Random Channel Assignment Research Articles

Prediction-based channel assignment for minimizing channel switching in mobile WBANs

As the world’s population rises, the healthcare system experiences significant changes. Wireless body area network (WBAN) is an emerging technology that has considerable impact on medical and non-medical applications. However, two crucial challenges in WBANs are interference minimization and channel assignment. High interference may increase collision probability, transmission delay, and energy consumption. Multichannel schemes are proposed to reduce the data transmission latency and improve the system throughput by allowing simultaneous transmission of sensors in coexisting WBANs. When WBAN users move, they need to switch the channels frequently to avoid potential channel conflicts and to maintain the Quality of Service (QoS). However, frequent switching may raise energy consumption and aggravate delay. Existing multichannel assignment schemes failed to perform well in highly dynamic and densely deployed WBANs environments. In contrast to existing studies, this paper proposes a Prediction-based Channel Assignment (PCA) algorithm that selects the channels for WBANs to remain valid for future time instances and thus minimizes the delay and number of channel switches for dynamic and coexisting WBANs. When a WBAN needs to switch a channel, the proposed method predicts the future neighbors of that WBAN based on its history. It explores the channel information of present and future neighbors to select a suitable channel with higher resilience in a dynamic environment. Thus, our algorithm minimizes channel interference by avoiding unnecessary channel switching. We have used machine learning algorithms to predict the future neighbors of a WBAN. Experiment results show that the proposed algorithm performs better than an existing algorithm and random channel assignment in delay and throughput.

Decentralized Adaptive Spectrum Learning in Wireless IoT Networks Based on Channel Quality Information

In the Internet of Things (IoT) context such as low power wide area network (LPWAN), it is essential to reduce the packet losses, e.g., to save energy. Decentralized artificial intelligence (AI) techniques have been proposed to combat radio collisions, but the approach here is extended to deal additionally with the channel propagation effects. In this article, a Quality of Channel Allocation (QoC-A) learning technique based on bandit algorithms is proposed in order to choose the transmission channel. This aims to reduce the effect of the propagation impairments between the radio channels while using the effective signal power (ESP) as a quality metric. In addition, a discounted QoC-A (DQoC-A) algorithm is proposed to adapt rapidly to any abrupt change in the channels’ conditions. An experimental campaign on a real IoT device is carried out to demonstrate the low complexity and efficiency of these proposed decentralized algorithms. In the given results, QoC-A outperforms the classical upper confidence bound (UCB) policy with a more accelerated learning process. On the other hand, the feasibility of using the DQoC-A in nonstationary scenarios is illustrated by its rapid convergence when abrupt changes in the channels’ conditions occur. At the end of the process, these proposed learning techniques give 4.1 and 2.4 times fewer packet losses than the traditional ones with a random channel assignment scheme, in the stationary and nonstationary scenarios, respectively.

Performance analysis of multilayer multicast MANET CRN based on steiner minimal tree algorithm

In this study, the multicast mobile ad hoc (MANET) CRN has been developed, which involves multi-hop and multilayer consideration and Steiner minimal tree (SMT) algorithm is employed as the router protocol. To enhance the network performance with regards to throughput and packet delivery rate (PDR), as channel assignment scheme, the probability of success (POS) is employed that accounts for the channel availability and the time needed for transmission when selecting the best channel from the numerous available channels for data transmission from the source to all destinations nodes effectively. Within Rayleigh fading channels under various network parameters, a comparison is done for the performance of SMT multicast (MANET) CRN with POS scheme versus maximum data rate (MDR), maximum average spectrum availability (MASA) and random channel assignment schemes. Based on the simulation results, the SMT multicast (MANET) CRN with POS scheme was seen to demonstrate the best performance versus other schemes. Also the results proved that the throughput and PDR performance are improved as the number the primary channels and the channel’s bandwidth increased while dropped as the value of packet size D increased. The network’s performance grew with rise in the value of idle probability (P_I) since the primary user’s (PU) traffic load is low when the value of P_I is high.

Resource Allocation in Wireless Powered IoT Networks

In this paper, efficient resource allocation for the uplink transmission of wireless powered IoT networks is investigated. We adopt LoRa technology as an example in the IoT network, but this work is still suitable for other communication technologies. Allocating limited resources, like spectrum and energy resources, among a massive number of users faces critical challenges. We consider grouping wireless powered IoT users into available channels first and then investigate power allocation for users grouped in the same channel to improve the network throughput. Specifically, the user grouping problem is formulated as a many to one matching game. It is achieved by considering IoT users and channels as selfish players which belong to two disjoint sets. Both selfish players focus on maximizing their own utilities. Then we propose an efficient channel allocation algorithm (ECAA) with low complexity for user grouping. Additionally, a Markov Decision Process (MDP) is used to model unpredictable energy arrival and channel conditions uncertainty at each user, and a power allocation algorithm is proposed to maximize the accumulative network throughput over a finite-horizon of time slots. By doing so, we can distribute the channel access and dynamic power allocation local to IoT users. Numerical results demonstrate that our proposed ECAA algorithm achieves near-optimal performance and is superior to random channel assignment, but has much lower computational complexity. Moreover, simulations show that the distributed power allocation policy for each user is obtained with better performance than a centralized offline scheme.

Channel assignment based coding mechanism for reliable transmission for smart cities

The broadband wireless network provides an efficient solution for both data reliability and network congestion to meet the demand of the increasing number of mobile users. However, due to dense deployment of nodes, interference and data dissemination problems occur which result in unreliable packet delivery during data transmission in smart cities. All this creates problem in efficient data transmission. In order to solve this problem, in this work, we propose a novel Code-Encoding and Decoding (C-ED) mechanism for IEEE 802.16 networks that helps to mitigate the problem of data dissemination by using minimal number of channels. The proposed C-ED mechanism provides a reliable packet delivery during multicast transmission. As the smart cities are connected with complex networks, our scheme utilizes the existing channel assignment strategies to assign a minimal number of channels to the network and transmit the data using the proposed C-ED mechanism. To illustrate the effectiveness of the proposed C-ED mechanism, it is evaluated by combining it with the existing channel assignment strategies i.e., clique partitioning and random. The simulation results show that the C-ED mechanism with clique partitioning provides improved quality of service than random channel assignment. The C-ED mechanism is compared with Luby Transform (LT) codes which result in reduced transmission delay and improved throughput for a complex network.

Self-coexistence in cellular cognitive radio networks based on the IEEE 802.22 standard

Cellular networks are the most diffuse type of wireless networks; however, they suffer from spectrum shortage due to the recent increase in smartphone wireless applications and services. Cognitive radios offer a key technology to successfully maintain, optimize and upgrade wireless networks and effectively address spectrum overcrowding problem. CRs have the ability to sense the frequency spectrum, learn from history, and make intelligent decisions to adjust their transmission parameters, and hence, perfectly integrate themselves into the existing wireless networks. The main challenges for implementing cellular CR networks (CCRNs) include the coexistence between CR devices and external wireless cellular networks, and the self-coexistence among these devices. The coexistence/self-coexistence problem in CCRNs can be seen as a channel assignment problem among the network cells. Considering IEEE 802.22 as the standard reference for cellular network mechanisms, this article addresses the coexistence/self-coexistence issues, and proposes two channel assignment schemes for cooperative and non-cooperative CR devices along with their pros and cons. An experimental study and comparison with a random channel assignment demonstrate that a robust and efficient channel assignment scheme is a critical feature in CCRNs.

Channel Assignment for Throughput Optimization in Multichannel Multiradio Wireless Mesh Networks Using Network Coding

Compared to single-hop networks such as WiFi, multihop infrastructure wireless mesh networks (WMNs) can potentially embrace the broadcast benefits of a wireless medium in a more flexible manner. Rather than being point-to-point, links in the WMNs may originate from a single node and reach more than one other node. Nodes located farther than a one-hop distance and overhearing such transmissions may opportunistically help relay packets for previous hops. This phenomenon is called opportunistic overhearing/listening. With multiple radios, a node can also improve its capacity by transmitting over multiple radios simultaneously using orthogonal channels. Capitalizing on these potential advantages requires effective routing and efficient mapping of channels to radios (channel assignment (CA)). While efficient channel assignment can greatly reduce interference from nearby transmitters, effective routing can potentially relieve congestion on paths to the infrastructure. Routing, however, requires that only packets pertaining to a particular connection be routed on a predetermined route. Random network coding (RNC) breaks this constraint by allowing nodes to randomly mix packets overheard so far before forwarding. A relay node thus only needs to know how many packets, and not which packets, it should send. We mathematically formulate the joint problem of random network coding, channel assignment, and broadcast link scheduling, taking into account opportunistic overhearing, the interference constraints, the coding constraints, the number of orthogonal channels, the number of radios per node, and fairness among unicast connections. Based on this formulation, we develop a suboptimal, auction-based solution for overall network throughput optimization. Performance evaluation results show that our algorithm can effectively exploit multiple radios and channels and can cope with fairness issues arising from auctions. Our algorithm also shows promising gains over traditional routing solutions in which various channel assignment strategies are used.

Improper colouring of (random) unit disk graphs

For any graph G, the k-improper chromatic number χ k ( G ) is the smallest number of colours used in a colouring of G such that each colour class induces a subgraph of maximum degree k. We investigate χ k for unit disk graphs and random unit disk graphs to generalise results of McDiarmid and Reed [Colouring proximity graphs in the plane, Discrete Math. 199(1–3) (1999) 123–137], McDiarmid [Random channel assignment in the plane, Random Structures Algorithms 22(2) (2003) 187–212], and McDiarmid and Müller [On the chromatic number of random geometric graphs, submitted for publication].

On The Span Of A Random Channel Assignment Problem

In the radio channel assignment problems considered here, we must assign a ‘channel’ from the set 1,2,... of positive integers to each of n transmitters, and we wish to minimise the span of channels used, subject to the assignment leading to an acceptable level of interference. A standard form of this problem is the ‘constraint matrix’ model. The simplest case of this model (the 0, 1 case) is essentially graph colouring. We consider here a random model for the next simplest case (with lengths 0, 1 or 2), and determine the asymptotic behaviour of the span of channels needed as n→∞. We find that there is a ‘phase change’ in this behaviour, depending on the probabilities for the different lengths.

Asymptotically exact analysis of a loss network with channel continuity

Two channel assignment policies are considered for a Kelly type loss network with an additional channel continuity requirement. It is assumed that the channels on any given link have distinct identities, and that a connection should be assigned channels with a common identity on all links of its route. Such constraints arise in circuit switched WDM optical networks and wireless cellular networks. A functional law of large numbers, which was previously developed by Hunt and Kurtz and later refined by Zachary and Ziedins, is adapted to analyze a network with two links and three connection types. Asymptotically exact fluid-type approximations for the network process are obtained and their operating points are characterized. The results lead to asymptotic call blocking rates and point out that in cases of practical interest, random channel assignment has asymptotically the same blocking performance with more sophisticated channel assignment policies.

Random channel assignment in the plane

AbstractIn the model for random radio channel assignment considered here, points corresponding to transmitters are thrown down independently at random in the plane, and we must assign a radio channel to each point but avoid interference. In the most basic version of the model, we assume that there is a threshold d such that, in order to avoid interference, points within distance less than d must be assigned distinct channels. Thus we wish to color the nodes of a corresponding scaled unit disk graph. We consider the first n random points, and we are interested in particular in the behavior of the ratio of the chromatic number to the clique number. We show that, as n → ∞, in probability this ratio tends to 1 in the “sparse” case [when d = d(n) is such that the average degree grows more slowly than ln n] and tends to $2\sqrt{3}/\pi$ ∼ 1.103 in the “dense” case (when the average degree grows faster than ln n). We also consider related graph invariants, and the more general channel assignment model when assignments must satisfy “frequency‐distance” constraints. © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 22: 187–212, 2003

Random spacing channel assignment to reduce the nonlinear intermodulation distortion in cellular mobile communications

In cellular mobile communications, only a subset of all available frequency channels are assigned to each cell, hence, the interchannel spacing of channels is wider than the required minimum. In this paper, a random spacing channel assignment (RSCA) scheme is proposed which exploits the wider interchannel spacing of assigned channels in each cell to reduce the most deleterious third-order intermodulation distortion caused by the nonlinear devices, such as hard limiters or nonlinear power amplifiers. Results show that the proposed RSCA scheme can reduce the third-order intermodulation noise power by approximately 4-5 dB and yet it can still satisfy the minimum adjacent channel spacing requirement.