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Aiming to address the issue of multi-user dynamic spectrum access in an opportunistic mode in cognitive radio networks leading to low sum throughput, we propose a multi-user opportunistic spectrum access method based on multi-head self-attention and multi-agent deep reinforcement learning. First, an optimization model for joint channel selection and power control in multi-user systems is constructed based on centralized training with a decentralized execution framework. In the training phase, the decision-making policy is optimized using global information, while in the execution phase, each agent makes decisions according to its observations. Meanwhile, a multi-constraint dynamic proportional reward function is designed to guide the agent in selecting more rational actions by refining the constraints and dynamically adjusting the reward proportion. Furthermore, a multi-head self-attention mechanism is incorporated into the critic network to dynamically allocate attention weights to different users, thereby enhancing the ability of the network to estimate the joint action value. Finally, the proposed method is evaluated in terms of convergence, throughput, and dynamic performance. Simulation results demonstrate that the proposed method significantly improves the sum throughput of secondary users in opportunistic spectrum access.

The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal multiple access (NOMA) is one of the fundamental pieces of technology for solving the problem of limited bandwidth, and it is expected to be beneficial for many modern wireless underwater acoustic applications. NOMA downlink underwater acoustic communication (UWA) is accomplished by broadcasting data symbols from a source station to several users, which uses superimposed coding with variable power levels to enable detection through successive interference cancellation (SIC) receivers. Nevertheless, comprehensive information of the channel condition and channel state information (CSI) are both essential for SIC receivers, but they can be difficult to obtain, particularly in an underwater environment. To address this critical issue, this research proposes downlink underwater acoustic communication using a deep neural network utilizing a 1D convolution neural network (CNN). Two cases are considered for the proposed system in the first case: in the first case, two users with different power levels and distances from the transmitter employ BPSK and QPSK modulations to support multi-user communication, while, in the second case, three users employ BPSK modulation. Users far from the base station receive the most power. The base station uses superimposed coding. The BELLHOP ray-tracing algorithm is utilized to generate the training dataset with user depth and range modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. The DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in terms of BER in simulation results for all the modulation orders.

Recently, non-orthogonal multiple access (NOMA) has become prevalent in 5G communication. However, the traditional successive interference cancellation (SIC) receivers for NOMA still encounter challenges. The near-far effect between the users and the base stations (BS) results in a higher bit error rate (BER) for the SIC receiver. Additionally, the linear detection algorithm used in each SIC stage fails to eliminate the interference and is susceptible to error propagation. Consequently, designing a high-performance NOMA system receiver is a crucial challenge in NOMA research and particularly in signal detection. Focusing on the signal detection of the receiver in the NOMA system, the main work is as follows. (1) This thesis leverages the strengths of deep neural networks (DNNs) for nonlinear detection and incorporates the low computational complexity of the successive interference cancellation (SIC) structure. The proposed solution introduces a feedback deep neural network (FDNN) receiver to replace the SIC in signal detection. By employing a deep neural network for nonlinear detection at each stage, the receiver mitigates error propagation, lowers the BER in NOMA systems, and enhances resistance against inter-user interference (IUI). (2) We describe its algorithm flow and provide simulation results comparing FDNN and SIC receivers under MIMO-NOMA scenarios. The simulations clearly demonstrate that FDNN receivers outperform SIC receivers in terms of BER for MIMO-NOMA systems.

With the deployment of fifth-generation (5G) mobile communication technology and rapid advancements in artificial intelligence and edge computing, smart construction sites have emerged as a critical direction for the construction industry’s transformation and upgrading. However, existing intelligent Internet of Things (IoT) access algorithms often struggle to simultaneously meet practical requirements for high-efficiency data transmission rates, low latency, and secure privacy-aware access in the dynamic and complex environments of smart construction sites. To address this, this paper proposes a federated learning-based Multi-Objective Dueling Double Deep Q-Network (FL-MD3QN)-based IoT access algorithm for multi-site, multi-modal, multi-user IoT systems under the same Base Station (BS). First, a three-objective optimization mathematical model was established. The optimization goals include maximizing data transmission rates, minimizing transmission delays, and maximizing reliability. Constraints such as bandwidth, rate, bit error rate (BER), and security/privacy are defined. Second, the FL-MD3QN algorithm is developed to solve this optimization problem. This algorithm can adaptively adjust the access strategy to cope with the complex and ever-changing communication needs of smart construction sites and, by introducing a federated learning mechanism, it achieves collaborative optimization of multiple construction site IoT systems while ensuring user privacy. Simulation results demonstrated significant advantages of the FL-MD3QN algorithm. For latency, it achieved markedly lower delays across multi-modal services compared to benchmark algorithms, with the shortest training time. In transmission rates, FL-MD3QN delivered the highest average rates, particularly excelling in video services. Under high signal-to-noise ratio conditions, FL-MD3QN achieved exceptionally low BER values. Additionally, it attained high levels in average access success rate and average reward value, confirming its robust adaptability and optimization performance in complex smart construction environments.

Secondary users (SUs) can use radio resources simultaneously and often in a Non-Orthogonal Multiple Access-based Cognitive Radio network (NOMA-CRN). This exhaust is insufficient for 5G cellular systems to provide physical layer services. To address this problem, Joint user cluster pairing (J-UCP) technique is introduced to maximise data capacity and channel utility. This method can identify strong and weak users based on channel state data (CSI). Then users are paired (1-strong SU) and (1-weak SU) based on channel gain difference. To maximise data rate, an appropriate channel gain difference is maintained for all near gain user pairings. Assuring that the channel gain differential is higher than a threshold in a near gain SU pair forms. Consider a single SU pair that occupies two near-gain users to comprehend the realistic situation by analysing outage probability using stochastic geometry. To boost the SU pair’s sum rate, an ergodic sum rate is calculated using a closed-loop solution for the outage probability. To achieve near-gain user pair eligibility in a CR-NOMA system, the analytical model and simulation outputs for outage probabilities and sum rates are obtained.

Cognitive radio (CR) is a futuristic technology which efficiently uses the underutilized TV band spectrum for mobile communication. The spectrum scarcity issue, mobile traffic due to ever-increasing number of clients utilizing the same spectrum and interference problems will be efficiently handled by CR networks. In this paper, we employ CR spectrum for safe transmission of medical reports consisting of magnetic resonance imaging (MRI) scanned images. An effective image encryption algorithm named Arnold cat-map (ACM) transform is used in order to prevent unauthorized alterations in the MRI scanned image by any un-authenticated personnel. Further, we upgrade the resolution of the MRI scanned image by super-resoluting it by SPARSE super-resolution technique. Furthermore, we analyze the transmission of MRI scanned image by considering turbo code as channel encoder. We incorporate space time block code (STBC) as multiple-input and multiple-output (MIMO) profile due to its supremacy in spatial diversity and code division multiple access (CDMA) for simultaneous data transmission to numerous users, for transmission of the MRI scanned report. We utilize CR sub-band frequency to realize multi-carrier (MC) communication and to generate orthogonal spread-spectrum. Furthermore, we also analyse the error rate performance of the system for various Stanford University Interim (SUI) channel models. Finally, from the simulations we divulge that CR defined MIMO MC-CDMA system obtain MRI image with enhanced resolution and upgraded privacy when communicating through realistic channel model specifications.

The future 5G wireless network will be identified by flexibility design, high combination of services, and higher data rate. In this treatise, we present the performance evaluation physical layer design for multi-carrier code-division-multiple-access (MC-CDMA) system for cognitive radio network (CRN) employing link adaptive multiple-input multiple- output (MIMO). CRN in conjunction with MIMO promises to achieve massive amelioration in system bandwidth. We consider link adaption scheme (LAS) which will select modulation scheme and MIMO profile based on channel parameters. CRN is a device in wireless communication which can sense the idle unused spectrum and allocate the spectrum dynamically to base station. We utilize sub-band frequency of CRN for multi-carrier communication and to extract for user-specific spreader in CDMA system. Further, we realize iterative decoder at the receiver to achieve better error-rate performance for CRN based MIMO MC-CDMA system with less signal-to-noise ratio (SNR). Furthermore,We study the performance of adaptive MIMO scheme with CRN defined MC-CDMA for Stanford University Interim channel model specifications. We discern through computer simulations that CRN based MC-CDMA system with adaptive MIMO scheme achieves significant performance enhancement both in error-rate and data rate.

In this letter, the error-rate (ER) performance of multiple input and multiple output multi-carrier code-division-multiple-access (MIMO MC-CDMA) is evaluated with the aid of cognitive radio network (CRN). MIMO is proven to be useful for high data rate application. MC-CDMA is used to accommodate higher number of user by eliminating channel impairments. CRN is suggested for 5G network to offer higher bandwidth by exposing idle spectrum. Multi-carrier modulation is processed inverse fast-Fourier transform at transmitter and demodulation at each receiver using fast-Fourier transform. Multi-carrier technique is introduced to obtain bandwidth efficiency and overcome the problem of frequency selectivity. At each mobile station, we estimate user’s information using MMSE based iterative algorithm. Further; the system performance is tested using channel encoder. We structure channel encoder using turbo code which is designed with the help of two convolutional encoder. The input information is interleaved using random interleaver and is fed to second convolutional encoder. We create puncturing matrix using input information and output of two convolutional encoders. Then we puncture parity bit information to obtain necessary code rate. Further, we decode and estimate information using iterative decoder which ensure higher performance with lower signal-to-noise ratio. It is vindicated from simulations that CRN based MIMO MC-CDMA system with iterative decoder swell better ER while ensuring higher data rate for downlink signal transmission.

In this contribution, the performance of double space time transmit diversity (DSTTD) based multiple-input and multiple-output system with spatial diversity is investigated for cognitive radio network (CRN) based code-division-multiple-access (CDMA) system. CRN is realized in order to invoke available idle spectrum for reliable communication between base station and mobile station. DSTTD is constructed using two space- time processing unit. DSTTD is proven to provide high throughput .The user-specific spreading code is generated using sub-band frequency of CRN. Time-Frequency domain signature sequence is exploited to distinguish secondary users. The performance of CRN based DSTTD-CDMA scheme is analyzed using coded system. At each MS, iterative type of decoder is implemented to estimate secondary users’ information. The performance is analyzed for coded CRN based DSTTD-CDMA system for channel model with Stanford University Interim along with long-term evolution specifications. It is observed through simulation results that coded CR DSTTD-CDMA system establish proficient communication for secondary users while offering higher throughput and higher spectral efficiency and better performance in the context of bit-error-rate with fewer value of signal-to-noise ratio. Further, it is discerned that decoding algorithm obviate adjacent-channel interference along with secondary multi-user interference and CRN based CDMA system in conjunction DSTTD offers solution for high data rate application.

In this work, the problem of multi-rate Multi-Carrier (MC) Code Division Multiple Access (CDMA) wireless transmission is addressed. In particular, we investigated the possibility of exploiting subcarrier grouping, already considered in literature for constant bit-rate MC-CDMA, in order to reduce mutual interference among different rate users and to allow the use of theoretically-optimum Maximum-Likelihood Multi-User Detection (ML-MUD) with affordable computational burden. We propose a multi-code Group Orthogonal (GO) OFDMA-CDMA system where the available subcarriers are subdivided into fixed-cardinality orthogonal subcarrier groups. The user’s data stream is selectively multiplexed into a variable number of substreams, which depends on the data-rate. Then, these substreams are transmitted over an orthogonal subcarrier group, univocally assigned to a user rate class. Experimental results obtained by adopting linear multi-user detection show that the proposed GO-OFDMA-CDMA outperforms state-of-the-art Variable Spreading Length (VSL) and multi-code MC-CDMA as far as higher data rate users are concerned. On the other hand, BER performance of lowest-rate users is slightly worse. Orthogonal subcarrier grouping allows to greatly increasing BER performance when using ML-MUD operated over small subcarrier groups. In such a case, the tradeoff to be managed is between achievable performance and computational complexity.