Adaptive Channel Allocation Research Articles

(r, Q) stock replenishment in multi-mode distribution systems with uncertain delivery channels and adaptive channel allocation

To mitigate the impact of uncertainty in goods distribution systems, multiple channels and transportation modes may be used. In this paper, a discrete-time model of supplier-depot interaction involving multiple channels of goods relocation is constructed using the concepts of dynamic systems theory. The considered class of systems faces two types of uncertainty: a priori unknown variations of market demand and unpredictable changes of lead-time delay, different for each channel. The variation pattern is arbitrary. As a control mechanism, the nonlinear (r, Q) replenishment policy (provide Q units of goods when the stock falls below r) is considered. As opposed to the previous studies, the policy is demonstrated robust to both sources of uncertainty acting simultaneously using a formal mathematical argument. Moreover, it is shown how to select the policy parameters to obtain full demand realization, thus eliminating backorders, irrespective of the pattern of delay and demand fluctuations. It is also discussed how to assign the depot capacity so that emergency storage will not be required even though deliveries from a few periods arrive concurrently, or out of order. In addition, a new channel allocation strategy, dynamically adapting itself to the channel temporal characteristics, is developed. As evidenced in the tests, the proposed adaptive strategy, by reducing the waiting time for the lot reassembly at the depot, allows for holding cost reduction by several percent with respect to a static strategy.

Adaptive Channel Allocation for Robust Differentiable Architecture Search.

Differentiable architecture search (DARTS) has attracted much attention due to its simplicity and significant improvement in efficiency. However, the excessive accumulation of the skip connection, when training epochs become large, makes it suffer from weak stability and low robustness, thus limiting its practical applications. Many works have attempted to restrict the accumulation of skip connections by indicators or manual design. These methods, however, are susceptible to human priors and hyperparameters. In this work, we suggest a more subtle and direct approach that no longer explicitly searches for skip connections in the search stage, based on the paradox that skip connections were proposed to guarantee the performance of very deep networks, but the networks in the search stage of DARTS are actually very shallow. Instead, by introducing channel importance ranking and channel allocation strategy, the skip connections are implicitly searched and automatically refilled unimportant channels in the evaluation stage. Our method, dubbed adaptive channel allocation (ACA) strategy, is a general-purpose approach for DARTS, which universally works in DARTS variants without introducing human priors, indicators, or hyperparameters. Extensive experiments on various datasets and DARTS variants verify that the ACA strategy is the most effective one among the existing methods in improving robustness and dealing with the collapse issue when training epochs become large.

Load-Aware Channel Allocation for IEEE 802.11ah-Based Networks

Most wireless communication technologies addressed to the Internet of Things (IoT) applications face the bottleneck of dense and large-scale use cases. One solution to this problem is a periodic channel reservation strategy, in which only a small group of stations can compete for channel access during a given period. The IEEE 802.11ah standard, a.k.a. Wi-Fi HaLow, deploys this idea in its channel access protocol, named Restricted Access Window (RAW). A single RAW consists of one or more RAW slots during which only designated stations can contend for channel access. This paper considers an IEEE 802.11ah-based network with randomly distributed stations around the Access Point (AP), operating under a Rayleigh-fading channel with capture enabled. We develop an analytical model to evaluate the contention of a group of stations and propose a Load-Aware Channel Allocation (LACA) algorithm for the RAW slot period. The LACA algorithm ensures the delivery of all packets that designated stations carry, allowing the allocation of load-aware RAW slots, which is effective in enhancing the Age of Information (AoI). We evaluate the Packet Delivery Ratio (PDR) and channel usage within a pre-allocated RAW slot to prove the effectiveness of our proposal. We further study the impact of the spatial distribution of the stations around the AP and the capture effect under a Rayleigh channel on the performance of the proposed LACA algorithm. Extensive simulations are used to validate our analytical results. Our proposal provides a load-aware and adaptive channel allocation scheme based on the dynamic conditions of the network. Our model can be implemented in a global configuration scheme for the RAW mechanism in heterogeneous networks or for alternative communication technologies that address dense scenarios with the integration of periodic channel reservations.

Downlink channel analysis at multi-cell massive MIMO: novel adaptive channel allocation scheme

In large multiple-input multiple-output (MIMO) systems, we offer a new access strategy for crowd situations. Crowd situations include a high number users who have intermittent access patterns, making orthogonal scheduling impossible. In this paper, the work is driven by the massive MIMO problem at crowded environment. The pilot contamination problem is dependent on the protocol assumptions is observed in the system. We investigate a crowd situation in which the users number and their access behaviours make scheduling broadcasts impossible. The pilot assignment is time-slot randomized, but the data portion is repeated, allowing it to use successive-interference-cancellation across copies of comparable packets. In result analysis, the transmission of downlink to the user has shown with changing the number of users, fading effect, transmission power and the number of base station antennas.

Adaptive channel allocation for spectrum sharing based on ACO algorithm

Adaptive channel allocation for spectrum sharing based on ACO algorithm

A Novel Adaptive Channel Allocation Scheme for Multi-Cell Massive MIMO Systems

A Novel Adaptive Channel Allocation Scheme for Multi-Cell Massive MIMO Systems

A Delay Balanced Adaptive Channel Allocation Mechanism for LTE-U and WiFi Coexistence Systems

This paper proposes a delay-balanced adaptive channel allocation (DB-ACA) mechanism for improving the channel access performance of an LTE-U and WiFi coexistence system. To support the DB-ACA mechanism, an integrated network architecture is introduced for coordinating the channel access in the coexistence system. Based on the integrated network architecture, the coexistence system is able to adaptively adjust the number of unlicensed channels allocated to the LTE-U system and WiFi system, respectively, according to the current traffic load status of the coexistence system. To ensure the access fairness of the LTE-U and WiFi systems, the DB-ACA mechanism introduces a fairness criterion, which requires that the channel access delay be balanced on the unlicensed band between the LTE-U and WiFi systems. Theoretical models are developed to determine the number of unlicensed channels that need to be adjusted, and performance models are derived to analyze the blocking probability and access delay of the LTE-U system and WiFi system, respectively. The effectiveness of the performance models is validated through simulation results. It is demonstrated through numerical results that the proposed DB-ACA mechanism can effectively ensure access fairness of the coexistence system, and significantly improve the system performance in terms of the blocking probability and access delay.

Adaptive beam formation and channel allocation using substance near multicast protocol and CS-iEHO

Adaptive beam formation and channel allocation using substance near multicast protocol and CS-iEHO

An improved fast transform algorithm based on adaptive hybrid channel allocation in TSCH networks

Time Slotted Channel Hopping (TSCH) is one of the five access behavior technologies defined in the IEEE 802.15.4e standard. Research shows that the wireless sensor network with single dynamic and static channel allocation in TSCH mode has higher performance. The clogging rate results in unnecessary energy waste. Therefore, this paper proposes an adaptive hybrid channel allocation strategy (AHCA) to measure the link quality change of wireless sensor networks in TSCH mode. The strategy estimates the part of the channel quality and determines whether the node wants to transmit on this channel and the optimal channel for node information transmission. According to the change of the amount of information transmitted, the hybrid channel allocation scheme of the dynamic and static channel ratio is adaptively adjusted to reduce the energy consumption of the information transmission process. This article performs experimental simulation on Contiki-NG. The experimental results show that the proposed scheme has shorter transmission time and lower congestion rate than the single dynamic static channel allocation wireless sensor network. The more reasonable and effective measurement, analysis and use of channel resources can fundamentally increase the amount of information transfer in wireless sensor networks per unit time, and thus extend the lifetime of wireless sensor networks.

Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks

The wireless mesh network (WMN) has proven to be a great choice for network communication technology. WMNs are composed of access points (APs) that are installed and communicate with each other through multi-hop wireless networks. One or more of these APs acts as a gateway (GW) to the internet. Hosts of WMNs are stationary or mobile. According to the structure of WMNs, some network features may be affected, such as the overall performance, channel interference, and AP connectivity. In this paper, we propose a new adaptive channel allocation algorithm for a multi-radio multi-channel wireless mesh network. The algorithm is aimed to minimize the number of channel reassignments while maximizing the performance under practical constraints. The algorithm defines a decision function for the channel reassignments. The decision function aims to minimize the traffic around the GW. Whenever the traffic changes in the wireless mesh network, the decision function decides which channel radio reassignment should be done. We demonstrated the effectiveness of our algorithm through extensive simulations using Network Simulator 2 (NS-2).

Adaptive channel allocation for spectrum sharing based on ACO algorithm

Adaptive channel allocation for spectrum sharing based on ACO algorithm

Fuzzy inference based adaptive channel allocation for IEEE 802.22 compliant smart grid network

Smart grid (SG) uses bi-directional communication among the components of power system all the way from generation down to power consumers. The basic architecture of SG is comprised of multi-layered network with applications that have diverse quality-of-service (QoS) requirements. Integrating cognitive radio (CR) in SG network results in efficient handling of differential data amounts and latencies, while meeting stringent reliability requirements through cognition in the system parameters and bandwidth adaptation. Meeting data rate, reliability, and latency demands of various smart grid applications pose greater challenge in presence of uncertainty factors e.g. spectrum sensing errors, channel unavailability with desired parameters and signal-to-noise ratio etc. Spectrum sensing is the fundamental requirement of any CR-based network and this is required to identify available idle channels. Existing channel selection algorithms do not consider exact SG communication requirements simultaneously to allocate a suitable channel in accordance with some wireless standards. In this paper, we propose a technique which selects the optimum channel for the particular application, from a pool of available channels which best meets the QoS requirements. For the optimum channel selection, fuzzy inference system optimization technique is used. The physical layer is based on mode-4 of IEEE 802.22 standard, wireless regional area network. A novel approach is also proposed for allocation of channel when desired channel in not available. This approach is based on selecting an alternate channel, in event of unavailability of desired channel, with parameters that closely match with the desired requirements in order to reduce re-transmission probability. The proposed technique outperforms existing algorithms in terms of achieved latency by a minimum of 200%, and throughput by approximately 50%.

Performance Evaluation of an Adaptive Channel Allocation Technique for Cognitive Wireless Sensor Networks

This paper deals with a cognitive overlay IEEE 802.15.4e wireless sensor network relying on a low-complexity spectrum sensing technique. In particular, the paper critically compares two spectrum sensing schemes with the aim of identifying the most appropriate solution in terms of performance and implementation complexity. Furthermore, the paper provides an analytical framework to investigate the cognitive overlay IEEE 802.15.4e wireless sensor network behavior in terms of throughput, packet dropping probability, and energy efficiency. The obtained results clearly highlight the good behavior of the proposed approach without burdensome cost or complexity and its advantages for several emerging applications such as those supported by fog computing architecture.

Interference Bound for Local Channel Allocation

A lower bound of the total co-channel interference is proposed for the channel allocation problem when applied to a reduced set of nodes. The rest of the network nodes remain unaffected. This bound is independent of the particular channel allocation algorithm employed and no assumptions are made about the propagation model or the deployment scenario. Assuming that the bound is tight to the interference generated by the optimal channel allocation, its computation may help, for example, to estimate the minimum set of nodes for which channel allocation performs nearly-optimal while minimizing node reconfigurations. Another example of usage is the estimation of the minimum number of channels required for a given performance. The tightness of the proposed bound is evaluated through simulations, with a difference lower than 1 % in the conducted simulations. In addition, a sample use case--adaptive local channel allocation--is also provided.

Distributed Adaptive Channel Allocation in Multi-Radio Wireless Sensor Networks

Distributed Adaptive Channel Allocation in Multi-Radio Wireless Sensor Networks

Cooperative weighted-fair control strategy for spectrum self-coexistence in multi-cell WRAN systems

The emerging IEEE 802.22-based Wireless Regional Area Network (WRAN) is the first wireless standard based on the Cognitive Radio (CR) technology. WRAN is designed to offer wireless access services in a large coverage area by allowing Secondary Users (SUs) to opportunistically exploit the under-utilized licensed portion of spectrum that is primarily allocated for TV services. To enable efficient WRAN communications, the operation of a WRAN system should address two types of coexistence problem: incumbent co-existence and self-coexistence. In this paper, we investigate the self-coexistence problem in a multi-cell WRAN system that adopts an exclusive spectrum sharing policy. Specifically, we present an adaptive channel allocation scheme based on a cooperative max–min weighted fair allocation strategy. The proposed scheme is based on a centralized sensing mechanism that identifies the available spectrum opportunities for the WRAN cells. Our scheme dynamically allocates the available spectrum (idle channels) to the WRAN cells based on their expected traffic loads such that the total number of simultaneously admitted SU transmissions in the WRAN system is maximized. The expected traffic load is accurately estimated using a sample mean estimator based on previously monitored traffic in each cell. Simulation results indicate that our scheme is quite robust to traffic estimation error. Compared to reference spectrum allocation schemes, simulation results indicate that the proposed scheme effectively exploits the available spectrum opportunities by increasing the total number of served SU transmissions, which consequently results in a significant enhancement in the overall WRAN system performance.

An Efficient Adaptive Channel Allocation Scheme For Cellular Networks

Cellular networking is providing global information access to users on the move. Handoff algorithms allow the users to move among heterogeneous wireless networks. In this paper, the authors have proposed an efficient Adaptive Channel Allocation Scheme (ACAS) where the number of guard channels is adjusted automatically based on the average handoff blocking rate measured in the past certain period of time. The handoff blocking rate and the new call blocking rates are minimized. It is seen that the proposed scheme is far efficient than the Static Channel Allocation Scheme. The resource utilization of the proposed scheme is more.

A channel reuse strategy with adaptive channel allocation for all-optical WDM networks

The fundamental problems of WDM networks are: (1) high rate of control packet loss and (2) high propagation delay for each (re)transmission. In this paper, we minimize the station randomness to access the control architecture introducing a collisions-free access scheme. We propose a synchronous protocol according which at the end of the propagation delay each station applies a distributed algorithm for packet transmission following the data channel collisions and the receiver collisions avoidance algorithms. We introduce two data transmission stages. The time difference between them is one packet transmission time. At the end of the first stage all data channels are free and can be reused by the remaining data packets during the second stage. The proposed protocol ensures a totally collisions-free performance. The main advantage is that the data channels reuse strategy applied during the second stage provides enhanced transmission probability to the rejected packets during the first stage. This allows the data packets to try retransmission in the same cycle without requiring control packets re-coordination that increases propagation delay. Thus, we achieve large number of data packets transmission, even more than the data channels number, providing throughput improvement and delay reduction, comparing with other studies.

A Novel Distributed Adaptive Channel Allocation Algorithm in Mesh WLAN Network and Its Routing QoS

A Novel Distributed Adaptive Channel Allocation Algorithm in Mesh WLAN Network and Its Routing QoS

A Novel Adaptive Channel Allocation Scheme To Handle Handoffs

Wireless networking is becoming an increasingly important and popular way of providing global information access to users on the move. One of the main challenges for seamless mobility is the availability of simple and robust handoff algorithms, which allow a mobile node to roam among heterogeneous wireless networks. In this paper, the authors devise a scheme, A Novel Adaptive Channel Allocation Scheme (ACAS) where the number of guard channel(s) is adjusted automatically based on the average handoff blocking rate measured in the past certain period of time. The handoff blocking rate is controlled under the designated threshold and the new call blocking rate is minimized. The performance evaluation of the ACAS is done through simulation of nodes. The result shows that the ACAS scheme outperforms the Static Channel Allocation Scheme by controlling a hard constraint on the handoff rejection probability. The proposed scheme achieves the optimal performance by maximizing the resource utilization and adapts itself to changing traffic conditions automatically.