Channel Allocation

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Performance and Evaluation of Adaptive Dynamic Channel Allocation protocol in Hybrid Multichannel Wireless Mesh Networks

Performance and Evaluation of Adaptive Dynamic Channel Allocation protocol in Hybrid Multichannel Wireless Mesh Networks

Two dynamic interfaces able to negotiate a common channel when they are in transmission range of each other. Communication takes place when they have data to transmit. These links are called as dynamic links. Fig. 1 shows all possible dynamic links in dotted lines. Dotted line stresses that the only pair of nodes can communicate. TDMA is used here. Time is divided into fixed-length intervals. Each interval consists of control and data interval. During control interval, all nodes communicate using a default channel to negotiate the channels to be used in the data interval. Nodes transmit and receive data on the negotiated channels during the data interval. Care should be taken in the dynamic channel allocation to avoid interference from the static interfaces. In control interval, using default channel each dynamic interfaces negotiate. Interference from the static interfaces must be eliminated during the control interval. Negotiation default channel is not used in the channel allocation of the static interfaces. This will not affect the efficiency of channel usage, because dynamic interfaces can still use this default channel in the data interval.
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Adaptive Antenna Array Assisted Dynamic Channel Allocation Techniques

Adaptive Antenna Array Assisted Dynamic Channel Allocation Techniques

It can be seen from Table III that in an LOS environment all of the channel allocation schemes benefit from the use of base station AAAs in terms of an increased level of teletraffic car- ried, hence supporting an increased number of users. The FCA algorithm benefited most from the employment of AAAs with a 160% increase in terms of the number of users supported, when using a four-element antenna array. The performance improve- ments of the LOLIA with due to using AAAs were more modest than for the FCA system. Specifically, 44% more users were supported by the four-element AAA assisted LOLIA using , when compared to the single antenna element based re- sults. The network capacity of the LOLIA with a 19-cell exclu- sion zone was higher than that of the LOLIA using , until the limited number of channels available in conjunction with such a large exclusion zone became significant. Up to this point, the AAAs reduced the levels of interference, thus improving the network capacity. However, when using a four-element AAA, the new call blocking probability became the dominant network performance factor.
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Channel Allocation Scheme in Cellular System

Channel Allocation Scheme in Cellular System

---------------------------------------------------------------------ABSTRACT--------------------------------------------------------------- Channel allocation scheme proposed for cellular systems. We start with allocation a set of channels to each cell in fixed channel assignment scheme. The scheme used for handling the high density cell problem because it proposes to move unused channels from suitable low density cells to the high density ones through a channel borrowing algorithm. Detailed simulation experiments are carried out in order to evaluate our proposed methodology. Performance comparison gives that the D-LBSB scheme performs better than a C-LBSB version in an overloaded system.
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Using Quorum for Solving Channel Allocation Problem

Using Quorum for Solving Channel Allocation Problem

Over the years, several channel allocation algorithms have been presented by the researchers. But in this section, only dynamic channel allocation algorithms will be discussed which are generally fault tolerant in nature. The first fault-tolerant channel allocation algorithm was proposed by Prkash-Shivartri-Singhal [3] in 1999. Prakash- Shivratri- Singhal algorithm was able to handle the failure of MHs and MSSs. Additionally, the author assumed fail- stop failure of MSSs. Yang et al. [2] in 2005, proposed a fault tolerant algorithm to solve channel allocation problem. Their algorithm was able to work efficiently even if the network congestion, link failures and/or mobile service station failures exists,. Yang et al. [2] made the assumption that cellular network was divided in to cells and the reuse distance was considered to be r (the radius of a cell). In [2] algorithm, authors divided 6 cells in to 5 groups of varying size. The request for a channel can be granted if the requesting cell receives the reply from all members of a group. However, this algorithm does not work if the replies received by the requesting cell do not satisfy the criteria above mentioned. The algorithm is successful in case the area of coverage is divided in to hexagonal cells and the reuse distance is fixed which is r (radius of the cell). However, it is not practical to divide the area in to hexagonal cells in some cases, and the reuse distance may also significantly vary. Additionally, the messages required to be sent by the cell is equal to the number of all interfering neighbors even in case of fault free scenario. However, The message complexity is not significant when the number of neighbors is small. When the reuse distance is large and number of interfering neighbors significantly high, the message complexity of Yang et al.’s algorithm may severely affect the performance. Yang-Manivannan [4] proposed another fault tolerant algorithm which divides the set of cells in to k disjoints subsets such that the distance between any two cells in a subset is at least the reuse distance. Moreover, the channels are also divided in to k disjoint group PC 0 ,
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Goodput and channel allocation in opportunistic spectrum access networks

Goodput and channel allocation in opportunistic spectrum access networks

In context of allocation, the authors in [8] proposed co- operative and non cooperative channel allocation strategies based on color-sensitive graph coloring model. In [9], the authors proposed heuristic channel allocation algorithms based on multichannel contention graph and linear programming. In [10], authors derive the optimal access probabilities for fairness among two independent SUs in terms of throughput. The authors in [11] discussed the coexistence of dissimilar SUs in a cognitive radio network, using spectrum utilization as the benchmark. The techniques presented in [9] [10] [11] only discuss spectrum sharing without modelling the impact of SUs forced termination and blocking.
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Joint Beam Forming, Power and Channel Allocation in Multi-User and Multi-Channel Underlay MISO Cognitive Radio Networks

Joint Beam Forming, Power and Channel Allocation in Multi-User and Multi-Channel Underlay MISO Cognitive Radio Networks

ABSTRACT :In this paper, we consider a joint beam forming, power and channel allocation in a multi-user and multi- channel underlay multiple input single output (MISO) cognitive radio network (CRN). In this system, primary users (PU’s) spectrum can be reused by the secondary user transmitters(SUTXs) to maximize the spectrum utilization while the intra-user interference is minimized by implementing bean forming at each SU-TX. After formulating the joint optimization problem as a non-convex, mixed integer nonlinear programming(MINLP) problem, we propose a solution which consists of two stages. In the first stage, A feasible solution for power allocation and beam forming vectors is derived under a given channel allocation by converting the original problem into a convex form with an introduced optimal auxiliary variable and semi definite relaxation (SDR) approach. After that, In the second stage two explicit searching algorithms, i.e., genetic algorithm (GA) and simplified annealing (SA)-based algorithm, are proposed to determine suboptimal channel allocations. Simulation results show that the beam forming, power and channel allocation with SA (BPCA- SA) algorithm can achieve close-to-optimal sum-rate while having a lower computational complexity compared with beam forming, power and channel allocation with GA (BPCA_GA) algorithm.
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An Efficient Dynamic Channel Allocation Algorithm for Wi MAX Networks

An Efficient Dynamic Channel Allocation Algorithm for Wi MAX Networks

changed dynamically is important to examine the robustness of the method. The implementation of the proposed scheme operating in a parallel machine which would minimize call service time sufficiently or otherwise would maximize the effectiveness of the particular channel assignment algorithm is also very challenging. By viewing the above schemes as combinatorial problems, their complexity is simplified and the allocation process becomes an optimization task for which the proposed scheme is able to give adequate solutions which can easily be implemented. The proposed scheme was found to be the most effective channel allocation scheme.
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A Wind Driven Optimization Based WDM Channel Allocation Algorithm

A Wind Driven Optimization Based WDM Channel Allocation Algorithm

The phrase “Golomb ruler” [31] denotes a set of positive integer values, where any two numbers from the set consisting of different pairs do not have the same difference. Theoretically, this is identical to a ruler formed in a way where any two pairs of marks do not calculate the same distance. An illustration of the Golomb ruler is displayed in Figure 1. An Optimal Golomb Ruler depends on given number of marks and is the shortest ruler possible [32]. For allocating channels in WDM system, it is feasible to attain the least distinct number by applying Optimal Golomb Ruler to the channel allocation problem. The new FWM frequencies produced will not lie in the one previously allocated for the channels due to the fact that any two numbers taken as a pair do not have the same distance.
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Performability analysis of channel allocation with channel recovery strategy in cellular networks

Performability analysis of channel allocation with channel recovery strategy in cellular networks

With the rapid growth in the demand for personal communications services(PCS), ecient channel allocation plays a vital role in providing the pre-speci ed Quality of Service(QoS). Many channel allocation schemes have been proposed by several researchers 6, 10, 12, 16]. These schemes generally assume a perfect situation where the channel in usage never fails. In a practical environment, cellular networks, like any other physical system, are subject to failures. A channel may fail to operate properly due to various reasons such as power loss, software/hardware problems or hostile action. With the increasing demand for cellular communications, a disruption in service could cause severe consequences in both economic and social sense. Thus providing restoration subsequent to channel failures has become an important issue in ensuring network integrity.
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A Cuckoo Search based WDM Channel Allocation Algorithm

A Cuckoo Search based WDM Channel Allocation Algorithm

Fig 1: A 4–Marks Golomb Ruler having Ruler Length 6 A perfect Golomb ruler measures all the integer distances from 0 to L, where L is the length of the ruler [18], [24], [25]. An optimal Golomb ruler is the shortest length ruler for a given number of marks. There can be multiple different OGRs for a specific number of marks. However, the unique optimal Golomb 4–mark ruler is shown in Figure 1, which measures all the integer distances from 1 to 6 (and is therefore also a perfect ruler) [24], [40]–[44]. Therefore applying OGRs to the channel allocation problem, it is possible to achieve the smallest distinct number to be used for the channel allocation. As the difference between any two numbers is different, the
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Resource and Interference Minimization Channel Allocation in MCMR Networks

Resource and Interference Minimization Channel Allocation in MCMR Networks

There is a lot of work on channel allocation in the MCMR network. According to the channel allocation execution mode, it can be divided into the centralized channel allocation scheme and the distributed channel allocation scheme. It can also be divided into static allocation and dynamic allocation based on the distribution method. Static channel allocation refers to the channel at a certain period of time or permanently bound to a fixed interface. It will not bring about the switching delay caused by channel switching. Management of channel in static allocation is easy, interference is also relatively easy to control [1]. The algorithm in [2] is a centralized channel allocation scheme aiming at minimizing resources. An algorithm named CLICA [3] by setting priority order for nodes guarantees the best channel is selected to be assigned to nodes' links.
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Efficient Channel Allocation in Wireless Mesh Network

Efficient Channel Allocation in Wireless Mesh Network

ABSTRACT-Efficiently allocating the channels in a network considering various factors like bandwidth, number of data packets, priority of packets etc is becoming a challenging task. A static network is used here to reduce the delay and to send the data packets more efficiently to the destination. To find the optimum path among the paths available, we have proposed a system improvising some performance factors. Also considerable shortest paths are chosen by specific algorithm. And finally the channels are allocated to the corresponding packets so that the residual bandwidth is more and the delay is lesser. The channel allocation scheme handled here is Fixed Channel Allocation. In FCA, there are problems like it is less efficient in handling peer-to-peer traffic. Thus the task to allocate the channels without the degradation of performance factors here becomes more challenging and the new proposed system helps us to allocate the channels efficiently.
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Flowchart for Clustered-Based Channel Allocation Management Scheme

Flowchart for Clustered-Based Channel Allocation Management Scheme

In the GSM system, several Base Transceiver Station (BTS) are controlled by a single Base Station Controller (BSC). In this paper, the BTSs in a given BSC is referred to as a cluster. In this paper, flowchart for clustered-based channel allocation management scheme is presented. The mechanism is meant to reduce call blocking/dropping to the barest minimum. Usually, traffic intensity value varies from one BTS to another, as it is directly proportional to the number of GSM users that make or receive calls within a particular period of time. In order to maintain a reliable GSM network that will provide minimum call blocking/dropping, the paper presents the flowchart for network resource management scheme that will adopt sharing of traffic loads among BTSs that belong to a particular network cluster. The scheme also considers the following; available channels, mobility factor, offered traffic, number of nearby BTSs, new call arrival rate, handoff call arrival rate and mean call duration. With limited available channels per BTS, instead of blocking a new call or dropping a handoff call whenever a particular BTS reaches its maximum allowable capacity, this newly proposed scheme however checks for a nearby BTS within that cluster that has free channels, and then performs a routine operation that will transfer some percentage of the traffic load to the free nearby BTS. This routine operation therefore allows the BTS to admit more calls.
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Optimal channel allocation with idle time 
		usage (OCA ITU): Adaptive channel scheduling strategy for 802 11 based Wireless 
		Local Area Networks

Optimal channel allocation with idle time usage (OCA ITU): Adaptive channel scheduling strategy for 802 11 based Wireless Local Area Networks

This manuscript explores a novel channel-scheduling algorithm for varying size windows transmission in 802.11 Wireless Local Area Networks. The objective of the proposal is to achieve maximum throughput and minimal transmission loss and fair channel usage. The critical factors considered to schedule a channel are optimal bandwidth and idle channel availability. The proposed scheduling strategy is a hierarchical approach of three levels. The optimal idle channel allocation, optimal multiple idle channels allocation and optimal multiple channels with considerable transmission intervals allocation are the objectives of the respective levels of the scheduling hierarchy of the proposed algorithm. The introduction of the WLAN and channel scheduling associated literature, detailed exploration of the proposed channel scheduling strategy and performance analysis by simulation study presented in this article. The experimental study is evincing the scalability and robustness of the proposal in the context of maximizing throughput and minimizing the transmission loss. The performance analysis compared the proposed model with contemporary scheduling strategies found in recent literature.
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Optimal Channel Allocation Mechanism to Improve the QoS in Wireless Networks

Optimal Channel Allocation Mechanism to Improve the QoS in Wireless Networks

We notice that static channel assignments are insufficient for the requirements of 802.11 networks resulting out of the variations of traffic-load. However, in view of the maintenance of compatibility with the existing protocol standards, we opine that it is neither feasible nor necessary to employ dynamic strategies of channel allocation in order to improve system performance in the context of fluctuations of traffic load. Instead, adaptive assignment schemes can be considered as appropriate alternatives. The projected focus of this paper is on our proposal of an adaptive channel allocation mechanism for 802.11 networks depending on the bandwidth aware algorithm. Computer simulation of the proposed adaptive scheme, using the ns2 software reveals a perceptible improvement of data throughput performance over fixed assignment algorithms.
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Index Terms –Channel allocation, cellular network, blocking probability, handoff, channel borrowing

Index Terms –Channel allocation, cellular network, blocking probability, handoff, channel borrowing

In a cellular network, the geographical area is divided into different smaller areas known as cells, and each cell has its own base station, transmitter and receiver, and control unit [1]. The shape of the cell is assumed to be hexagonal because there is no case of area overlap and gap when they are arranged together [2]. Each cell is allotted a set of the disjoint frequency channel [3], by keeping in mind the concept of frequency reuse [4]. It is a technique through which user at the distinct location may simultaneously use the same channel or frequency by using this concept, the spectral efficiency of the communication system is enhanced. There are three main types of channel allocation schemes (a) Fixed channel allocation (FCA) (b) Dynamic channel allocation (DCA), (c) Hybrid channel allocation (HCA) [4, 5]. There is a possibility that user in a call will move from one cell to another cell, then at that time to serve that call a concept of handoff or handover [6] arises, through this concept, there is a smooth transition of a call from one cell to another cell. It is mainly of two types (a) soft handoff (b) hard handoff [7]. Different algorithms are proposed [6-8] to improve spectral efficiency by using channel borrowing FCA and by using optimal spatial dynamic allocation of channels and by using HCA schemes. For each cell, the call blocking and call dropping probability are also calculated by using Erlang B and Erlang C formula [9].
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Optimal Channel Allocation with Hot Spot Technique in Wireless Network

Optimal Channel Allocation with Hot Spot Technique in Wireless Network

Due to increased urge to use the wireless communication in a satisfied way, a promised quality of service is required to manage incoming new calls and handoff more efficiently [2]. In radio resource management for wireless network, proper channel allocation scheme are required to allocate bandwidth for communication channels to base stations, access points and terminal equipment. Call refusal rate or Call drop rate is the main evaluation parameter, when talking about channel allocation scheme i.e. the ratio between numbers of drop calls to the total number of calls. For the given technique Call drop rate can be measured in erlang, where 1 erlang= 1 ongoing call per second. In [5&6] different channel allocation algorithms are used to allocate channels. As in [1-3], Following are the three major categories for assigning channels to cells.
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An intelligent wireless channel allocation in HAPS 5G communication system based on reinforcement learning

An intelligent wireless channel allocation in HAPS 5G communication system based on reinforcement learning

A wireless dynamic channel allocation algorithm for HAPS 5G massive MIMO communication based on distance decision is proposed, which guarantees the quality of service of all kinds of services and maximizes the resource utilization ratio of high-altitude platform communication in reference [10]. Aiming at solving the problem of horizontal swing caused by a stratospheric crosswind on high-altitude platforms, a channel alloca- tion algorithm combining channel reservation with handoff queuing is proposed to solve the problem of handoff between cellular for the ground calling users to continue to obtain reliable services in reference [11]. The algorithm takes full account of the service level re- quirements of different types of user terminals, differenti- ates the priority of user terminals, and queues the handoff callers on the basis of channel reservation from the view- point of reducing the handover failure rate. Although the above literature has solved the problem of channel alloca- tion in HAPS 5G system in some aspects, it is not very suitable for the HAPS 5G massive MIMO network. The future HAPS 5G massive MIMO network will face a large number of data connections and the channel allocation that needs to be processed will be massive, while the HAPS 5G massive MIMO network is an autonomous learning and self-renewal intelligent system for the un- known environment, which can sense the external envir- onment and learn from the environment by using artificial intelligence technology. By changing some operating pa- rameters (such as transmission power, carrier frequency, and modulation technology) in real time, it can adapt to the statistical characteristics of the received wireless sig- nals, so as to realize reliable communication and efficient utilization of spectrum resources anytime and anywhere. Therefore, this paper proposes an intelligent wireless channel allocation algorithm for HAPS 5G massive MIMO communication system based on reinforcement learning, which adopts Q-learning reinforcement learning algorithm in artificial intelligence algorithm and combines back-propagation neural network to enable HAPS 5G massive MIMO communication system to learn inde- pendently according to environment, intelligently accord- ing to channel load and blocking condition. The channel resources are allocated effectively in the system.
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AN EFFICIENT HYBRID CHANNEL ALLOCATION MECHANISM IN MOBILE NETWORK

AN EFFICIENT HYBRID CHANNEL ALLOCATION MECHANISM IN MOBILE NETWORK

situation. When the traffic is uniform, the problem of channel assignment is simple. But when it is non-uniform, the problem is complex or complicated. In FCA scheme, it is important to plan that how many channels are borrowable and how many channels are non borrowable. If the scheme is DCA, then which type of DCA to be followed: centralized or distributed? Whether to adopt FCA or DCA or HCA? These are some of the problem areas to analyze. Whenever there is no possibility to assign a channel to a fresh call, then the call will be blocked. It is tolerable. But at any cost, a continued call should not be dropped or terminated. We need to take care to continue handoff calls. So what should be the mechanism to do so? We need to analyze all of the problems properly so that we can reach at an efficient channel allocation algorithm.
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Channel Allocation for Third Generation Mobile Radio Systems

Channel Allocation for Third Generation Mobile Radio Systems

Figure 1: Blocking and Forced termination performance versus number of users comparison of the Locally Optimized Least Interference Algorithm with 7 \local" BSs and of xed channel alloca[r]

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