Time Division Multiple Access Research Articles

A comprehensive review of adaptive modulation and coding techniques for spectrum efficiency and interference mitigation in satellite communication

This paper provides a detailed analysis of Advanced Adaptive Modulation and Coding (AMC) techniques in satellite communication and compare it with the conventional frequency reuse system such as Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA), which rely on fixed resource allocation, AMC dynamically modifies modulation and coding rates, optimizing throughput during favorable conditions and ensuring reliability in adverse scenarios. In optimal conditions, spectral efficiency improves by 30-50% due to AMC's adaptability, as governed by Signal to Noise Ratio (SNR) and Bit Error Rate (BER) thresholds. This paper also demonstrates that its integration with machine learning (ML), specifically, deep learning (DL) and deep reinforcement learning (DRL) enables prediction and adaption, which can address challenges such as channel fading and dynamic signal variations in Low Earth Orbit (LEO) satellite network. Traditional methods are outperformed by these data driven approaches for further improving throughput and reliability. While AMC requires sophisticated hardware, complex algorithms, and efficient feedback systems, solutions such as predictive algorithms and hybrid approaches mitigate these challenges. These findings highlight AMC’s transformative role in addressing growing demands for higher data rates, efficient spectrum utilization, and robust communication, positioning it as a cornerstone for the future of satellite communication systems in dynamic environments.

Leveraging PLOAM messaging for environmental temperature mapping in aerial-deployed time-division multiple access PONs

The use of optical access networks with aerial-deployed fiber for deriving maps of environmental temperature is investigated. Telecom operators have thousands of kilometers of deployed fiber to provide last-mile broadband services, which could be leveraged to extract temperature information with no additional cost since data are already available as part of the physical layer operations, administration, and maintenance (PLOAM) traffic. Here, it is shown how this information can be used to develop maps of environmental temperature as a method to complement present weather observation platforms. Preliminary experimental results with a G.984 passive optical network (PON) in operation show the feasibility of the technique.

Learning uplinks and downlinks transmissions in RF-charging IoT networks

This paper considers uplink and downlink transmissions in a network with radio frequency-powered Internet of Things sensing devices. Unlike prior works, for uplinks, these devices use framed slotted Aloha for channel access. Another key distinction is that it considers uplinks and downlinks scheduling over multiple time slots using only causal information. As a result, the energy level of devices is coupled across time slots, where downlink transmissions in a time slot affect their energy and data transfers in future time slots. To this end, this paper proposes the first learning approach that allows a hybrid access point to optimize its power allocation for downlinks and frame size used for uplinks. Similarly, devices learn to optimize (1) their transmission probability and data slot in each uplink frame, and (2) power split ratio, which determines their harvested energy and data rate. The results show our learning approach achieved an average sum rate that is higher than non-learning approaches that employed Aloha, time division multiple access, and round-robin to schedule downlinks or/and uplinks.

Phase-difference carrier frequency estimation of signal bursts with phase shift keying

Due to the rapid development of communication systems with time division multiple access (TDMA) there is a need to estimate the carrier frequency of signal bursts with phase shift keying. This paper presents the carrier frequency estimation of signal bursts with phase shift keying based on previously developed methods of optimal weighting of the difference-phase statistics without significant computational costs. Algorithms of computationally efficient methods for estimating the carrier frequency of packet signals with Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK), as well as with quadrature amplitude digital modulation, using optimal weighting of the difference-phase statistics are presented. A comparative analysis of computationally efficient methods for estimating the carrier frequency of signal bursts with QPSK is performed for various methods using optimal weighting of the difference-phase statistics, based on the results of experimental observations of packet communication signals of an aircraft under various reception conditions. Statistical simulation of estimation algorithms is performed. Estimation results for different signal-to-noise ratios are illustrated. The proposed algorithms for computationally efficient estimation of the carrier frequency of signal bursts with digital modulation using optimal weighting of phase-difference statistics allow to increase the accuracy of measuring the carrier frequency of these signals for different dynamics of the observed aircraft motion, which is of great importance when receiving signals from low earth orbit (LEO) spacecraft. The proposed algorithms for estimating the carrier frequency of signal bursts with phase shift keying are characterized by high computational efficiency and allow to increase the accuracy of estimating the carrier frequency of signal bursts with phase shift keying under different reception conditions at moderate signal-to-noise ratios, which makes it advisable to use the presented algorithms in the equipment of multi-satellite communication systems based on LEO such as Starlink and OneWeb.

Distributed Passive Positioning and Sorting Method for Multi-Network Frequency-Hopping Time Division Multiple Access Signals.

When there are time division multiple access (TDMA) signals with large bandwidth, waveform aliasing, and fast frequency-hopping in space, current methods have difficulty achieving the accurate localization of radiation sources and signal-sorting from multiple network stations. To solve the above problems, a distributed passive positioning and network stations sorting method for broadband frequency-hopping signals based on two-level parameter estimation and joint clustering is proposed in this paper. Firstly, a two-stage filtering structure is designed to achieve control filtering for each frequency point. After narrowing down the parameter estimation range through adaptive threshold detection, the time difference of arrival (TDOA) and the velocity difference of arrival (VDOA) can be obtained via coherent accumulating based on the cross ambiguity function (CAF). Then, a multi-station positioning method based on the TDOA/VDOA is used to estimate the position of the target. Finally, the distributed joint eigenvectors of the multi-stations are constructed, and the signals belonging to different network stations are effectively classified using the improved K-means method. Numerical simulations indicate that the proposed method has a better positioning and sorting effect in low signal-to-noise (SNR) and low snapshot conditions compared with current methods.

METHOD OF RECOGNITION OF FPV-UAV RADIO SIGNALS FORMED ACCORDING TO CROSSFIRE AND EXPRESSLRS STANDARDS

Unmanned aerial vehicles (UAVs) with the First Person View (FPV) system are the most common type of attack UAVs used at the tactical level by the armed forces of the russian federation. Timely detection of facts of their using by the enemy and countering them are important tasks solved by units of the Ukrainian Defense Forces. An effective countermeasure way for FPV-UAVs is to create interference in their radio control channels frequency bands. To increase the radio suppression range, it is necessary to use interference matched in frequency and structure, the necessary condition for the formation of which is the presence of a priori information about the structure of the radio signal. Analysis of the available information on the construction of enemy FPV-UAVs shows that their radio control and telemetry channels are usually combined, have time division multiple access, the signals are formed according to CrossFire and ExpressLRS standards and have fixed sets of parameter values that can be used as features in recognition. The proposed FPV-UAV radio signal recognition technique is based on decision tree and minimum metric methods and uses a priori information on frequency, time and modulation parameters of radio signals. The combination of the two decision-making methods made it possible to reduce the computational complexity by eliminating operations for determining the parameters of radio signals that do not correspond to the decisions taken at the previous stages, and provides the possibility of adding new time parameters without changing the developed recognition technique. The method consists of a sequence of operations for estimation radio signal parameters, comparing them with known values, making decisions about the type of standard and command- telemetry radio line operation mode. Only one fragment with a duration of one frequency element is sufficient to recognize the FPV-UAV radio signal, which reduces the technical requirements for detection means in which the method can be used.

Analytical and Empirical Insights into Wireless Sensor Network Longevity: The Role MAC Protocols and Adaptive Strategies

Wireless Sensor Networks (WSNs) have become essential in various fields, such as corporate industries, environmental monitoring, military defense, and healthcare, due to their ability to monitor and transmit data from remote locations. However, the battery-powered nature of WSN nodes necessitates a strong emphasis on energy conservation to extend network lifespan. This paper evaluates the impact of different Medium Access Control (MAC) protocols on WSN performance, focusing on energy efficiency and data transmission reliability. We explore various MAC protocols, including contention-based protocols like Carrier Sense Multiple Access (CSMA), schedule-based protocols like Time Division Multiple Access (TDMA), and hybrid protocols that combine both approaches. The methodology integrates analytical modelling, empirical data collection, and expert interviews to comprehensively assess the performance and longevity of WSNs under different MAC protocols. Analytical models were developed to simulate WSN behavior, considering factors such as node density, traffic load, and energy consumption. Empirical data from real-world deployments and experimental setups were analyzed to validate the models and provide practical insights. Expert interviews further highlighted the challenges and considerations in MAC protocol design and implementation. This paper underscores the importance of context-specific MAC protocol selection and the potential of adaptive strategies, including machine learning, to enhance WSN efficiency and reliability. These insights can guide the development of more sustainable and high-performing WSN applications.

Delay sensitive and energy adaptive clustering hierarchy protocol using enhanced agglomerative hierarchy algorithm with time‐controlled jellyfish optimization

AbstractWireless sensor networks have sensing functions which made up of small sensor nodes that are processing and communication capabilities. However, the traditional approach of designating a single sensor node as the cluster head often leads to a faster depletion of its energy resources than another node. So, periodic reassignment of the cluster head role among different sensor nodes is crucial to extend operational lifetime and ensure sustained performance. Therefore, this research proposes an Enhanced Agglomerative Hierarchy Algorithm based on the Low Energy Adaptive Clustering Hierarchy (EAHALEACH) protocol, coupled with an enhanced time‐controlled optimization algorithm, to enhance energy efficiency by selecting the optimal cluster head from a candidate pool. The proposed method enhances energy efficiency by optimally selecting cluster heads, which reduces energy consumption and extends the lifespan of the network. Additionally, a lightweight energy‐aware cluster head rotation algorithm is introduced to efficiently rotate the cluster head role within the sensor node network, minimizing unnecessary data transmissions and extending the network lifetime. A hybrid approach combining Carrier Sense and Time Division Multiple Access (CSTDMA) optimizes packet forwarding by dynamically allocating time slots, reducing collisions and contention to improve packet forwarding efficiency. Comparative analysis with existing techniques demonstrates that the hybrid clustering‐based LEACH protocol achieves superior performance in terms of throughput 4600 bps by 2000 rounds, energy consumption 50 J, latency as 1 ms and network lifetime in 1000th node attains 3500 s. These advancements contribute to prolonged operational efficiency and sustained performance in wireless sensor network deployments.

Full-duplex dynamic TDMA scheme and clock synchronization and compensation method for the multi-user UWOC system.

In this Letter, we propose a full-duplex dynamic time division multiple access (FDD-TDMA) scheme for multi-user underwater wireless optical communication (UWOC). It supports full-duplex communication through wavelength division duplex (WDD) and enhances the system throughput using dynamic time resource allocation. Additionally, a clock synchronization and compensation method is proposed for precise clock synchronization without time-keeping. With the proposed FDD-TDMA scheme and the clock synchronization and compensation method, we establish a two-user UWOC system based on on-off keying (OOK) modulation. Experiments are conducted in a 10 m water pool environment. The experimental results show that the designed system can achieve a data rate of 25 Mbps without error codes and 40 Mbps with a bit error rate (BER) below the forward error correction (FEC) limit. The FDD-TDMA scheme notably improves the system throughput when communication demands among users are variable compared to the conventional time division multiple access (TDMA). Moreover, a reduction in phase deviations from microseconds to ten nanoseconds is achieved by the clock synchronization and compensation method.

Learning efficiency maximization in UAV-and-RIS-aided mobile edge learning system

With the ever-increasing number of Internet of Things devices (IoTDs) and the rapid development of artificial intelligence (AI) technologies, mobile edge learning (MEL) has emerged as a new communication network paradigm that can deploy machine learning on mobile edge computing (MEC) platforms with abundant computational resources. Then how to fully exploit the potential of MEL and enhance its performance is an important issue. To address this issue, the paper proposes an unmanned aerial vehicle (UAV)-and-reconfigurable intelligent surface (RIS)-aided MEL system. In the system, the UAV equipped with a MEL server can fly close to the IoTDs to collect their data for training a deep learning model. And the RIS mounted on the building can improve the wireless channel environment between the UAV and IoTDs to assist the UAV in collecting data. In order to maximize the MEL learning performance while minimizing the system energy consumption, this paper also proposes a new optimization metric called learning efficiency. Then, a learning efficiency maximization problem based on the proposed system is formulated by jointly optimizing the minority class sample size, the transmit resource of the IoTDs, the phase shift of the RIS, and the trajectory of the UAV. Considering the intractability of the problem, we solve it using the alternating optimization (AO) algorithms based on the two types of UAV trajectory design, i.e., a time-division-multiple-access (TDMA) design with higher performance and a Flight-Hover design with lower complexity. The simulation results demonstrate that the proposed optimization metric and algorithms are effective and perform excellently compared to other baselines.

Dynamically stabilized recurrent neural network optimized with intensified sand cat swarm optimization for intrusion detection in wireless sensor network

Wireless Sensor Networks (WSNs) are susceptible to various security threats owing to its deployment in hostile environments. Intrusion detection system (IDS) contributes a critical role on securing WSNs by identifying malevolent activities and ensuring data integrity. Traditional IDS techniques often struggle with the dynamic and resource-constrained nature of WSNs. In this paper, Dynamically Stabilized Recurrent Neural Network Optimized with Intensified Sand Cat Swarm Optimization for Wireless Sensor Network Intrusion identification (DSRNN-ISCOA-ID-WSN) is proposed. Initially, the input data is amassed from WSN-DS dataset. After that, the pre-processing segment receives the data. In pre-processing stage, redundant and biased records are removed from input data with the help of Adaptive multi-scale improved differential filter (AMSIDF). Then the optimal are selected by utilizing Wolf-Bird Optimization Algorithm (WBOA). DSRNN is used to classify the data as Normal, Grey hole, Black hole, Time division multiple access (TDMA), and Flooding attacks. Then Intensified Sand Cat Swarm Optimization (ISCOA) is employed to optimize the weight parameters of DSRNN for accuracte classification. The proposed DSRNN-ISCOA-ID-WSN technique is implemented Python. The performance of the proposed DSRNN-ISCOA-ID-WSN approach attains 29.24 %, 33.45 %, and 28.73 % high accuracy; 30.53 %, 27.64 %, and 26.25 % higher precision when compared with existing method such as Machine Learning-Powered Stochastic Gradient Descent Intrusions Detection System for WSN Attacks (SGDA-ID-WSN), An updated dataset to identify threats in WSN (CNN-ID-WSN) and Denial-of-Service attack detection in WSN: a Low-Complexity Machine Learning Model (DTA-ID-WSN) respectively.

Load-adaptive MAC protocol for frontier detection in Underwater Mobile Sensor Network

This work proposes a load-adaptive Medium Access Control (MAC) protocol for the frontier/boundary detection application of underwater phenomena using Underwater Mobile Sensor Network (UWMSN). A leader-follower architecture of a swarm of underwater vehicles is proposed here. Autonomous Underwater Vehicles (AUVs) traverse a random mobility pattern beneath one Autonomous Surface Vehicle (ASV) (leader) in the proposed network. ASV has to guide multiple-follower AUVs in the event of interest. The vehicular swarm aims to explore the frontiers in the event to build the map. Load-adaptive MAC protocol is therefore proposed and implemented in this hybrid multi-vehicular network to ensure seamless vehicular communications. The ASV has navigational capabilities to aid the AUVs in navigation and data collection. The proposed MAC protocol can adjust the dynamic mobility and load in the network. The protocol aims to provide dynamic Time Division Multiple Access (TDMA) slots for the AUVs wirelessly linked in the vicinity of the ASV. These slots are used for ranging/navigation and data transmission. Additional urgent data from any AUVs can be transmitted in open Carrier Sense Multiple Access (CSMA) protocol following the TDMA duration. Results have been generated by comparing protocols like CSMA, ALOHA, and TDMA with the proposed Load-Adaptive MAC protocol. The protocols have been compared to the throughput vs number of nodes and throughput vs simulation time. It has been observed that the proposed MAC can perform better than ALOHA and CSMA protocols. Nevertheless, it can produce comparable results for TDMA protocol while supporting the dynamic mobility and load in the network meantime supporting urgent data transmission for nodes in demand.

Energy efficient multi-user task offloading through active RIS with hybrid TDMA-NOMA transmission

In this paper, we address the challenge of minimizing system energy consumption for task offloading within non-line-of-sight (NLoS) mobile edge computing (MEC) environments. Our approach integrates an active reconfigurable intelligent surface (RIS) and employs a hybrid transmission scheme combining time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) to enhance the quality of service (QoS) for user task offloading. The formulation of this problem as a non-convex optimization issue presents significant challenges due to its inherent complexity. To overcome this, we introduce an innovative method termed element refinement-based differential evolution (ERBDE). Initially, through rigorous theoretical analysis, we optimally determine the allocation of local computation resources, computation resources at the base station (BS), and transmit power of users, while maintaining fixed values for the offloading ratio, amplification factor, phase of the reflecting element, and the transmission period. Subsequently, we employ the differential evolution (DE) algorithm to iteratively fine-tune the offloading ratio, amplification factor, phase of the reflecting element, and transmission period towards near-optimal configurations. Our simulation results demonstrate that the implementation of active RIS-supported task offloading utilizing the hybrid TDMA-NOMA scheme results in an average system energy consumption reduction of 80.3%.

Joint optimization for energy efficient full-duplex UAV relaying with multiple user pairs

This paper investigates an unmanned aerial vehicle (UAV) assisted amplify-and-forward relaying, where a full-duplex (FD) fixed-wing UAV employs a time-division multiple access scheduling protocol to provide relay services for multiple source–destination user pairs. With the aim of maximizing energy efficiency (EE) of the system, a joint optimization problem is studied so as to jointly fulfill the communication scheduling of multiple user pairs, the transmit power control and the trajectory design of the UAV. Since the optimization variables of the problem are coupled, it is non-convex and hence hard to solve directly. To this end, the initial problem is decomposed into three subproblems corresponding to the optimization of communication scheduling, and transmit power and trajectory of the UAV, respectively. The three subproblems are solved by utilizing the linear programming, the successive convex approximation (SCA), and the Dinkelbach’s algorithm. Then an iterative algorithm based on the block coordinate descent technique is proposed to tackle the joint optimization problem by optimizing the three blocks of variables alternately. Simulation results demonstrate that the proposed algorithm converges efficiently, and the EE of the joint optimization scheme can be significantly improved compared to the benchmark schemes.

Design and Implementation of TDMA Scheduling Based on BRKGA

Abstract Messages in wireless Ad hoc networks may require intermediate multi-hop forwarding from the source to the target node. To find an optimum contend-free time slots scheduling solution in Ad hoc, a TDMA (Time Division Multiple Access) scheduling scheme based on BRKGA (Biased Random-key GA) is proposed. The algorithm divides the population of generation k into elite group and rest group. The elite solutions are copied directly to the next generation. One parent from the elite group and another parent from the rest population are randomly selected to crossover a new solution. The worst Pm solutions of the population of generation k are replaced by the mutants. The simulation results demonstrate that the TDMA based on BRKGA exhibits better performance in terms of lower frame length and higher channel utilization.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System With Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4 × and 4.3 × in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39 × with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6 × and 2.8 × savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1 × in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

Ultra-Low-Power Sensor Nodes for Real-Time Synchronous and High-Accuracy Timing Wireless Data Acquisition.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ -4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.

Task execution latency minimization for energy-sensitive IoTs in wireless powered mobile edge computing: A DRL-based method

Wireless powered mobile edge computing (MEC) has become a vital component of future 6G networks, offering efficient computational capabilities to internet of things (IoT) devices and ensuring a stable energy supply. In this paper, we propose an innovative design for a wireless powered MEC-assisted energy-sensitive IoT systems. The system integrates wireless power technology (WPT), allowing IoT devices to efficiently perform tasks under wireless power supply without consuming their battery energy. Therefore, we formulate a dynamic computational task execution latency minimization (DCTELM) problem to tackle the impacts of unpredictable energy requirements and potential communication latency on resource allocation. We also analyze the differences between time division multiple access and non-orthogonal multiple access communication protocols. Given the complexity and dynamic nature of this problem, we define it as a mixed integer nonlinear programming problem. To address this problem, we introduced an enhanced deep reinforcement learning (DRL) algorithm. First, the DCTELM problem is transformed into a Markov decision process (MDP), which simplifies the structure of the problem. Then, after determining the offloading decision, we reveal the convex relationship between resource allocation and task latency. Furthermore, the action space of MDP is further reduced, and a multi-head proximal policy optimization (PPO) algorithm is proposed to deal with the simplified MDP problem. This process reduces the complexity of problem solving, decreases the consumption of computational resources. Simulation results demonstrate that our method outperforms other baselines in terms of computation latency, particularly with the introduction of an exploration strategy during the training phase, which significantly reduces task latency.

Resource allocation for relaying cognitive network with energy harvesting

This paper investigates a relay-based cognitive wireless-powered communication network with multiple secondary receivers and a secondary relaying network. In the secondary network, a relay adopts an energy model to harvest energy from radio frequency signals of the primary and secondary sources and subsequently utilizes the harvested energy to cooperate with the source for information transmission to multiple Internet-of-things users over time-division multiple access. The research group formulated a novel optimization problem that jointly optimizes the power and time fraction for energy and information transmission to maximize the sum throughput of the secondary network. The authors first converted the non-convex problem to a more computationally tractable problem and then proposed an iterative algorithm in which closed-form solutions are obtained at each iteration. Thus, simulation results verify and demonstrate the effectiveness of the proposed approach.

Spatial Simultaneous Functioning-Based Joint Design of Communication and Sensing Systems in Wireless Channels

This paper advocates for spatial simultaneous functioning (SSF) over time division multiple access (TDMA) in joint communication and sensing (JCAS) scenarios for improved resource utilization and reduced interference. SSF enables the concurrent operation of communication and sensing systems, enhancing flexibility and efficiency, especially in dynamic environments. The study introduces joint design communication and sensing scenarios for single input single output (SISO) and multiple input multiple output (MIMO) JCAS receivers. An MIMO-JCAS base station (BS) is proposed to process downlink communication signals and echo signals from targets simultaneously using interference cancellation techniques. We evaluate the communication performance and sensing estimation across both Rayleigh and measured realistic channels. Additionally, a deep neural network (DNN)-based approach for channel estimation and signal detection in JCAS systems is presented. The DNN outperforms the traditional methods in the bit error rate (BER) versus signal-to-noise ratio (SNR) curves, leveraging its ability to learn complex patterns autonomously. The DNN’s training process fine-tunes the performance based on specific problem characteristics, capturing the nuanced relationships within data and adapting to varying SNR conditions for consistently superior performance compared to the traditional approaches.