Idle Channel Research Articles

Physical design for microfluidic biochips considering actual volume management and channel storage

In recent years, microfluidic biochips have been widely applied in various fields of human society. The optimization design of system-architecture based on continuous-flow microfluidic biochips has been widely studied. However, most previous work was based on the traditional chip architecture with dedicated storage, which not only limits the performance of biochips but also increases their manufacturing costs. In order to improve the execution efficiency and reduce the manufacturing cost, a distributed channel-storage architecture can be used to temporarily cache intermediate fluids in idle flow channels. Under this architecture, careful consideration of the volume management of the fluid to be cached is a prerequisite for ensuring the reliability of bioassay results. However, the existing work has not considered the volume management of the fluid to be cached in detail. This may cause the volume of the fluid to not match the capacity of the storage channel, which can contaminate other fluids and lead to incorrect bioassay results or increase the manufacturing cost of biochips due to long storage channels. In this paper, we propose a physical design method for microfluidic biochips that considers the actual volume of fluid while utilizing distributed channel storage. We address this problem by taking a placement and routing co-design strategy throughout the iterative process of the simulated annealing algorithm. Experimental results under multiple benchmarks show that the proposed method can effectively reduce the completion time of bioassays, minimize the flow path length, and decrease the number of intersections.

Improved spectrum prediction model for cognitive radio networks using hybrid deep learning technique

Cognitive Radio (CR) technology has been highlighted as one of the most likely answers to the issue of spectrum shortage with the rise of fifth generation and beyond communication. Secondary users (SUs) in cognitive radio networks (CRN) must continuously monitor the spectrum to forecast channel occupancy by primary users (PUs) based on fundamental factors, such as location, time, and RF band. A hybrid deep learning model called LSTM-MLP (Long Short-Term Memory-Multilayer Perceptron) is proposed to improve idle channel prediction probability thus reducing the overall sensing time by cognitive users during spectrum sensing. Performance evaluation for the proposed model is done in terms of prediction error and efficiency, the GSM-900 spectrum dataset demonstrates that LSTM-MLP performs better in terms of improved prediction accuracy compared to existing state-of-art prediction techniques.

When DSRC Meets With C-V2X: How to Jointly Predict and Select Idle Channels?

When DSRC Meets With C-V2X: How to Jointly Predict and Select Idle Channels?

An incentive-based dynamic energy efficient spectrum allocation for cognitive radio networks

Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user’s protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms.

Performance analysis of distributed coordination function with early collision detection in In-band Full-duplex wireless networks

In-band Full-duplex (IB-FD) wireless has the potential to double the spectral efficiency offering its innate capability to resolve conventional problems emerge owing to the imperfect medium access control (MAC) in In-band half-duplex (IB-HD) wireless networks. IEEE 802.11 distributed coordination function (DCF), a binary slotted exponential back-off with carrier sense multiple access scheme, has been used extensively in literature as the fundamental contention resolution technique to devise MAC protocols for IB-FD wireless networks. Unlike IB-HD, an IB-FD network can contain two simultaneously active transmission links within a single collision domain. This phenomenon invalidates the applicability of existing IB-HD models of the DCF to compute its performance (i.e., saturation throughput) accurately in IB-FD wireless networks. We have proposed two MAC protocols for two types of network scenarios taking early collision detection capability of an IB-FD wireless transceiver into consideration. This paper also presents a distinct analytical model to quantify the performance of the DCF in the context of IB-FD wireless assuming finite number of stations and idle channel conditions. We have applied the concept of primary–secondary transmission links and, thoroughly derived the probability that a station transmits either as primary or secondary in a randomly chosen time-slot taking secondary transmitter’s back-off counter into account. We have also investigated the effect of inter-station interference free IB-FD link on the performance of the DCF. Our protocol, when applied to network without hidden stations, has obtained the highest throughput gain of 1.59, 1.61, 1.68 while the DCF is configured with CWmin of 16, 32, 64, respectively than IEEE 802.11 basic access scheme. We have observed increased throughput gain with hidden stations, thanks to inter-station interference free transmission region, and achieved 2.12, 2.14, 2.16 times higher throughput, respectively than that of IEEE rts-cts access scheme.

Age-Dependent Distributed MAC for Ultra-Dense Wireless Networks

We consider an ultra-dense wireless network with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$</tex-math> </inline-formula> channels and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M = N$</tex-math> </inline-formula> devices. Messages with fresh information are generated at each device according to a random process and need to be transmitted to an access point. The value of a message decreases as it ages, so each device searches for an idle channel to transmit the message as soon as it can. However, each channel probing is associated with a fixed cost (energy), so a device needs to adapt its probing rate based on the “age” of the message. At each device, the design of the optimal probing strategy can be formulated as an infinite horizon Markov Decision Process (MDP) where the devices compete with each other to find idle channels. While it is natural to view the system as a Bayesian game, it is often intractable to analyze such a system. Thus, we use the Mean Field Game (MFG) approach to analyze the system in a large-system regime, where the number of devices is very large, to understand the structure of the problem and to find efficient probing strategies. We present an analysis based on the MFG perspective. We begin by characterizing the space of valid policies and use this to show the existence of a Mean Field Nash Equilibrium (MFNE) in a constrained set for any general increasing cost functions with diminishing rewards. Further we provide an algorithm for computing the equilibrium for any given device, and the corresponding age-dependent channel probing policy.

Diffusion limit of a modified Erlang-B system with sensing time of secondary users.

We analyze a queueing model for cognitive wireless networks using the asymptotic-diffusion method (Moiseev et al. (2020); Nazarov et al. (2020)). Cognitive wireless is a technology that resolves radio spectrum shortages by allowing secondary users (SUs, unlicensed users) to occupy channels initially assigned to primary users (PUs, licensed users). SUs need to sense the channel availability upon arrival. After sensing, an SU can transmit if there is an idle channel; otherwise, the SU must continue sensing. We consider the situation where SUs may be interrupted by the arrivals of PUs when all channels are occupied. We derive a diffusion limit for the queueing model when the SUs' mean sensing time tends to infinity. The diffusion limit leads to an approximate probability distribution of the number of sensing SUs. Finally, we derive a necessary stability condition which turns out to be consistent with the sufficient condition obtained in previous research.

A novel spectrum handoff switching decision scheme for improved performance of secondary users in cognitive radio network

SummarySpectrum handoff (SH) in the cognitive radio network (CRN) is considered as a key challenging area to enhance the performance of secondary users (SUs) in CRN. If the primary user is detected, the SU may pause and stay on the same channel or may perform SH to another idle channel. An accurate and precise handoff decision improves the overall throughput and quality of experience of end‐users. In this paper, we introduce a new SH algorithm and continuous short‐sensing strategy to improve the overall throughput of SUs. In addition, we have derived the minimum length of the target channel sequence based on network‐specific parameters like desired call dropping probability. Further, an optimum channel search time is obtained to minimize the handoff delay. The simulation result shows that the proposed scheme improves the overall throughput of CRN, and the mean opinion score of different video applications increases by 10%, 4.6%, and 1% for rapid motion, gentle walk, and slight motion types of video applications. In the case of VoIP applications, the maximum simultaneous call is improved by 2 times in the case of G.711, 1.72 times in the case of G.729, and 1.66 times in the case of iLBC.

OFDM Spectrum Sensing Analysis and Implementation Method

OFDM is a carrier modulation technology, which realizes the parallel transmission of high-speed serial data through frequency division multiplexing. OFDM has an excellent ability to resist multipath fading and is commonly used in wireless communication transmission technology. Spectrum sensing technology is a technology that allows cognitive users to accurately obtain the authorized spectrum usage in wireless networks through signal detection and other processing methods. It is the basis for effectively using different idle channels in different time domains. This paper mainly discusses the OFDM technology, deduces the algorithm of the implementation principle and mathematical characteristics of OFDM, analyses its spectrum characteristics and performance advantages, focuses on the principle and method of OFDM spectrum sensing based on spectrum sensing technology, combs the characteristics of several existing commonly used OFDM spectrum sensing methods, and analyses the implementation cost, hardware requirements, transmission performance The performances of interference factors and other aspects are sorted out, the advantages and disadvantages of several existing OFDM spectrum sensing methods are compared, their respective applicable environments are compared, and their possible problems in the future development prospect of OFDM technology and the direction that needs further research and improvement are put forward.

Hybrid load-balancing algorithm for distributed fog computing in internet of things environment

Fog computing is a novel idea created by Cisco that provides the same capabilities as cloud computing but close to objects to improve performance, such as by minimizing latency and reaction time. Packet failure can happen on a single fog server across a large number of messages from internet of things (IoT) sensors due to several variables, including inadequate bandwidth and server queue capacity. In this paper, a fog-to-server architecture based on the IoT is proposed to solve the problem of packet loss in fog and servers using hybrid load balancing and a distributed environment. The proposed methodology is based on hybrid load balancing with least connection and weighted round robin algorithms combined together in fog nodes to take into consideration the load and time to distribute requests to the active servers. The results show the proposed system improved network evaluation parameters such as total response time of 131.48 ms, total packet loss rate of 15.670%, average total channel idle of 99.55%, total channel utilization of 77.44%, average file transfer protocol (FTP) file transfer speed (256 KB to 15 MB files) of 260.77 KB/sec, and average time (256 KB to 15 MB) of 19.27 sec.

Fuzzy ELM-based optimal spectrum sensing in CR-IoT network

In the Internet of Things (IoT), spectral efficiency is a major challenge since many sensors and devices are placed widely throughout the network. Spectrum scarcity issues have been a concern for the Internet of Things since its inception. The use of Cognitive Radio (CR) technology is thought to be a potential remedy. Two of the problems that CR presents in the IoT are quick response times and efficient spectrum detection even at low signal-to-noise ratios. In order to address the spectrum scarcity issue, spectrum prediction is a key task of CR. As a result of spectrum prediction, sensor and decision-making delays are reduced, resulting in fewer collisions between primary and secondary users. To overcome this problem, an innovative GSCI-FELM technique has been proposed. The ultimate aim of the proposed method is to identify the spectrum holes and allocate the best suitable channel to the SU-IoT device. The Global Channel State Information (GSCI) estimation is first used to identify the spectrum holes locally at the SU-IoT devices, which solves the PU identification issue by converting it into an Idle Channel State (IDC) or Busy Channel State (BCS). Eight different features are extracted and given to Fuzzy ELM for classification. The secondary users are given access to the available node once it has been identified. According to the findings, the proposed technique is effective in predicting the spectrum availability of the various nodes in the IoT spectrum network. The accuracy of the proposed FELM is 5.76%, 2.96%, 7.4%, and 1.85% better than LSTM-RNN, STFT-CNN, SVM and SHAE respectively.

Estimation of Traffic Influence on Energy Saving at GSM Channels with Reallocation

This paper presents an innovative procedure for base station emission power calculation in the case of traffic channels reallocation implementation in GSM mobile systems. The main idea of active traffic channels reallocation is to place the connections which require higher emission power due to higher mutual distance between base station and mobile station to the first frequency carrier because these channels have to be always in active mode due to the applied discipline of traffic service in GSM mobile systems. The principle of connections reallocation on the base of distance values sequence determination is novelty comparing to the much more applied principle of connections arrangement according to the propagation conditions. Power saving as the result of this method usage is compared to the necessary power when the sequential hunting with homing method of idle channel looking for is applied. Two special cases are analyzed: 1. the case of low offered traffic (when not more than 6 traffic channels are busy on average) and the case when offered traffic is increased. It is proved that the achieved relative energy saving when the offered traffic is increased over 6Erl rapidly grows at first and then begins to fall. It is important that the absolute value of power saving continuously increases when the offered traffic is increased. One typical example of base station emission power save daily profile is presented on the base of the obtained results.

Intelligent Dynamic Spectrum Allocation in MEC-Enabled Cognitive Networks: A Multiagent Reinforcement Learning Approach

Making effective use of scarce spectrum resources, along with efficient computational performance, is one of the key challenges for future wireless networks. To tackle this issue, in this paper, we focus on the intelligent dynamic spectrum allocation (DSA) in a mobile edge computing (MEC) enabled cognitive network. And our objective is to optimize the spectrum utilization and load balance among idle channels. Since users can only acquire part of environment information in a decentralized way, we model such a problem as decentralized partially observed Markov decision process (Dec-POMDP) and design the corresponding evaluating metric to encourage users sense and access spectrum properly. Then, we propose a QMIX-based DSA method with centralized training decentralized execution (CTDE) structure to tackle it. In the training phase, the users offload the computational tasks to the MEC server to obtain the optimal distributed DSA strategies, through which the users select the optimal channel locally in the execution phase. Simulation results show that, using the proposed algorithm, users can independently capture spectrum holes, and hence improve the spectrum utilization while balancing the load on available channels.

Measurement of the Quantum Efficiency of Analog Detectors in the Parametric Down-Conversion Field

Approaches to the standard-free calibration of the quantum efficiency of a wide class of analog detectors based on the measurement of statistical characteristics of fields generated by parametric down-conversion are analyzed. General expressions are obtained for the noise reduction factor of the difference photocurrent and for the covariance of photocurrents in the signal and idler channels that take into account the possibility of strong fluctuations in the amplitudes of single-photon response functions of the detectors used. It is shown that the measurement of the noise reduction factor of the difference photocurrent using detectors that cannot operate in the photon counting mode is on its own insufficient to directly characterize the level of two-mode squeezing in the down-conversion field and to determine the quantum efficiency of photosensitive elements without additional calibration procedures. A method to determine the quantum efficiency of such detectors based on measuring the dependence of the normalized covariance of photocurrents on the parametric gain is proposed.

Joint bandwidth and power resource allocation technique in multi‐carrier non‐orthogonal multiple access‐based cognitive Internet of Things networks

AbstractThe recent development of the Internet of Things (IoT) devices offers an intelligent concept for integrating massive number of interconnected wireless devices. However, supporting such massive number of IoT connections (hundreds of billions) requires an efficient and dynamic spectrum access strategy. Accordingly, the integration of non‐orthogonal multiple access (NOMA) in the cognitive radio (CR) systems, referred to as a multi‐carrier NOMA CR‐enabled system, is envisioned as a potential spectrum‐efficient networking paradigm that can support massive energy‐efficient IoT connectivity in B5G networks while maintaining the quality of services (QoS) of the IoT devices. However, the power consumption issue of the multi‐carrier NOMA CR‐based system becomes as one of key challenges, especially when considering the expected massive connectivity. Accordingly, several power‐aware optimization frameworks have been considered to address this issue. While most of the existing CR‐based multi‐carrier NOMA power allocation mechanisms only optimize the allocated per‐user power, this article proposes a novel joint bandwidth and power allocation problem over each available idle channel with the aim of minimizing the overall transmission power under a set of QoS constrains. Specifically, the bandwidth of each channel is adaptively subdivided into two variable‐width sub‐channels, each is capable to serve a group of CR users using power‐domain NOMA. This novel design is formulated as a joint optimization problem that is shown to be a non‐convex. Therefore, an iterative algorithm with a second‐order cone programming is deployed to handle the non‐convexity of the problem, and thus, determine the solution of it. Simulation results reveal that the developed variable bandwidth adaptive power allocation mechanism outperforms the conventional equal sub‐channel allocation in terms of the required transmission power, which significantly improves the energy efficiency in the system.

SEE‐MAC: Spectrum and energy efficient‐medium access control protocol for Internet of Things

SummaryIn this paper, we have devised a method for spectrum‐and energy‐efficient wireless communication systems for the Internet of Things (IoT) devices that require continuous uninterrupted power for their operation. However, the potential application of the proposed scheme is vast and is relevant to any wireless communication systems deployed for the next generation connected systems that face challenges in spectrum availability and battery charging of the devices. To fulfill the demand of wireless devices, such as sensor nodes deployed in the IoT, we have proposed a spectral‐and energy‐efficient‐medium access control (SEE‐MAC) protocol with a radio‐frequency energy harvesting (EH) facility. The proposed protocol has integrated wireless power harvesting with cognitive sensor nodes to provide both the energy‐ and spectrum‐efficient wireless communication system. In the proposed SEE‐MAC, the cognitive sensor nodes take the advantage of both the idle and active licensed channels of the primary network for data transmission and EH. The proposed system identifies the active licensed channel of the network to be allotted to each cognitive sensor node for EH and idle channel recognition for data transmission. The mathematical analysis has also been performed representing allocation of primary user channels for the EH and data transmission. The closed‐form expressions obtained along with simulation results have illustrated the amount of harvested energy, throughput, and energy efficiency achieved by the cognitive sensor nodes of the network.

“Advancing Secretly by an Unknown Path”: A Reinforcement Learning-Based Hidden Strategy for Combating Intelligent Reactive Jammer

This letter investigates the problem of combating the intelligent reactive jammer through a deep reinforcement learning (DRL) based hidden strategy. Existing anti-jamming researches always assume a capacity-limited adversary which is non-reactive or only launches attacks while detecting activities of the transmitter. In this letter, we consider an intelligent reactive jammer that releases track jamming once activities of the transmitter are detected, otherwise it launches indiscriminate jamming (e.g., sweep or comb jamming). A DRL-based hidden strategy is proposed to resist this powerful jamming via adjusting power and accessing idle channels simultaneously. Moreover, the parallel policy network and individual reward function are introduced to enhance the anti-jamming performance. Simulation results confirm that the proposed algorithm can significantly elude the jammer’s detection and achieve dynamic spectrum access in a dynamic and unknown environment.

Modified Erlang Loss System for Cognitive Wireless Networks

This paper considers a modified Erlang loss system for cognitive wireless networks and related applications. A primary user has pre-emptive priority over secondary users, and the primary customer is lost if upon arrival all the channels are used by other primary users. Secondary users cognitively use idle channels, and they can stay (either in an infinite buffer or in an orbit) in cases where idle channels are not available upon arrival or they are interrupted by primary users. While the infinite buffer model represents the case with zero sensing time, the infinite orbit model represents the case with positive sensing time. We obtain an explicit stability condition for the cases where arrival processes of primary users and secondary users follow Poisson processes, and their service times follow two distinct arbitrary distributions. The stability condition is insensitive to the service time distributions and implies the maximal throughout of secondary users. Moreover, we extend the stability analysis to the system with outgoing calls. For a special case of exponential service time distributions, we analyze the buffered system in depth to show the effect of parameters on the delay performance and the mean number of interruptions of secondary users. Our simulations for distributions rather than exponential reveal that the mean number of terminations for secondary users is less sensitive to the service time distribution of primary users.

Reinforcement-Learning-Based Dynamic Spectrum Access for Software-Defined Cognitive Industrial Internet of Things

The cognitive industrial Internet of Things (CIIoT) can improve transmission performance by utilizing the spectrum licensed to a primary user (PU), providing that the normal communication of the PU is not disturbed. However, the traditional spectrum access schemes for the CIIoT are difficult to adapt to the various communication environments. In this article, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning-based dynamic spectrum access is proposed for the CIIoT to intelligently utilize the spectrum resources in three access scenarios: orthogonal multiple access (OMA), underlay spectrum access, and nonorthogonal multiple access (NOMA). In the OMA scheme, the CIIoT learns to access the idle channels to avoid distributing the PUs, but its communication continuity cannot be guaranteed when most of the channels are occupied by the PUs. In the underlay scheme, the CIIoT learns to utilize the busy channels to ensure the communication continuity by limiting its transmit power within the tolerance of the PU. However, the interference to the PU cannot be eliminated, which will decrease the PU’s throughput. In the NOMA scheme, however, the CIIoT can utilize the busy channels by canceling the interference to the PU with successive interference cancellation, which will guarantee the transmission performance of both the CIIoT and the PU. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning-based spectrum access algorithm is proposed to improve the transmission performance of the CIIoT in the three schemes. The simulation results have shown the advantages of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning-based NOMA scheme in terms of guaranteeing the throughput of the CIIoT nodes and decreasing the interference to the PUs.

Entanglement-Assisted Joint Monostatic-Bistatic Radars.

With the help of entanglement, we can build quantum sensors with sensitivity better than that of classical sensors. In this paper we propose an entanglement assisted (EA) joint monostatic-bistatic quantum radar scheme, which significantly outperforms corresponding conventional radars. The proposed joint monostatic-bistatic quantum radar is composed of two radars, one having both wideband entangled source and EA detector, and the second one with only an EA detector. The optical phase conjugation (OPC) is applied on the transmitter side, while classical coherent detection schemes are applied in both receivers. The joint monostatic-bistatic integrated EA transmitter is proposed suitable for implementation in LiNbO3 technology. The detection probability of the proposed EA joint target detection scheme outperforms significantly corresponding classical, coherent states-based quantum detection, and EA monostatic detection schemes. The proposed EA joint target detection scheme is evaluated by modelling the direct radar return and forward scattering channels as both lossy and noisy Bosonic channels, and assuming that the distribution of entanglement over idler channels is not perfect.