Uncrewed Aerial Vehicles (UAVs) are increasingly used in civilian and industrial applications, making secure low-altitude operations crucial. In dense mmWave environments, accurately classifying low-altitude UAVs as either inside authorized or restricted airspaces remains challenging, requiring models that handle complex propagation and signal variability. This paper proposes a deep learning model, referred to as CoBA, which stands for integrated Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention which leverages Fifth Generation (5G) millimeter-wave (mmWave) radio measurements to classify UAV operations in authorized and restricted airspaces at low altitude. The proposed CoBA model integrates convolutional, bidirectional recurrent, and attention layers to capture both spatial and temporal patterns in UAV radio measurements. To validate the model, a dedicated dataset is collected using the 5G mmWave network at TalTech, with controlled low altitude UAV flights in authorized and restricted scenarios. The model is evaluated against conventional ML models and a fingerprinting-based benchmark. Experimental results show that CoBA achieves superior accuracy, significantly outperforming all baseline models and demonstrating its potential for reliable and regulated UAV airspace monitoring.

The Unmanned Aerial Vehicle (UAV)-assisted networks play a vital role in disaster circumstances for quick rescue operations by enabling emergency communication services. Unlike conventional networks, emergency networks have unique challenges, such as encountering Critical Nodes (CNs) that contain vital information. The effectiveness of rescue operations mainly depends on the coverage of these CNs to retrieve essential data for coordinating rescue efforts. In this context, Age-of-Information (AoI) is used to evaluate the timely collection of data from CNs. Voronoi diagram-based partitioning is employed as an adaptive mechanism linked with UAV swarm size and K-means clustering to enable nodes distribution-aware spatial partitioning, ensuring collision-free path planning in disaster scenarios. The distance-optimized CNA trajectory is proposed to optimize UAV swarm paths for coverage maximization and AoI minimization by adapting the scalarization approach. The performance of the proposed algorithm is analyzed based on coverage, AoI, trajectory length, and total flight time. Simulation results show that the proposed distance-optimized CNA trajectory outperforms the conventional distance-based and the CNA trajectory by 25.27% and 41.20%, respectively. It improves the CNs coverage and AoI of distance-based trajectory by 30% and 10%, priority-based Traveling Salesman Problem (TSP) by 47% and 7%, and CNA trajectory by 13% and 17%, respectively. When the percentage of CNs increases from 10% to 40%, the number of covered CNs increases linearly for a given number of hovering points.

We propose EMORe, an adaptive reconstruction method designed to enhance motion robustness in free-running, free-breathing self-gated 5D cardiac magnetic resonance imaging (MRI). Traditional self-gating-based motion binning for 5D MRI often results in residual motion artifacts due to inaccuracies in cardiac and respiratory signal extraction and sporadic bulk motion, compromising clinical utility. EMORe addresses these issues by integrating adaptive inter-bin correction and explicit outlier rejection within an expectation-maximization (EM) framework, whereby the E-step and M-step are executed alternately until convergence. In the E-step, probabilistic (soft) bin assignments are refined by correcting misassignment of valid data and rejecting motion-corrupted data to a dedicated outlier bin. In the M-step, the image estimate is improved using the refined soft bin assignments. Validation in a simulated 5D MRXCAT phantom demonstrated EMORe’s superior performance compared to standard compressed sensing reconstruction, showing significant improvements in peak signal-to-noise ratio, structural similarity index, edge sharpness, and bin assignment accuracy across varying levels of simulated bulk motion. In vivo validation in 13 volunteers further confirmed EMORe’s robustness, significantly enhancing blood-myocardium edge sharpness and reducing motion artifacts compared to compressed sensing, particularly in scenarios with controlled coughing-induced motion. Although EMORe incurs a modest increase in computational complexity, its adaptability and robust handling of bulk motion artifacts improves image quality, supporting its potential for improved clinical applicability and diagnostic confidence of 5D cardiac MRI.

In ill-posed imaging inverse problems, there can exist many hypotheses that fit both the observed measurements and prior knowledge of the true image. Rather than returning just one hypothesis of that image, posterior samplers aim to explore the full solution space by generating many probable hypotheses, which can later be used to quantify uncertainty or construct recoveries that appropriately navigate the perception/distortion trade-off. In this work, we propose a fast and accurate posterior-sampling conditional generative adversarial network (cGAN) that, through a novel form of regularization, aims for correctness in the posterior mean as well as the trace and K principal components of the posterior covariance matrix. Numerical experiments demonstrate that our method outperforms competitors in a wide range of ill-posed imaging inverse problems.

Due to their potential to accomplish complicated missions more effectively, UAV swarms have attracted a lot of attention lately. UAV swarm offers enhanced intelligence, improved coordination, increased flexibility, survivability, and reconfigurability. It is a multi-disciplinary system that necessitates a tight integration of several sub-systems, including optimal trajectory planning, localization, task coordination, etc. This review covers the important aspects of UAV swarms including swarm formation control, communication, swarm path planning, autonomy, coordination, and security. It additionally explores recent technical advancements in UAV swarm algorithms that have made the development of complex UAV swarm systems possible. This paper also provides insight into ethical aspects and the use cases of UAV swarms in various military, civilian, and entertainment applications. This paper is concluded by highlighting the potential future directions and challenges of UAV swarm technology and the need for more research and development to exploit their potential fully. Overall, this paper presents a comprehensive review of UAV swarm technology, addressing its potential for revolutionizing many fields and supporting advancements in the future.

Abstract Due to the increase in the growth of data in this era of the digital world and limited resources, there is a need for more efficient data compression techniques for storing and transmitting data. Data compression can significantly reduce the amount of storage space and transmission time to store and transmit given data. More specifically, text compression has got more attention for effectively managing and processing data due to the increased use of the internet, digital devices, data transfer, etc. Over the years, various algorithms have been used for text compression such as Huffman coding, Lempel-Ziv-Welch (LZW) coding, arithmetic coding, etc. However, these methods have a limited compression ratio specifically for data storage applications where a considerable amount of data must be compressed to use storage resources efficiently. They consider individual characters to compress data. It can be more advantageous to consider words or sequences of words rather than individual characters to get a better compression ratio. Compressing individual characters results in a sizeable compressed representation due to their less repetition and structure in the data. In this paper, we proposed the ArthNgram model, in which the N-gram language model coupled with arithmetic coding is used to compress data more efficiently for data storage applications. The performance of the proposed model is evaluated based on compression ratio and compression speed. Results show that the proposed model performs better than traditional techniques.

Accelerated magnetic resonance (MR) imaging attempts to reduce acquisition time by collecting data below the Nyquist rate. As an ill-posed inverse problem, many plausible solutions exist, yet the majority of deep learning approaches generate only a single solution. We instead focus on sampling from the posterior distribution, which provides more comprehensive information for downstream inference tasks. To do this, we design a novel conditional normalizing flow (CNF) that infers the signal component in the measurement operator’s nullspace, which is later combined with measured data to form complete images. Using fastMRI brain and knee data, we demonstrate fast inference and accuracy that surpasses recent posterior sampling techniques for MRI. Code is available at https://github.com/jwen307/mri_cnf

In image recovery problems, one seeks to infer an image from distorted, incomplete, and/or noise-corrupted measurements. Such problems arise in magnetic resonance imaging (MRI), computed tomography, deblurring, super-resolution, inpainting, phase retrieval, image-to-image translation, and other applications. Given a training set of signal/measurement pairs, we seek to do more than just produce one good image estimate. Rather, we aim to rapidly and accurately sample from the posterior distribution. To do this, we propose a regularized conditional Wasserstein GAN that generates dozens of high-quality posterior samples per second. Our regularization comprises an $\ell_1$ penalty and an adaptively weighted standard-deviation reward. Using quantitative evaluation metrics like conditional Fréchet inception distance, we demonstrate that our method produces state-of-the-art posterior samples in both multicoil MRI and large-scale inpainting applications. The code for our model can be found here: https://github.com/matt-bendel/rcGAN.

Unmanned Aerial Vehicles (UAV) equipped with onboard embedded platforms and camera sensors provide access to difficult-to-reach areas and facilitate in remote sensing and autonomous decision-making capabilities in disaster recovery and management applications. Onboard computations are preferred due to connectivity, privacy, and latency problems. However, edge implementation becomes challenging because of limited onboard hardware resources (in terms of area, power, and storage). In this paper, we propose a UAV assisted edge computation framework that compresses the Convolutional Neural Networks (CNN) models to be run on an onboard embedded Graphics Processing Unit (GPU) for real-time disaster scenario classification. We use an imbalanced dataset named, Aerial Image Database for Emergency Response (AIDER), to replicate real-world disaster scenarios. Our experimental results show that optimized compressed model’s throughput is increased by about 99% which is up to 92x faster than the native model. Furthermore, the model size reduction enabled through proposed framework is about 84% without compromising accuracy and thus makes it suitable for edge GPUs.

Plug-and-play (PnP) methods that employ application-specific denoisers have been proposed to solve inverse problems, including MRI reconstruction. However, training application-specific denoisers is not feasible for many applications due to the lack of training data. In this work, we propose a PnP-inspired recovery method that does not require data beyond the single, incomplete set of measurements. The proposed self-supervised method, called recovery with a self-calibrated denoiser (ReSiDe), trains the denoiser from the patches of the image being recovered. The denoiser training and a call to the denoising subroutine are performed in each iteration of a PnP algorithm, leading to a progressive refinement of the reconstructed image. For validation, we compare ReSiDe with a compressed sensing-based method and a PnP method with BM3D denoising using single-coil MRI brain data.

For image recovery problems, plug-and-play (PnP) methods have been developed that replace the proximal step in an optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network. Although such methods have been successful, they can be improved. For example, the denoiser is often trained using white Gaussian noise, while PnP's denoiser input error is often far from white and Gaussian, with statistics that are difficult to predict from iteration to iteration. PnP methods based on approximate message passing (AMP) are an exception, but only when the forward operator behaves like a large random matrix. In this work, we design a PnP method using the expectation consistent (EC) approximation algorithm, a generalization of AMP, that offers predictable error statistics at each iteration, from which a deep-net denoiser can be effectively trained.

Nonuniform array geometries provide freedom for increased aperture and reduced mutual coupling. A necessary and sufficient condition is given for an array of isotropic sensor elements to be unambiguous for any specified set of directions of arrival. The set of unambiguous spatial frequencies is shown to be a parallelepiped, admitting simple geometrical interpretation. Results are used in design of linear, planar, and 3D arrays.

In phase-contrast magnetic resonance imaging (PC-MRI), spin velocity contributes to the phase measured at each voxel. Therefore, estimating velocity from potentially wrapped phase measurements is the task of solving a system of noisy congruence equations. We propose Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing. The estimator can recover the fullest possible extent of unambiguous velocities, which can greatly exceed twice the highest venc. The estimator uses all pairwise phase differences and the inherent correlations among them to minimize the estimation error. Correlations are directly estimated from multi-coil data without requiring knowledge of coil sensitivity maps, dephasing factors, or the actual per-voxel signal-to-noise ratio. Derivation of the estimator yields explicit probabilities of unwrapping errors and the probability distribution for the velocity estimate; this, in turn, allows for optimized design of the phase-encoded acquisition. These probabilities are also incorporated into spatial post-processing to further mitigate wrapping errors. Simulation, phantom, and in vivo results for three-point PC-MRI acquisitions validate the benefits of reduced estimation error, increased recovered velocity range, optimized acquisition, and fast computation. A phantom study at 1.5T demonstrates 48.5% decrease in root mean squared error using PRoM with post-processing versus a conventional "dual-venc" technique. Simulation and 3T in vivo results likewise demonstrate the proposed benefits.

The exponential growth of the Internet of Things (IoT) devices has resulted in huge power consumption issues in wireless devices. Cooperative communication with simultaneous wireless information and power transfer (SWIPT) is a promising technology to improve the coverage, capacity, power consumption, and reliability of the IoT networks. Relay selection plays a pivotal role in cooperative communications for improving the quality of service (QoS) of the network. In this paper, we propose a SWIPT based hybrid protocol to improve the QoS in terms of average end-to-end outage probability through proposed relay selection. Furthermore, we investigate the impact of time switching factor α and power splitting factor λ on the average outage probability and the amount of energy harvested. The simulation results demonstrate that by selecting the best relay through proposed relay selection, we observe maximum improvement of 94 % in terms of average end-to-end outage over the worst relay at transmit power of 38 dBm. Similarly, we observe an increase in the amount of energy harvested by approximately 90% over the worse relay at transmit power of 45 dBm.

Motivated by image recovery in magnetic resonance imaging (MRI), we propose a new approach to solving linear inverse problems based on iteratively calling a deep neural-network, sometimes referred to as plug-and-play recovery. Our approach is based on the vector approximate message passing (VAMP) algorithm, which is known for mean-squared error (MSE)-optimal recovery under certain conditions. The forward operator in MRI, however, does not satisfy these conditions, and thus we design new damping and initialization schemes to help VAMP. The resulting DD-VAMP++ algorithm is shown to outperform existing algorithms in convergence speed and accuracy when recovering images from the fastMRI database for the practical case of Cartesian sampling.

A new backoff scheme named Unique Backoff Algorithm (UBA) has been introduced in this paper for IEEE 802.15.6 Wireless Body Area Networks (WBANs). This scheme tries to eliminate the collision among nodes by assigning unique backoff values. UBA removes the large delays during transmissions between different nodes and hence increases overall system performance. Furthermore, the concept of Backup Slot ( BS) has also been introduced in the superframe to accommodate one failure of highest priority in the current superframe. For evaluation, proposed scheme has been simulated in comparison with the Binary Exponential Backoff (BEB) using different priorities. Results show that our scheme outperforms BEB in terms of throughput and superframe efficiency.

Wireless Mesh Networks provide a reliable, robust and resilient platform for broadband access. Main benefits of using Wireless Mesh Networks are their low cost, robustness, self healing, and self configuring properties. In Wireless Mesh Networks, routing metric determines the path from source to destination. Wireless link conditions can be affected by a number of factors including interference, congestion, mobility, and network topology. Routing metric needs to consider all these factors while making routing decisions. In addition, wireless link conditions do not remain static with time requiring the routing metric to be adaptive. Interference in Wireless Mesh Networks are of two types: inter-channel and intra-channel interference. Existing routing metrics for Wireless Mesh Networks either consider only one of the two interference types or do not capture changing network conditions. In this paper, we propose a new routing metric for Wireless Mesh Networks which takes into account both inter and intra-channel interference and is adaptive to changing network conditions. Our proposed metric is compared with the state of the art and shows throughput improvement of up to 20 percent and latency reduction of 25 percent.

In this work, we present a novel technique for image recovery from highly undersampled phase contrast magnetic resonance imaging (PC-MRI) data. Our approach is holistic and includes modeling of the underlying physics, optimized sampling strategies, and design of a novel inversion algorithm. We capture the unique magnitude and phase structure of PC-MRI data as a non-Gaussian conditional mixture density. We then create a factor graph that describes the joint posterior probability of the inference variables given the noisy measurements. A combination of standard belief propagation and generalized approximate message passing (GAMP) is used to form an iterative inversion algorithm capable of producing MAP or MMSE estimates of the signal of interest. Using the proposed technique, we demonstrate high-fidelity PC-MRI recovery for simulated and phantom data undersampled by a factor of ten.

Wireless Body Area Networks (WBANs) is a new technology for remote monitoring of patients. Sensor nodes are placed on different parts of the body such as implants and on body to collect data and transfer to the Sink node. Change in body posture, placement of sensors, priority of sensor data and energy consumption makes routing very difficult. Therefore, a Priority based Energy Aware (PEA) routing protocol is proposed in this paper. Child nodes choose a parent node connected to Sink based on a cost function that depends upon priority, residual energy and distance of node. Residual energy facilitates load balancing i.e. selection of different nodes for transmission. Distance helps in successful packet delivery to the parent node and caters for body postures. Priority helps to select a best possible path to forward the critical data keeping in view the energy constraint in WBANs. Comparison of different cost functions with proposed PEA protocol for performance metrics such as network lifetime, throughput and residual energy reveals that the proposed protocol results in increased network lifetime, throughput improvement of around 50% and higher residual energy.

In this paper, performance of relay based MAC protocol for two way traffic is evaluated in non-saturated condition. In real life, non-saturation is more prevalent where nodes are mostly idle. The idea of Two Way Opportunistic-Medium Access Control (TWO-MAC) protocol is inspired from the current drive to "go green". TWO-MAC uses high data rate nodes to work as relays (by using network coding) for the low data rate nodes in two way communications. TWO-MAC results in higher throughput, lower delays and reduced energy consumption. This is due to the use of relay and network coding for two way transmission which lowers overall blocking time and contention due to faster transmission. Further, it lowers unnecessary overhead and overhearing which reduces the energy consumption. The contribution in this work is the throughput, energy and delay gains of using network coding at the relay in a realistic non-saturated network.

The performance of a joint Hierarchical Modulation (HM) and Network Coding (NC) scheme for two way relay networks is introduced and evaluated in this paper. The proposed scheme uses selective relaying based on a signal-to-noise (SNR) threshold at the relay. In particular, a two way cooperative network with two sources and one relay is considered. Two different protection classes are modulated by a hierarchical 4/16-Quadrature Amplitude Modulation (QAM) constellation at the source. Based on the instantaneous received SNR at the relay, the relay decides to retransmit both classes by using a hierarchical 4/16-QAM constellation, the more- protection class by using a Quadrature Phase Shift Keying (QPSK) constellation, or remains silent. These thresholds at the relay give rise to multiple transmission scenarios in a two way cooperative network. Simulation results verify our analysis and efficacy of the scheme.

Public Cloud Computing (PCC) delivers technology "As a Service". It is widely accepted by consumers, enterprises and, even governments because it reduces financial budget for acquiring Information technology (IT) infrastructure. The major deterrence against its adoption is security and governance risks. These risks are associated with three facets, geographical location of cloud provider, change of governance level within cloud service layers and inadequacy of existing international security standards to maintain security. In this paper, these three domains are researched to formulate governance life cycle framework for managing user data security in PCC.

A nested uniform sampling over the sphere is presented. The set of sampling points contains a sequence of nested subsets, each providing an approximately uniform covering of the sphere. The proposed Matryoshka covering of the sphere allows multiresolution 3-D imaging. Simulation and electron paramagnetic resonance imaging results illustrate its application to 3-D tomography.

This paper analyzes the delay performance of Enhanced relay-enabled Distributed Coordination Function (ErDCF) for wireless ad hoc networks under ideal condition and in the presence of transmission errors. Relays are nodes capable of supporting high data rates for other low data rate nodes. In ideal channel ErDCF achieves higher throughput and reduced energy consumption compared to IEEE 802.11 Distributed Coordination Function (DCF). This gain is still maintained in the presence of errors. It is also expected of relays to reduce the delay. However, the impact on the delay behavior of ErDCF under transmission errors is not known. In this work, we have presented the impact of transmission errors on delay. It turns out that under transmission errors of sufficient magnitude to increase dropped packets, packet delay is reduced. This is due to increase in the probability of failure. As a result the packet drop time increases, thus reflecting the throughput degradation.

Dense deployments of wireless local area networks (WLANs) are becoming a norm in many cities around the world. However, increased interference and traffic demands can severely limit the aggregate throughput achievable unless an effective channel assignment scheme is used. In this work, a simple and effective distributed channel assignment (DCA) scheme is proposed. It is shown that in order to maximise throughput, each access point (AP) simply chooses the channel with the minimum number of active neighbour nodes (i.e. nodes associated with neighbouring APs that have packets to send). However, application of such a scheme to practice depends critically on its ability to estimate the number of neighbour nodes in each channel, for which no practical estimator has been proposed before. In view of this, an extended Kalman filter (EKF) estimator and an estimate of the number of nodes by AP are proposed. These not only provide fast and accurate estimates but can also exploit channel switching information of neighbouring APs. Extensive packet level simulation results show that the proposed minimum neighbour and EKF estimator (MINEK) scheme is highly scalable and can provide significant throughput improvement over other channel assignment schemes.

Dense deployments of wireless local area networks (WLANs) are fast becoming a permanent feature of all developed cities around the world. While this increases capacity and coverage, the problem of increased interference, which is exacerbated by the limited number of channels available, can severely degrade the performance of WLANs if an effective channel assignment scheme is not employed. In an earlier work, an asynchronous, distributed and dynamic channel assignment scheme has been proposed that (1) is simple to implement, (2) does not require any knowledge of the throughput function, and (3) allows asynchronous channel switching by each access point (AP). In this paper, we present extensive performance evaluation of this scheme when it is deployed in the more practical non-uniform and dynamic topology scenarios. Specifically, we investigate its effectiveness (1) when APs are deployed in a nonuniform fashion resulting in some APs suffering from higher levels of interference than others and (2) when APs are effectively switched `on/off' due to the availability/lack of traffic at different times, which creates a dynamically changing network topology. Simulation results based on actual WLAN topologies show that robust performance gains over other channel assignment schemes can still be achieved even in these realistic scenarios.

The popularity of wireless local area networks (WLANs) has resulted in their dense deployments around the world. While this increases capacity and coverage, the problem of increased interference can severely degrade the performance of WLANs. However, the impact of interference on throughput in dense WLANs with multiple access points (APs) has had very limited prior research. This is believed to be due to 1) the inaccurate assumption that throughput is always a monotonically decreasing function of interference and 2) the prohibitively high complexity of an accurate analytical model. In this work, firstly we provide a useful classification of commonly found interference scenarios. Secondly, we investigate the impact of interference on throughput for each class based on an approach that determines the possibility of parallel transmissions. Extensive packet-level simulations using OPNET have been performed to support the observations made. Interestingly, results have shown that in some topologies, increased interference can lead to higher throughput and vice versa.

This paper analyzes the performance of enhanced relay-enabled distributed coordination function (ErDCF) for wireless ad hoc networks under transmission errors. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduces energy consumption compared to IEEE 802.11 distributed coordination function (DCF) in an ideal channel environment. However, there is a possibility that this expected gain may decrease in the presence of transmission errors. In this work, we modify the saturation throughput model of ErDCF to accurately reflect the impact of transmission errors under different rate combinations. It turns out that the throughput gain of ErDCF can still be maintained under reasonable link quality and distance.

Wireless local area networks (WLANs) have changed the way many of us communicate, work, play and live. Due to its popularity, dense deployments are becoming a norm in many cities around the world. However, increased interference and traffic demands can severely limit the aggregate throughput achievable if an effective channel assignment scheme is not used. In this paper, we propose an enhanced asynchronous distributed and dynamic channel assignment scheme that is simple to implement, does not require any knowledge of the throughput function, allows asynchronous channel switching by each access point (AP) and is superior in performance. Simulation results show that our proposed scheme converges much faster than previously reported synchronous schemes, with a reduction in convergence time and channel switches by up to 73.8% and 30.0% respectively.

In this paper we propose an enhanced relay-enabled distributed coordination function (rDCF) for wireless ad hoc networks. The idea of rDCF is to use high data rate nodes to work as relays for the low data rate nodes. The relay helps to increase the throughput and lower overall blocking time of nodes due to faster dual-hop transmission. rDCF achieves higher throughput over IEEE 802.11 distributed coordination function (DCF). The protocol is further enhanced for higher throughput and reduced energy. These enhancements result from the use of a dynamic preamble (i.e. using short preamble for the relay transmission) and also by reducing unnecessary overhearing (by other nodes not involved in transmission). We have modeled the energy consumption of rDCF, showing that rDCF provides an energy efficiency of 21.7% at 50 nodes over 802.11 DCF. Compared with the existing rDCF, the enhanced rDCF (ErDCF) scheme proposed in this paper yields a throughput improvement of 16.54% (at the packet length of 1000 bytes) and an energy saving of 53% at 50 nodes.

Due to its popularity, dense deployments of wireless local area networks (WLANs) are becoming a common feature of many cities around the world. However, with only a limited number of channels available, the problem of increased interference can severely degrade the performance of WLANs if an effective channel assignment scheme is not employed. In an earlier work, we proposed an improved asynchronous distributed and dynamic channel assignment scheme that (1) is simple to implement, (2) does not require any knowledge of the throughput function, and (3) allows asynchronous channel switching by each access point (AP). In this paper, we present extensive performance evaluation of the proposed scheme in practical scenarios found in densely populated WLAN deployments. Specifically, we investigate the convergence behaviour of the scheme and how its performance gains vary with different number of available channels and in different deployment densities. We also prove that our scheme is guaranteed to converge in a single iteration when the number of channels is greater than the number of neighbouring APs.