International Journal of Communication Systems最新文献

In this study, we propose a simple spectrum sensing method based on exploiting the properties of a group-delay phaser. Following the theory that an additive white noise should have a flat spectrum over the band of interest, which is not the case for most data-modulated signals, the spectrum shape of input waveforms has been the test variable. The latter enables a clear distinguishing method between a noise background and a communication signal of a transmission body operating over the desired band. The phaser scatters the frequency components of received signals in time space, allowing a time-domain inspection of the corresponding spectral response. The accurate closed-form analytical expression for the probability of detection in an additive white Gaussian noise (AWGN) channel has been derived, in addition to the false alarm probability. The probability of detection has been studied versus signal-to-noise ratio (SNR) in AWGN and multipath channels. As well, the receiver operating characteristic (ROC) curves have been plotted for different values of SNR. A good performance has been achieved by our scheme, which has recorded a detection probability of 0.86 for a false alarm probability of 0.1, and further shown a kind of robustness against noise uncertainties. Experimental works have eventually been conducted, and the empirical results also validate the effectiveness of the elaborated approach. An improvement of around 30% in the probability of detection has been realized over the energy-based sensing technique, at low measures of false alarm probability.

With the current advancements in millimeter wave communication, low-profile structure and power handling capability at high frequencies have become the prime focus for the researchers. substrate integrated waveguide (SIWs), providing better efficiency by exhibiting high-quality factors and confining EM waves, have become a favorable technology in the antenna regime. Recently, SIWs along with liquid metals have facilitated reconfigurability with extremely thin substrate materials. Antenna on-chip (AOC) using Complementary Metal Oxide Semiconductor (CMOS) technologies along with Multiple Input and Multiple output (MIMO) applications are showing great potential for researchers in this domain. This review article intends to provide a comprehensive overview of the different types of SIW antennas operating in the 5G band, various SIW topologies, detailed analysis of gain, bandwidth, and isolation. Slotted SIW antennas give exceptional isolation between the bands and low cross-polarization levels while horn antenna arrays offer wide coverage. Slow wave SIW has achieved an 80% reduction in size with the usage of multi-antipodal metalized blinds via holes and distributed metal strips. Details on advancement in the artificial intelligence (AI) based SIW antennas are also presented in brief.

The fifth generation (5G) of cellular communication networks has introduced various advanced technologies to address the increasing demand for higher data rates and improved spectrum utilization. One of these technologies, non-orthogonal multiple access (NOMA), has gained significant attention due to its ability to enhance spectral efficiency by allowing multiple users to share the same time-frequency resource block. NOMA affords a number of advantageous features including increased spectrum efficiency (SE). It comes in various forms, such as power-domain (PD) NOMA and code-domain (CD) NOMA. This paper attentions mainly on enhancing the SE of downlink (DL) PD-NOMA in a 5G cooperative spectrum sharing environment using single input single output (SISO), massive MIMO (M-MIMO), and multiple input multiple output (MIMO). Two methods are approached here. In the first one, the NOMA handlers access the free/unconstrained channels using competing channel (C-Ch) approach, whereas the next one uses dedicated channel (D-Ch) approach. Five users are considered at distances of 1000, 800, 600, 400, and 200 m from the base station (BS) with different power allocation coefficients at transmitting power of 40 dBm and bandwidth of 70 MHz. Quadrature phase shift keying (QPSK) is used with successive interference cancellation (SIC) at the receiver side and superposition coding (SC) at the transmitter side under frequency selective Rayleigh fading environment. The PD DL NOMA system's results demonstrated that combining 32 × 32 MIMO, 64 × 64 MIMO, and 128 × 128 M-MIMO in a single cell and the same network with cooperative cognitive radio network (CoCRN) significantly improved the SE reliability. The user Ur5 produces an optimal SE performance of 3.753 bps/Hz/cell for PD DL NOMA with SISO, 5.77 bps/Hz/cell for CoCRN PD DL NOMA with SISO using C-Ch, and 7.45 bps/Hz/cell for CoCRN DL PDNOMA with SISO using D-Ch with a 40 dBm transmitting power. Furthermore, the SE for Ur5 (nearest user) was increased to 64%, 67%, and 69%, respectively, using PD-NOMA with 32 × 32 MIMO, PD-NOMA with 32 × 32 MIMO using C-Ch, and DL NOMA PD with 32 × 32 MIMO using D-Ch. DL PD-NOMA with MIMO(64 × 64) using D-Ch improved the SE rate by 77% having transmission power of 40 dBm in comparison with the SE outcome for DL PDNOMA with SISO; however, DL PD-NOMA with MIMO (64 × 64) most dramatically upgraded the SE rate for Ur5 by 73%. Using C-Ch and CoCRN DL PD-NOMA with MIMO (64 × 64), the SE performance was boosted by 76%. The best user, Ur5, had an 82% improvement in SE performance when DL PD-NOMA with M-MIMO (128 × 128) was compared to DL NOMA with SISO. With a 40 dBm transmission power, CoCRN DL NOMA with 128 × 128 M-MIMO using C-Ch showed an 88% improvement, whereas CoCRN DL PD-NOMA with M-MIMO (128 × 128) using D-Ch experienced an 89% improvement.

Many new use cases and a broad spectrum of vertical businesses are expected to be supported by next-generation wireless networks. Network slicing has been implemented to suit the stringent requirements of different services. By dividing the infrastructure network into several logical networks, this technology enables resource allocation based on services. In dynamic and unpredictable situations, managing resources across several domains and dimensions for end-to-end (E2E) slicing still presents difficulties. Tenant satisfaction requires striking a balance between the trade-off between revenue and the expense of resource allocation. Network slicing has emerged as a fundamental paradigm in next-generation networks to meet the diverse service requirements of various applications and users. However, the dynamic nature of network conditions and service demands poses challenges in efficiently allocating network resources to meet performance objectives. In this paper, a faster graph recurrent convolutional neural network (FGRCNN) with improved deep reinforcement learning (IDRL) is proposed to learn traffic behavior from link and node properties in addition to network structure. To train the FGRCNN model in the IDRL framework without requiring a labeled training dataset, employ the Deep Q-learning technique. This allows the framework to swiftly adjust to changes in traffic dynamics. A system is proposed to analyze real-time network and service data enabling dynamic adaptation of network slices for changing traffic patterns and service requirements. A comprehensive framework is presented that integrates deep learning models with network slicing orchestration mechanisms to achieve tailored service delivery. Through extensive simulations and experiments, the effectiveness approach in optimizing resource utilization is demonstrated, improving service quality and enabling agile network management in next-gen networks. Results highlight the potential of deep learning-enabled adaptive network slicing to support diverse and evolving service demands in future network environments.

Device-to-device (D2D) transmission is essential for enhancing the functionality of fifth-generation (5G) networks. This paper addresses the need for effective power allocation and resource management for D2D users, who operate as secondary users (SUs) alongside primary users (PUs). Ensuring that D2D operations do not disrupt PU interactions is crucial. Traditional bandwidth distribution approaches rely on complete channel state information (CSI) from the base station (BS), leading to uncertainty fin resource allocation. To get rid of these challenges, this paper proposes a novel handoff strategy based on spectrum allocation in cognitive radio networks (CRNs). The data priority–based channel allocation is carried out using proposed hybrid optimization cuttlefish updated dwarf mongoose optimization (CUDMO). It is the combination of both cuttle fish algorithm (CFA) and dwarf mongoose optimization (DMO) algorithms. This optimization considers constraints such as coverage, signal strength, distance, bandwidth and improved strategy like signal-to-noise ratio–channel usability (SNR-CU). Furthermore, an improved fuzzy logic–based proactive handoff mechanism, the fuzzy-induced modified rules for channel selection (FIMRCS), is introduced. This scheme optimally selects channels, minimizing service interruption during handoff. In the dynamic multichannel selection (DMCS) scheme, parameters like channel rank, channel transmission, and channel usability are considered as the constraints while selecting the channel. They are evaluated against a set of 27 defined rules, ensuring efficient data transmission through the chosen channels. Finally, the performance of proposed CUDMO algorithm is contrasted over state-of-the-art models in terms of various constraints. The CUDMO for Device 450 generated a bandwidth of 1.175 bps, surpassing the lower bandwidth achieved by conventional strategies.

This research aims to design hybrid analog and digital beamforming to improve the signal-to-noise ratio (SNR) and spectral efficiency (SE) of communication links. Considering the complexity and cost associated with fully connected multiple-input multiple-output (MIMO) communication models, a partially connected system model is adopted for the downlink millimeter-wave (mmWave) communication model. The analog beamforming utilizes the adaptive search (AdaLS) algorithm to minimize interference and enhance user power, whereas digital beamforming is optimized using the proposed Chaotic Chebyshev Aquila Optimization (CCAO) algorithm. The CCAO algorithm integrates chaotic Chebyshev–based solution mapping with the conventional Aquila Optimization Algorithm to enhance exploration capability. The system model is illustrated, and the problem is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) through the selection of the required signal at the receiver. The digital beamformer is designed using Lagrange's multiplier, and the analog beamformer is optimized using AdaLS. The proposed CCAO algorithm is detailed, incorporating chaotic dynamics to explore the solution space effectively. The research evaluates the performance of the proposed method against conventional approaches, showcasing improved normalized beam gain and SINR.

Device-to-device (D2D) communication is a promising development in 5G networks, offering potential benefits such as increased data rates, reduced costs and latency, and improved energy efficiency (EE). This study analyzes the operation of millimeter-wave (mmWave) in cellular networks. A client's device can establish a connection to either a base station or another client, facilitating D2D communication based on a distance threshold and accounting for interference. The research employs a deep reinforcement learning (DRL)–based resource allocation (RA) scheme for D2D-enabled mmWave communications underlaying cellular networks. It evaluates the effectiveness of several metrics: coverage probability, area spectral efficiency, and network EE. Among networks limited by noise, the proposed strategy demonstrates the highest coverage probability performance. The paper also suggests an optimization approach based on the firefly algorithm for RA, taking into account the stochastic nature of wireless channels. An asynchronous advantage actor–critic (A3C) DRL algorithm is modeled for this purpose. The performance of the proposed scheme is compared with two existing algorithms: soft actor–critic and proximal policy optimization. Overall, the numerical results indicate that our proposed firefly algorithm–optimized A3C method outperforms the other analytical methods.

To improve communication network efficiency, researchers must look at all aspects of transmission and the mechanisms that regulate their evolution as a whole. These features include solutions for dealing with the channel's noise and interference. To decrease interference and increase spectrum efficiency, orthogonal frequency division multiple access (OFDMA) systems employ orthogonal signals. While transmitting and receiving signals, noise and numerous feeds can be done in diverse ways. It has become increasingly common to use 256 quadratic modulation (QAM), which is more vulnerable to noise and has a higher bit error rate (BER). BERs in OFDM systems were high when multiple feeds and noise were present, as demonstrated in this article. Starting with the transmission and reception of Gaussian subband signals, an improved system has been designed that includes numerous stages of development. Thus, the need for “orthogonally” of transmitted signals to increase spectrum efficiency has been eliminated, as has the effect of surrounding channels. We have created a header for every frame that has been transmitted. Several transmitters and numerous receivers send these frames in parallel so that the channel state information (CSI) attributes may be evaluated using parallel processing. Using the identical transmission conditions for both OFDM systems and the proposed system, the simulation results reveal a significant reduction in BER values. This results in BER values of fewer than 10⁻¹ when there are two tabs and 10⁻¹ when there are three tabs for multiple feeding in the OFDM system. This corresponds to BER values of 10⁻¹¹ in a suggested system when there are three tabs. Some improvements have been made to the proposed design to make it distinctive and qualified to be regarded as a multiaccess system in today's contemporary communication infrastructures.

This paper presents a new algorithm that utilizes compressed sensing (CS) for reconstruction of wireless sensor networks (WSNs) data with spatial and temporal correlation. The proposed method utilizes a time-varying sliding window mechanism that dynamically adjusts both the window size and the number of measurements. This flexibility allows the algorithm to exploit spatio-temporal correlations effectively, ensuring that data within the window remains sparse and thus more compressible. By dynamically varying the number of measurements, the algorithm equitably distributes the sampling rate across different time slots, adapting to changes in signal characteristics and minimizing transmission costs. Simulation results demonstrate that our proposed algorithm outperforms other CS reconstruction methods by achieving higher reconstruction precision while requiring fewer transmissions. This is achieved through a decentralized data-window framework that maximizes the use of prior signal information, leading to improved signal recovery performance in diverse WSN scenarios.