FFR and SFR Aided Cellular Downlink

Abstract: Device-to-device (D2D) communication underlying cellular networks, allows direct transmission between two devices in each other’s proximity that reuse the cellular resource blocks in an effort to increase the network capacity and spectrum efficiency. However, this imposes severe interference that degrades the system’s performance. This problem may be circumvented by incorporating fractional frequency reuse (FFR) or soft frequency reuse (SFR) in OFDMA cellular networks. By carefully considering the downlink resource reuse of the D2D links, we propose beneficial frequency allocation schemes, when the macrocell has employed FFR or SFR as its frequency reuse technique. The performance of these schemes is quantified using both the analytical and simulation results for characterizing both the coverage probability and the capacity of D2D links under the proposed schemes that are benchmarked against the radical unity frequency reuse scheme. The impact of the D2D links on the coverage probability of macrocellular users (CUs) is also quantified, revealing that the CUs performance is only modestly affected under the proposed frequency allocation schemes. Finally, we provide insights concerning the power control design in order to strike a beneficial tradeoff between the energy consumption and the performance of D2D links.