Multi-channel Wireless Networks Research Articles

Interplay between Power Control and Channel Allocation for Multi-Channel Wireless Networks

In wireless networks, interference from adjacent nodes that are concurrently transmitting can cause packet reception failures and thus a significant throughput degradation. The interference can be simply avoided by assigning different orthogonal channels to each interfering node. However, if the number of orthogonal channels is smaller than that of interfering n odes, some adjacent nodes have to share the same channel and may interfere with each other. This interference can be mitigated by reducing the transmit power of the interfering nodes. In this paper, we propose to jointly coordinate the transmit power and the multi-channel allocation to maximize the network throughput performance by fully exploiting multi-channel availability. This coordination enables ea ch node to use high transmission power as long as different orthogonal channels can be assigned to its adja cent nodes. Then, we propose a simple multichannel media access control (MAC) protocol that allows the nodes on different channels to perform efficient data exchanges without interference in multi-cha nnel networks. We show that the proposed scheme improves the network throughput performance in comparison with other existing schemes.

Low-Complexity Scheduling Policies for Achieving Throughput and Asymptotic Delay Optimality in Multichannel Wireless Networks

In this paper, we study the scheduling problem for downlink transmission in a multichannel (e.g., OFDM-based) wireless network. We focus on a single cell, with the aim of developing a unifying framework for designing low-complexity scheduling policies that can provide optimal performance in terms of both throughput and delay. We develop new easy-to-verify sufficient conditions for rate-function delay optimality (in the many-channel many-user asymptotic regime) and throughput optimality (in general nonasymptotic setting), respectively. The sufficient conditions allow us to prove rate-function delay optimality for a class of Oldest Packets First (OPF) policies and throughput optimality for a large class of Maximum Weight in the Fluid limit (MWF) policies, respectively. By exploiting the special features of our carefully chosen sufficient conditions and intelligently combining policies from the classes of OPF and MWF policies, we design hybrid policies that are both rate-function delay-optimal and throughput-optimal with a complexity of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sup> log n), where n is the number of channels or users. Our sufficient condition is also used to show that a previously proposed policy called Delay Weighted Matching (DWM) is rate-function delay-optimal. However, DWM incurs a high complexity of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> ). Thus, our approach yields significantly lower complexity than the only previously designed delay and throughput-optimal scheduling policy. We also conduct numerical experiments to validate our theoretical results.

Sequential Learning for Multi-Channel Wireless Network Monitoring With Channel Switching Costs

We consider the problem of optimally assigning $p$ sniffers to $K$ channels to monitor the transmission activities in a multichannel wireless network with switching costs. The activity of users is initially unknown to the sniffers and is to be learned along with channel assignment decisions to maximize the benefits of this assignment, resulting in the fundamental tradeoff between exploration and exploitation. Switching costs are incurred when sniffers change their channel assignments. As a result, frequent changes are undesirable. We formulate the sniffer-channel assignment with switching costs as a linear partial monitoring problem, a superclass of multiarmed bandits. As the number of arms (sniffer-channel assignments) is exponential, novel techniques are called for, to allow efficient learning. We use the linear bandit model to capture the dependency amongst the arms and develop a policy that takes advantage of this dependency. We prove that the proposed Upper Confident Bound-based (UCB) policy enjoys a logarithmic regret bound in time $t$ that depends sublinearly on the number of arms, while its total switching cost grows in the order of $O(\log\log(t))$ .

Bounded information dissemination in multi-channel wireless networks

More and more wireless networks and devices now operate on multiple channels, which poses the question: How to use multiple channels to speed up communication? In this paper, we answer this question for the case of wireless ad-hoc networks where information dissemination is a primitive operation. Specifically, we propose a randomized distributed algorithm for information dissemination that is very near the optimal. The general information dissemination problem is to deliver $$k$$k information packets, stored initially in $$k$$k different nodes (the packet holders), to all the nodes in the network, and the objective is to minimize the time needed. With an eye toward the reality, we assume a model where the packet holders are determined by an adversary, and neither the number $$k$$k nor the identities of packet holders are known to the nodes in advance. Not knowing the value of $$k$$k sets this problem apart from broadcasting and all-to-all communication (gossiping). We study the information dissemination problem in single-hop networks with bounded-size messages. We present a randomized algorithm which can disseminate all packets in $$O(k(\frac{1}{\mathcal {F}}+\frac{1}{\mathcal {P}})+\log ^2n)$$O(k(1F+1P)+log2n) rounds with high probability, where $$\mathcal {F}$$F is the number of available channels and $$\mathcal {P}$$P is the bound on the number of packets a message can carry. Compared with the lower bound $$\varOmega (k(\frac{1}{\mathcal {F}}+\frac{1}{\mathcal {P}}))$$Ω(k(1F+1P)), the given algorithm is very close to the asymptotical optimal except for an additive factor. Our result provides the first solid evidence that multiple channels can indeed substantially speed up information dissemination, which also breaks the $$\varOmega (k)$$Ω(k) lower bound that holds for single-channel networks (even if $$\mathcal {P}$$P is infinity).

Effective Cluster based Routing Algorithm for WMNS using Effective Data Aggregation

Wireless Mesh Network (WMN) is a key technology, supporting a variety of several emerging and commercially interesting applications. The multi-hop nature of WMNs and the rapid growth of throughput demands lead to multichannels and multi-radios structures in mesh networks, but the interference of co-channels, as a main problem reduces the total throughput, especially in multi-hop networks. QoS (Quality of Service) refers to a vast collection of networking technologies and techniques that guarantees the ability of a network to provide with predictable results. Due to interference among various transmissions, the QoS routing in multi-hop wireless networks is formidable task. In case of multi-channel wireless network, since two transmissions using the same channel may get in with each other so there exists interference. The request of QoS connection usually accompanies bandwidth requirement and QoS routing seeks a source to destination route with requested bandwidth. The main objectives of QoS based routing are optimal utilization of resources for improving total network throughput and graceful performance degradation during overloading conditions offering better throughput, Dynamic determination of feasible paths for accommodating the QoS of the given flow under various policy constraints such as provider selection, path cost etc. The overall objective of this paper is to propose a new multi-radio, multi-channel and clustering based wireless mesh protocol to improve the QoS further. This paper has considered the DSDV protocol to locate the secure and optimized path. The proposed technique also utilizes the LZW based lossless data compression and intra cluster data aggregation to enhance the communication between the source and the destination. The use of clustering has the ability to aggregates the multiple packets and locate a single route using the clusters to improves the intra cluster data aggregation. The use of the LZW based loss less data compression has ability to reduce the data packet size so will consume lesser energy thus increase the network QoS. The MATLAB tool has been used to evaluate the effectiveness of the proposed technique. the comparative analysis has shown that the proposed technique outperforms over the available techniques.

Spectrum-Efficient Multi-Channel Design for Coexisting IEEE 802.15.4 Networks: A Stochastic Geometry Approach

For networks with random topologies (e.g., wireless ad-hoc and sensor networks) and dynamically varying channel gains, choosing the long term operating parameters that optimize the network performance metrics is very challenging. In this paper, we use stochastic geometry analysis to develop a novel framework to design spectrum-efficient multi-channel random wireless networks based on the IEEE 802.15.4 standard. The proposed framework maximizes both spatial and time domain frequency utilization under channel gain uncertainties to minimize the number of frequency channels required to accommodate a certain population of coexisting IEEE 802.15.4 networks. The performance metrics are the outage probability and the self admission failure probability. We relax the single channel assumption that has been used traditionally in the stochastic geometry analysis. We show that the intensity of the admitted networks does not increase linearly with the number of channels and the rate of increase of the intensity of the admitted networks decreases with the number of channels. By using graph theory, we obtain the minimum required number of channels to accommodate a certain intensity of coexisting networks under a self admission failure probability constraint. To this end, we design a superframe structure for the coexisting IEEE 802.15.4 networks and a method for time-domain interference alignment.

Communication and computing technology in biocontainment laboratories using the NEIDL as a model

The National Emerging Infectious Diseases Laboratories (NEIDL), Boston University, is a globally unique biocontainment research facility housing biosafety level 2 (BSL-2), BSL-3, and BSL-4 laboratories. Located in the BioSquare area at the University's Medical Campus, it is part of a national network of secure facilities constructed to study infectious diseases of major public health concern. The NEIDL allows for basic, translational, and clinical phases of research to be carried out in a single facility with the overall goal of accelerating understanding, treatment, and prevention of infectious diseases. The NEIDL will also act as a center of excellence providing training and education in all aspects of biocontainment research. Within every detail of NEIDL operations is a primary emphasis on safety and security. The ultramodern NEIDL has required a new approach to communications technology solutions in order to ensure safety and security and meet the needs of investigators working in this complex building. This article discusses the implementation of secure wireless networks and private cloud computing to promote operational efficiency, biosecurity, and biosafety with additional energy-saving advantages. The utilization of a dedicated data center, virtualized servers, virtualized desktop integration, multichannel secure wireless networks, and a NEIDL-dedicated Voice over Internet Protocol (VoIP) network are all discussed.

Max-Min Lifetime Optimization for Cooperative Communications in Multi-Channel Wireless Networks

Cooperative communication (CC) has been proposed for achieving spatial diversity without requiring multiple antennas on the same node. Many efforts in exploiting the benefits of CC focus on improving the performance in terms of outage probability or channel capacity. However, the energy efficiency of CC, which is critical for the applications with energy constraints, has been little studied. In this paper, we study the lifetime maximization problem for multiple source-destination pairs using CC in multi-channel wireless networks by an optimal dynamic allocation of resources in terms of power, channel, cooperative relay, and transmission time fraction. We prove it NP-hard and formulate it as a mixed-integer nonlinear programming (MINLP) problem, which is then transformed into a mixed-integer linear programming (MILP) problem using linearization and reformulation techniques. By exploiting several problem-specific characteristics, a time-efficient branch-and-bound algorithm is proposed to solve the MILP problem. Extensive simulations are conducted to show that the proposed algorithm can significantly improve the performance of energy efficiency over existing solutions.

Optimal channel assignment with aggregation in multi-channel systems: A resilient approach to adjacent-channel interference

Channel assignment mechanisms in multi-channel wireless networks are often designed without accounting for adjacent-channel interference (ACI). To prevent such interference between different users in a network, guard-bands (GBs) are needed. Introducing GBs has significant impact on spectrum efficiency. In this paper, we present channel assignment mechanisms that aim at maximizing the spectrum efficiency. More specifically, these mechanisms attempt to minimize the amount of additional GB-related spectrum that is needed to accommodate a new link. Similar to the IEEE 802.11n and the upcoming IEEE 802.11ac standards, our assignment mechanisms support channel bonding, and more generally, channel aggregation. We first consider sequential assignment (i.e., one link at a time), and we formulate the optimal ACI-aware channel assignment that maximizes the spectrum efficiency as a subset-sum problem. An exact exponential-time dynamic programming (DP) algorithm, a polynomial-time greedy heuristic, and an ∊-approximation are presented and compared. Second, considering a set of links (batch assignment), we derive the optimal ACI-aware exponential-time assignment that maximizes the network’s spectrum efficiency. The optimal batch assignment is compared with the sequential assignment. Results reveal that our proposed algorithms achieve considerable improvement in spectrum efficiency compared to previously proposed schemes.

On Quality of Monitoring for Multichannel Wireless Infrastructure Networks

Passive monitoring utilizing distributed wireless sniffers is an effective technique to monitor activities in wireless infrastructure networks for fault diagnosis, resource management, and critical path analysis. In this paper, we introduce a quality of monitoring (QoM) metric defined by the expected number of active users monitored, and investigate the problem of maximizing QoM by judiciously assigning sniffers to channels based on the knowledge of user activities in a multichannel wireless network. Two types of capture models are considered. The user-centric model assumes the frame-level capturing capability of sniffers such that the activities of different users can be distinguished while the sniffer-centric model only utilizes the binary channel information (active or not) at a sniffer. For the user-centric model, we show that the implied optimization problem is NP-hard, but a constant approximation ratio can be attained via polynomial complexity algorithms. For the sniffer-centric model, we devise stochastic inference schemes to transform the problem into the user-centric domain, where we are able to apply our polynomial approximation algorithms. The effectiveness of our proposed schemes and algorithms is further evaluated using both synthetic data as well as real-world traces from an operational WLAN.

Self-tuned distributed monitoring of multi-channel wireless networks using Gibbs sampler

Wireless side monitoring employing distributed sniffers has been shown to complement wired side monitoring using Simple Network Management Protocol (SNMP) and base station logs, since it reveals detailed PHY and MAC behaviors, as well as timing information. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Distributed algorithms are desirable to determine the optimal channel allocation of sniffer nodes to maximize the information collected. In this paper, we propose Gibbs sampler based algorithms for robust distributed monitoring of multi-channel wireless networks. Among several variants of the base Gibbs sampling approach, we find that most algorithms suffer from high sensitivity to parameter selection. In contrast, Gibbs sampling using a thermodynamic schedule is self-tuned and can adapt to different network configurations. Simulation studies show that the proposed algorithms can achieve faster convergence rate and have higher chance of reaching global optima than traditional Gibbs sampler algorithm.

Feasible routing selection mechanism in multichannel wireless networks

Considering more practical scenarios in the designing of joint routing, channel-assignment and scheduling algorithms in multichannel wireless networks, a modified routing selection scheme based on a performance guaranteed MP mechanism is put forward. The assumption made in MP, that the balance factor chosen for each link αl = α for all links l is not reasonable, is pointed out and may influence the performance of MP badly in the case of channels with large diversity. Focusing on this point, a balance factor used for channel assignment in MP is taken into account in the stage of route selection and the limitation of original MP is relaxed. As a result, the proposed joint algorithm is able to better adapt to the condition of channels with large diversity. It is proved that the modified policy for MP is able to achieve the same capacity region as that of original MP.

Approximate Online Learning Algorithms for Optimal Monitoring in Multi-Channel Wireless Networks

We consider the problem of optimally selecting m out of M sniffers and assigning each sniffer one of the K channels to monitor the transmission activities in a multi-channel wireless network. The activity of users is initially unknown to the sniffers and is to be learned along with channel assignment decisions. Even with the full knowledge of user activity statistics, the offline optimization problem is known to be NP-hard. In this paper, we first propose a centralized online approximation algorithm and show that it incurs sub-linear regret bounds over time. A distributed algorithm is then proposed with moderate message complexity. We demonstrate both analytically and empirically the trade-offs between the computation cost and the rate of learning.

Scheduling in Multi-Channel Wireless Networks: Rate Function Optimality in the Small-Buffer Regime

The problem of designing scheduling algorithms for a multichannel (e.g., orthogonal frequency division multiplexing-based) wireless downlink network is considered. The classic MaxWeight algorithm, although throughput-optimal, results in a very poor per-user delay performance in such systems. Hence, an alternate class of algorithms called iterated longest queues first (iLQF) is proposed for overcoming this issue. The iLQF-class algorithms are analyzed in a number of different system configurations. A particular algorithm in this class, called iLQF with pullup, is shown to be rate function optimal for the problem in an appropriate large deviations setting, and is shown to result in a strictly positive value of the rate function for a number of modifications to the basic system model. Thus, the proposed algorithm yields provable performance guarantees. The analytic results are confirmed through simulations.

Energy-Efficient Cooperative Communications for Multimedia Applications in Multi-Channel Wireless Networks

The dramatic growth of mobile multimedia communications imposes new requirements on quality-of-service and energy efficiency in wireless networks. In this paper, we study the energy- and spectrum-efficient cooperative communication (ESCC) problem by exploiting the benefits of cooperative communication (CC) for mobile multimedia applications in multi-channel wireless networks.In a static network, it is formulated as a mixed-integer nonlinear programming problem. To solve this problem, we use linearizationand reformulation techniques to transform it into a mixed-integer linear programming problem that is solved by a branch-and-bound algorithm with enhanced performance. To deal with the problem in dynamic networks, we propose an online algorithm with lowcomputational complexity and deployment overhead. Extensive simulations are conducted to show that the proposed algorithmcan significantly improve the performance of energy efficiency in both static and dynamic networks.

An efficient traffic management framework for multi-channel wireless backbone networks

An efficient traffic management framework for multi-channel wireless backbone networks

Routing Metrics for Minimizing End-to-End Delay in Multiradio Multichannel Wireless Networks

This paper studies how to select a path with the minimum expected end-to-end delay (EED) in a multiradio multichannel (MR-MC) wireless mesh network. While the existing studies mainly focus on the packet transmission delay due to medium access control (MAC), our new EED metric further takes into account the queuing delay at the MAC layer. In particular, in the MR-MC context, we develop a generic iterative approach to compute the multiradio achievable bandwidth (MRAB) for a path, taking the impact of inter-/intraflow interference and space/channel diversity into consideration. The MRAB is then combined with the EED to form the metric weighted end-to-end delay (WEED). As a byproduct of MRAB, a channel diversity coefficient is defined to quantitatively represent the channel diversity for a given path. Moreover, we design and implement a distributed WEED-based routing protocol for MR-MC wireless networks by extending the well-known AODV protocol. Extensive simulation results are presented to demonstrate the performance of EED/WEED-based routing, with comparison to some existing well-known routing metrics.

Signal transmission model for the substations grounding grid

The signal of the wireless sensor network in grounding grid, owing to energy loss, network congestion, path constraints and other factors, is easy to delay even partially losing. In order to ensure that the signal can be transmitted effectively in grounding grids for the substation, this paper presents a method based on traffic model of back-off balanced multiple sensor network cooperation model. As we all know, cognitive radio (CR) technology is adopted in multi-channel wireless networks to provide enough channels for data transmission. The MAC protocols should enable the secondary users to maintain the accurate channel state information to identify and utilize the leftover frequency spectrum in a way that constrains the level of interference to the primary users. We proposed a novel cooperation spectrum sensing scheme in which the secondary users adopt backoff-based sensing policy based on the traffic model of the primary users to maximum the throughput of the network. To obtain the full accurate information of the spectrum is a difficult task so that we propose the backoff sensing as a sub-optimal strategy. Since the secondary users sense only a subset of the channels in our proposed scheme, less time is spent to get the channel state information as more time is saved for the data transmission. And while dealing the signal data, I combine the intensity transfer method instead of the priority method. This can effectively reduce the network congestion, to ensure that the main information can be transfer well. It is also very useful to signal transmission for the Multi-sensor in Substations Grounding Grid (SGG).

Channel assignment in heterogeneous multi-radio multi-channel wireless networks: A game theoretic approach

Channel assignment is a challenging issue for multi-radio multi-channel wireless networks, especially in a competing environment. This paper investigates channel assignment for selfish nodes in a heterogeneous scenario, in which nodes may have different QoS requirements and thus compete for different channels with unequal bandwidth. The interaction among nodes is formulated as a non-cooperative Multi-radio Channel Assignment Game (MCAG), where Nash Equilibrium (NE) corresponds to a stable channel assignment outcome from which no individual node has the incentive to deviate. The NEs in MCAG are characterized in this paper. Since multiple NEs may exist in this game, it is natural to choose the NE that maximizes the network utility, i.e., the sum of node utilities. It is shown that the optimal NE outcome can be derived by solving an integer non-linear programming problem. Based on some observations on the radio number distribution of NE, we propose a two-stage optimization algorithm to achieve an optimal channel assignment. Finally, computer simulations validate the effectiveness of the proposed algorithm.

A New Channel Conflict Model for Multi-Radio Multi-Channel Wireless Networks

Before the channel allocation of wireless networks, usually need to select the appropriate channel conflict model to model the channel collision. For the current channel conflict model cant model the channel collision comprehensively, the graph N-tuple coloring model based on graph N-tuple coloring theory is proposed in this article. The theoretical analysis and simulation results show that this model can model the channel collision comprehensively, and transform the channel allocation problem into N-tuple coloring problem on a graph for the further analysis and solve.