Channel Access Research Articles

Fabrication trueness and internal fit of hybrid abutment crowns fabricated by using additively and subtractively manufactured resins

ObjectivesTo evaluate the fabrication trueness and internal fit of hybrid abutment crowns fabricated by using additively and subtractively manufactured restorative materials. MethodsA maxillary first premolar crown with a screw access channel was designed onto a digitized master titanium base abutment. This file was used to fabricate 40 crowns additively (Crowntec (CT) and VarseoSmile Crown Plus (VS)) or subtractively (Brilliant Crios (BC) and Vita Enamic (EN)) (n = 10). Crowns were digitized with an intraoral scanner and root mean square method was used to evaluate fabrication trueness. Master abutment and the crowns when seated on the abutment were also digitized with the same intraoral scanner and triple scan method was used to evaluate internal fit. Data were analyzed either with 1-way ANOVA (surface deviations) or Kruskal-Wallis (internal fit) tests (α= 0.05). ResultsCT had the highest overall, external, and marginal surface deviations (P≤.030), whereas BC had the lowest external (P≤.001) and VS and EN had the lowest marginal surface deviations (P≤.007). BC had the highest intaglio surface deviations (P<.001). BC and EN had higher average gap values CT and VS (P≤.006); however, the differences within additively and subtractively manufactured materials were nonsignificant (P≥.858). ConclusionsOne of the tested additively manufactured resins (CT) resulted in mostly lower trueness than that of other materials. However, deviations at the intaglio and marginal surfaces were generally small and the maximum mean difference among test groups when average gap was considered was 17.4 µm. Therefore, clinical fit of hybrid abutment crowns fabricated with tested materials may be similar.

Interactions of coumarin and amine ligands with six cytochrome P450 2D6 allelic variants: Molecular docking

Human CYP2D6 contributes extensively to the biotransformation of important therapeutic drugs. CYP2D6 substrate and inhibitor specificity may be affected by genetic polymorphism. This study aimed to characterize interactions of three typical ligands, 3-cyano-7-ethoxycoumarin, fluoxetine and terbinafine with six CYP2D6 variants using molecular docking simulations. The compounds were docked individually to the CYP2D6 models based on published crystal structure (PDB code: 3TBG). All ligands bound within the active site pocket near the heme. Binding involved residues found in critical secondary structures that formed the active site boundary: B-C loop, F helix, F-G loop, β-1 strands and I helix. Twenty-five amino acids were involved in the binding, and all were located in the known substrate recognition sites. Hydrophobic bonds involving phenylalanine (Phe120, Phe384) dominated CEC binding whereas electrostatic bonds between the protonated nitrogen with acidic residues (Glu216, Glu222, Asp301) dominated in binding of fluoxetine and terbinafine. Collectively, the subtle structural changes in the active site and substrate access channels induced by the mutations in the variants contributed to differential ligand docking poses. This study has provided insights into important molecular properties for CYP2D6 catalysis and inhibition, and formed basis for further exploration of structural determinants for potency and specificity of CYP2D6 ligands.

Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks

We consider the problem of dynamic spectrum access (DSA) in cognitive wireless networks, consisting of primary users (PUs) and secondary users (SUs), where only partial observations are available at the SUs due to narrowband sensing and transmissions. The network operates in a time-slotted regime, where the traffic patterns of the PUs are modeled as finite-memory Markov chains, that are unknown to the SUs. Since observations are partial, then both channel sensing and access actions affect the throughput. Focusing on the case in which there is a single SU, our objective is to maximize the SU’s long-term throughput. To that aim, we develop a novel algorithm that learns <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">both</i> access and sensing policies via deep Q-learning, dubbed Double Deep Q-network for Sensing and Access (DDQSA). To the best of our knowledge, this is the first work that jointly optimizes both sensing and access policies for DSA via deep Q-learning. Next, we consider wireless networks with access policy which implements a fixed channel hopping dynamics, for which we analytically determine the optimal SU sensing and access policy and its associated throughput. Then, we demonstrate that indeed, the proposed DDQSA algorithm can achieve near-optimal performance for the considered network. Our results show that the proposed DDQSA algorithm learns a policy that implements both sensing and channel access, which significantly outperforms existing approaches, and can achieve the optimal performance in certain scenarios.

IoT-MAC: A Channel Access Mechanism for IoT Smart Environment

IoT-MAC: A Channel Access Mechanism for IoT Smart Environment

Channel Access Optimization in Unlicensed Spectrum for Downlink URLLC: Centralized and Federated DRL Approaches

The sixth-generation (6G) communication research is currently in the early stage, where ultra-reliable low-latency communication (URLLC) is still an important service as in the fifth-generation (5G). Since 6G networks are expected to provide even higher levels of massive connectivity, high spectrum efficiency, high reliability, and low latency than 5G communication, it would confront much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. However, how to achieve URLLC requirements in NR-U networks is extremely challenging due to interference and collisions among multiple radio access technologies (e.g., WiFi). Therefore, it is urgent to design efficient spectrum-sharing algorithms to support URLLC in emerging 6G networks. In this paper, we develop novel centralized deep reinforcement learning (CDRL) and federated DRL (FDRL) frameworks, respectively, to optimize the downlink URLLC transmission in NR-U and WiFi coexistence systems through dynamically adjusting energy detection (ED) thresholds. Our results show that both CDRL and FDRL approaches have improved the reliability of the NR-U system significantly, but the CDRL framework has sacrificed the reliability of the WiFi system. To guarantee the reliability of the WiFi system while improving the NR-U system, we take fairness into account by redesigning the reward of CDRL.

Zero-Error Communication Over Adversarial MACs

We consider zero-error communication over a two-transmitter deterministic adversarial multiple access channel (MAC) governed by an adversary who has access to the transmissions of both senders (hence called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">omniscient</i> ) and aims to maliciously corrupt the communication. None of the encoders, jammer and decoder is allowed to randomize using private or public randomness. This enforces a combinatorial nature of the problem. Our model covers a large family of channels studied in the literature, including all deterministic discrete memoryless noisy or noiseless MACs. In this work, given an arbitrary two-transmitter deterministic omniscient adversarial MAC, we characterize when the capacity region 1) has nonempty interior (in particular, is two-dimensional); 2) consists of two line segments (in particular, has empty interior); 3) consists of one line segment (in particular, is one-dimensional); 4) or only contains (0, 0) (in particular, is zero-dimensional). This extends a recent result by Wang, Budkuley, Bogdanov and Jaggi (2019) from the point-to-point setting to the multiple access setting. Indeed, our converse arguments build upon their generalized Plotkin bound and involve delicate case analysis. One of the technical challenges is to take care of both “joint confusability” and “marginal confusability”. In particular, the treatment of marginal confusability does <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> follow from the point-to-point results by Wang et al. Our achievability results follow from random coding with expurgation.

Collaborative Semantic Communication for Edge Inference

We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object from different angles and locations, which are then used jointly to retrieve similar images at the edge server over a shared multiple access channel (MAC). We propose two novel deep learning-based joint source and channel coding (JSCC) schemes for the task over both additive white Gaussian noise (AWGN) and Rayleigh slow fading channels, with the aim of maximizing the retrieval accuracy under a total bandwidth constraint. The proposed schemes are evaluated on a wide range of channel signal-to-noise ratios (SNRs), and shown to outperform the single-device JSCC and the separation-based multiple-access benchmarks. We also propose a channel state information-aware JSCC scheme with attention modules to enable our method to adapt to varying channel conditions.

Unsourced Multiple Access With Random User Activity

To account for the massive uncoordinated random access scenario, which is relevant for the Internet of Things, Polyanskiy (2017) proposed a novel formulation of the multiple-access problem, commonly referred to as unsourced multiple access, where all users employ a common codebook and the receiver decodes up to a permutation of the messages. In this paper, we extend this seminal work to the case where the number of active users is random and unknown <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> . We define a random-access code accounting for both misdetection (MD) and false alarm (FA), and derive a random-coding achievability bound for the Gaussian multiple access channel. Our bound captures the fundamental trade-off between MD and FA probabilities. It suggests that the lack of knowledge of the number of active users entails a small penalty in energy efficiency when the target MD and FA probabilities are high. However, as the target MD and FA probabilities decrease, the energy efficiency penalty becomes more significant. For example, in a typical IoT scenario with framelength 19200 complex channel uses and 25–300 active users in average, the required energy per bit to achieve both MD and FA probabilities below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , predicted by our bound, is only 0.5–0.7 dB higher than that predicted by the bound in Polyanskiy (2017) for a known number of active users. This gap increases to 3–4 dB when the target MD probability and/or FA probability is below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . Taking both MD and FA into account, we use our bound to benchmark the energy efficiency of slotted-ALOHA with multi-packet reception, of a decoder that simply treats interference as noise, and of some recently proposed unsourced multiple access schemes. Numerical results suggest that, when the target MD and FA probabilities are high, it is effective to estimate the number of active users, then treat this estimate as the true value, and use a coding scheme that performs well for the case of known number of active users. However, this approach becomes energy inefficient when the requirements on MD and FA probabilities are stringent.

Remote Tracking of Distributed Dynamic Sources Over a Random Access Channel With One-Bit Updates

In this work, we consider a network, where distributed information sources whose states evolve according to a random process transmit their time-varying states to a remote estimator over a shared wireless channel. Each source generates packets in a decentralized manner and employs a slotted random access mechanism to transmit the packets. In particular, we are interested in networks with a large number of low-complexity devices that share low-capacity random access channels. Accordingly, we investigate update strategies for remote tracking of source states that require each update to constitute as few bits as possible. To that end, we develop update strategies requiring only one-bit of information per update that employ a local cancellation strategy. We further analytically compare the performance of the cancellation-enabled update policy to the optimal policy that does not restrict the number of bits for each update, which show that an asymptotic upper bound of the optimality ratio is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{13\sqrt{2}}{12}$</tex-math></inline-formula> . Through simulations, we compare the proposed cancellation-enabled one-bit update policy with zero-wait sampling and threshold-based sampling policies that require more than one-bit of information per update. The comparisons show that the cancellation-enabled update policy at its optimal threshold level outperforms the multi-bit update policies.

Revisiting Dirty Paper Coding to Hybrid Precoding for Massive MIMO Downlink Broadcast Channel

It is well-known that dirty paper coding (DPC)-based transceiver can achieve the sum capacity of a multi-input multi-output (MIMO) broadcast channel. In this paper, we study DPC-based hybrid precoding to maximize the sum-rate of a massive MIMO (mMIMO) downlink broadcast channel, subject to the constant modulus constraint of the analog phase shifter network (PSN). Using the duality between a massive MIMO broadcast channel (mMIMO-BC) and a massive MIMO multiple access channel (mMIMO-MAC), we propose to first optimize the transmitters and the hybrid receiver for the mMIMO-MAC, before obtaining the hybrid precoder of mMIMO-BC according to the MAC-BC duality. The algorithm is also extended for mMIMO orthogonal frequency division multiplexing (OFDM) systems over broadband frequency-selectivity channels. Numerical results show that the proposed DPC-based hybrid precoding can significantly outperform the existing linear counterpart, especially in the case of numerous users. In simulations based on the 5G new radio (5G-NR) standard, the relative gain of the proposed scheme appears prominent, which suggests that the DPC can be a worthy candidate for the future generation of wireless communications.

Constrained Riemannian Noncoherent Constellations for the MIMO Multiple Access Channel

We consider the design of multiuser constellations for a multiple access channel (MAC) with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> users, with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> antennas each, that transmit simultaneously to a receiver equipped with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> antennas through a Rayleigh block-fading channel when no channel state information (CSI) is available to either the transmitter or the receiver. In full-diversity scenarios where the coherence time is at least <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> ≥ ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> + 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> , the proposed constellation design criterion is based on the asymptotic expression of the multiuser pairwise error probability (PEP) derived by Brehler and Varanasi (2001). In non-full diversity scenarios, for which the previous PEP expression is no longer valid, the proposed design criteria are based on proxies of the PEP recently proposed by Ngo and Yang (2021). Although both the PEP expression and its bounds or proxies were previously considered intractable for optimization, in this work we derive their respective unconstrained gradients. These gradients are in turn used in the optimization of the proposed cost functions in different Riemannian manifolds representing different power constraints. In particular, in addition to the standard unitary space-time modulation (USTM) leading to optimization on the Grassmann manifold, we consider a more relaxed percodeword power constraint leading to optimization on the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oblique manifold</i> , and an average power constraint leading to optimization on the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trace manifold</i> . Equipped with these theoretical tools, we design multiuser constellations for the MIMO MAC in full-diversity and non-full-diversity scenarios with state-of-the-art performance in terms of symbol error rate (SER).

Capacity Region of Asynchronous Multiple Access Channels With FTN

This paper derives the capacity region of asynchronous multiple access channel (MAC) with faster-than-Nyquist (FTN) signaling. We first express the capacity region in the frequency domain. Next, we prove that the capacity definition for finite memory MAC can be generalized to infinite memory MAC. The achievable rate region utilizing the finite memory definition in fact achieves the same region calculated in the frequency domain. Our analysis shows that power optimization is necessary to achieve the capacity region for asynchronous MAC and FTN.

DIGITAL FINANCIAL INCLUSION AND INCLUSIVE DEVELOPMENT OF INDIA

With the prospect of reaching billions of new customers, banks and a widening array of non-banks have begun to offer digital financial services for financially excluded and underserved populations, building on the digital approaches that have been used for years to improve access channels for those already served by the formal financial sector. Digital financial services — including those involving the use of mobile phones — have now been launched in more than 80 countries, with some reaching significant scale. Digital financial inclusion involves the deployment of the cost-saving digital means to reach currently financially excluded and underserved populations with a range of formal financial services suited to their needs that are responsibly delivered at a cost affordable to customers and sustainable for providers.

S-Ethyl-Isothiocitrullin-Based Dipeptides and 1,2,4-Oxadiazole Pseudo-Dipeptides: Solid Phase Synthesis and Evaluation as NO Synthase Inhibitors.

We previously reported dipeptidomimetic compounds as inhibitors of neuronal and/or inducible NO synthases (n/iNOS) with significant selectivity against endothelial NOS (eNOS). They were composed of an S-ethylisothiocitrullin-like moiety linked to an extension through a peptide bond or a 1,2,4-oxadiazole link. Here, we developed two further series where the extension size was increased to establish more favorable interactions in the NOS substrate access channel. The extension was introduced on the solid phase by the reductive alkylation of an amino-piperidine moiety or an aminoethyl segment in the case of dipeptide-like and 1,2,4-oxadiazole compounds, respectively, with various benzaldehydes. Compared to the previous series, more potent inhibitors were identified with IC50 in the micromolar to the submicromolar range, with significant selectivity toward nNOS. As expected, most compounds did not inhibit eNOS, and molecular modeling was carried out to characterize the reasons for the selectivity toward nNOS over eNOS. Spectral studies showed that compounds were interacting at the heme active site. Finally, selected inhibitors were found to inhibit intra-cellular iNOS and nNOS expressed in RAW264.7 and INS-1 cells, respectively.

Human cytochrome P450 3A7 binding four copies of its native substrate dehydroepiandrosterone 3-sulfate

Human fetal cytochrome P450 3A7 (CYP3A7) is involved in both xenobiotic metabolism and the estriol biosynthetic pathway. Although much is understood about cytochrome P450 3A4 and its role in adult drug metabolism, CYP3A7 is poorly characterized in terms of its interactions with both categories of substrates. Herein, a crystallizable mutated form of CYP3A7 was saturated with its primary endogenous substrate dehydroepiandrosterone 3-sulfate (DHEA-S) to yield a 2.6Å X-ray structure revealing the unexpected capacity to simultaneously bind four copies of DHEA-S. Two DHEA-S molecules are located in the active site proper, one in a ligand access channel, and one on the hydrophobic F'-G' surface normally embedded in the membrane. While neither DHEA-S binding nor metabolism exhibit cooperative kinetics, the current structure is consistent with cooperativity common to CYP3A enzymes. Overall, this information suggests that mechanism(s) of CYP3A7 interactions with steroidal substrates are complex.

Non-Orthogonal Multiple Access with One-Bit Analog-to-Digital Converters Using Threshold Adaptation.

In digital communication systems featuring high-resolution analog-to-digital converters (ADCs), the utilization of successive interference cancellation and detection can enhance the capacity of a Gaussian multiple access channel (MAC) by combining signals from multiple transmitters in a non-orthogonal manner. Conversely, in systems employing one-bit ADCs, it is exceedingly difficult to eliminate non-orthogonal interference using digital signal processing due to the considerable distortion present in the received signal when employing such ADCs. As a result, the Gaussian MAC does not yield significant capacity gains in such cases. To address this issue, we demonstrate that, under a given deterministic interference, the capacity of a one-bit-quantized channel becomes equivalent to the capacity without interference when an appropriate threshold value is chosen. This finding suggests the potential for indirect interference cancellation in the analog domain, facilitating the proposition of an efficient successive interference cancellation and detection scheme. We analyze the achievable rate of the proposed scheme by deriving the mutual information between the transmitted and received signals at each detection stage. The obtained results indicate that the sum rate of the proposed scheme generally outperforms conventional methods, with the achievable upper bound being twice as high as that of the conventional methods. Additionally, we have developed an optimal transmit power allocation algorithm to maximize the sum rate in fading channels.

Urban electricity governance and the (re)production of heterogeneous electricity constellations in Dar es Salaam

BackgroundElectricity infrastructures in sub-Saharan African cities are characterized by heterogeneous socio-technical constellations, including alternative grid access channels and off-grid systems. These constellations secure access beyond conventional grids but also produce adverse social, environmental, and economic outcomes affecting sustainable energy transition efforts. In fact, interventions aiming to promote energy transitions may be restricted by institutional mechanisms that produce and maintain these heterogeneous constellations. This article explores these institutional mechanisms by focusing on the governance of heterogeneous electricity constellations in Dar es Salaam, Tanzania. It develops a novel framework on governance modalities to understand and explain the logics, mechanisms, and actors that govern different constellations across diverse neighborhoods as well as to unpack how they limit the potential of sustainable energy transitions and offer specific opportunities for them.ResultsThis article is based on a qualitative case study covering three diverse neighborhood types in Dar es Salaam (i.e., low-income, peri-urban, and affluent areas) that reflect heterogeneous user demands. The research draws on interviews with residents and community leaders to understand local modes of coordination, the participatory observation of technical features and user practices, as well as document sources and semi-structured expert interviews to analyze institutional aspects. Our study demonstrates that heterogeneous electricity constellations in Dar es Salaam are governed by the place-based interplay of four governance modalities: hierarchical, market-based, network-based, and managerial governance. Based on this conceptualization, we identified critical barriers for interventions toward urban energy transitions in the context of infrastructural heterogeneity, namely, conflicting logics that shape conventional grid services, complex and fragmented actor constellations, and diverging, place-based interests among various actors, including different state actors.ConclusionsOur study indicates that heterogeneous urban infrastructure constellations are not merely a response to the considerable socio-spatial inequalities within Southern cities. Rather, their prevailing importance and (re)production must be understood as resulting from the interplay of various governance modalities. The study contributes to debates on urban energy transitions in sub-Sahara Africa by explaining the institutional complexity associated with infrastructural heterogeneity, which can restrict interventions aiming to improve and universalize service provision through heterogeneous urban electricity constellations.

Station Grouping Mechanism using Machine Learning Approach for IEEE 802.11ah

IEEE 802.11ah, also known as Wi-Fi HaLow, improves the scalability of the Internet of Things (IoT) by connecting a large number of low-power devices. IEEE 802.11ah has developed a novel feature called Restricted Access Window (RAW) that aims to address one of the main problems of the IoT: excessive channel contention in large sensor networks. All stations can access the channel from the same group when using RAW, which allows the Access Point (AP) to split stations into different groups. Existing station grouping schemes support only the homogeneous traffic scenarios, having the same data transmission interval, packet size, and Modulation and Coding Scheme (MCS). In this paper, we highlight two contributions to solving this problem. The first is a Machine Learning (ML) based model that predicts RAW configuration parameters and performance under specific network conditions. It predicts accurately and is very quick to train. Second, a technique for a channel access mechanism that uses an ML model to determine the optimal RAW station grouping configuration for dynamic traffic conditions and heterogeneous stations. We compare our ML-based model with the existing state-of-the-art solutions. We calculated the performance of our proposed model and compared it to traditional 802.11ah and the Traffic Adaptive RAW Optimization Algorithm (TAROA). The results show that our proposed model outperforms a significant improvement in terms of throughput, packet received ratio, delay, and execution time.

Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars

The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g., ETSI) provide requirements in terms of maximum transmit power but do no mandate specific radar waveform parameters nor channel access scheme policies. Techniques for interference mitigation are thus becoming very important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. In our previous work, we have shown that organizing the radar band into time-frequency resources that do not interfere with each other vastly reduces the amount of interference by facilitating band sharing. In this paper, a metaheuristic is presented to find the optimal resource sharing between radars, knowing their relative positions and thereby the line-of-sight and non-line-of-sight interference risks during a realistic scenario. The metaheuristic aims at optimally minimizing interference while minimizing the number of resource changes that radars have to make. It is a centralized approach where everything about the system is known (e.g., the past and future positions of the vehicles). This and the high computational load induce that this algorithm is not meant to be used in real-time. However, the metaheuristic approach can be extremely useful for finding near optimal solutions in simulations, allowing for the extraction of efficient patterns, or as data generation for machine learning.

A Modified DSR Protocol Using Deep Reinforced Learning for MANETS

Wireless Networks like Mobile Adhoc Networks (MANETs) have been under extensive research over the past few years with congestion control as one of the most important features to ensure efficient and fair sharing of network resources among users. Machine learning (ML) has achieved a success rate in addressing large-scale and complex problems and researchers have begun to shift their attention from the rule-based method to an ML-based approach to handle the complex needs of future networks where conventional rule-based approaches tend to become inefficient and ineffective. To handle the problems in congestion control, precise notification should be generated as the backpressure transmission process. Although backpressure-based routing algorithms give optimal throughput, they typically have poor delay performance under moderate loads. This may be because packets are being sent over longer routes unnecessarily. Furthermore, the existing backpressure-based optimisation algorithms require every node to compute differential backlogs for every destination queue with the corresponding destination queue at every adjacent node. The proposed algorithm proves to be a cross-layer protocol for wireless MANET that generalises the channel access management and routing process which includes traffic management, connection maintenance and distributed scheduling for concurrent transmission. The joint congestion control method with scheduling algorithm has enhanced active radio communication network by interchanging scheduling schema with adaptation modelling and also the optimum congestion dominance and flow control model is being designed using deep reinforced learning.