Efficient Spectrum Utilization Research Articles

Green integrated cooperative spectrum sensing for cognitive satellite terrestrial networks

AbstractIn this paper, a two‐way relay‐aided cognitive satellite terrestrial network (TR‐CSTN) model is proposed, where primary users are located at the edge of the base station. In the TR‐CSTN, one of satellite terminal users (STUs) is selected by the fusion center as the TR to forward information between two edge primary users with power of the TR. Meanwhile, these edge primary users share the licensed frequency band with the selected TR to send information to the satellite. Then, given the limited spectrum utilization and energy efficiency (EE) of the communication system, the cooperative spectrum sensing is employed to realize green communication. Specifically, the fusion center threshold, energy detection threshold, sensing duration and number of STUs are jointly optimized to enhance EE. Furthermore, considering that the node's energy shortage results in a short network lifetime, absolute EE gets improved. In detail, a power allocation scheme named normalized power aided Lévy flight trajectory‐based whale optimization algorithm (NP‐LWOA) is provided, which fulfills effective energy compensation among STUs to prolong the network lifetime notably. Finally, numerical results confirm the theoretical analysis and show the effectiveness of the TR‐CSTN and the NP‐LWOA in efficiently achieving the concept of green communication compared with other methods.

Performance evaluation of hard‐decision and soft‐data aided cooperative spectrum sensing over Nakagami‐m fading channel

AbstractThe secondary users can opportunistically utilize the spectrum holes left by the licensed users (also called primary users [PUs]) with the help of cognitive radio (CR) technology. The cooperative spectrum sensing (CSS) is crucial for the implementation of CR algorithms in the next‐generation wireless networks, for the specific identification of spectral gaps of PUs under any fading/shadowing channel. Accurate spectrum sensing reveals that the CUs only provide data when the PUs report being idle. Yet, there is always a possibility that CU transmission will cause interference with PU communications, leading to inefficient spectrum use. So, this work explores the performance of a proposed CSS by considering such factors with the key objective of enhancing spectrum utilisation efficiency. This paper evaluates the role of the proposed CSS with a variety of hard decision fusion rules (HDFR) schemes as well as soft data fusion schemes (SDFS) methods. Toward this end, for the proposed system, closed‐form expressions for the evaluation of various performance metrics such as the false alarm, miss‐detection probabilities, total error probability (TEP), and throughput/energy efficiency are derived by taking into consideration the Nakagami‐m fading environment using different hard and soft algorithms and are validated by means of numerical simulations. Furthermore, by taking into account various network specifications, such as channel error probability (q), fading severity parameter (m), number of samples (M), number of CR nodes (N), average sensing (S) channel signal‐to‐noise ratio, and sensing threshold (λ), a performance evaluation between the SDFS and HDFR schemes has been carried out. Furthermore, using HDFR/SDFS schemes, analytical models are formulated for determining an optimal number of CR nodes () and optimal threshold () values for achieving the lowest TEP and highest energy efficiency.

SDR-Implementation of a Support Vector Machine-Assisted Covariance-Based Spectrum Sensing Algorithm in the Presence of Correlated Noise

Cognitive radio (CR) is an emergent technology that is considered for more efficient spectrum utilization. Spectrum Sensing (SS), i.e. detection of primary user (PU) in the frequency band, is one of the key tasks of cognitive radio systems. Recently, various works on the support vector machine (SVM)-assisted covariance-based spectrum sensing (CSS) in multi-receiver systems were presented and evaluated with computer simulations. However, the real environment is different, especially since the noise is often assumed to be spatially uncorrelated, while it is correlated in reality. Therefore, we propose a four-receivers software-defined radio (SDR)-based test platform for experimental evaluation of SVM-assisted CSS techniques. Furthermore, we implement an SVM-assisted CSS technique in the proposed platform to assess its performance when two and four receivers are activated. The measurements showed that increasing the number of receiving channels does indeed enhances the detection probability. Moreover, we evaluated the algorithm in both white and correlated noise scenarios. The evaluation demonstrates how the presence of correlated noise deteriorated the performance of the SVM-assisted CSS compared with the case where only white noise is present. More specifically, the simulations showed that to reach 99% detection probability when noise correlation is present, the implemented algorithm requires an SNR value that is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{10}~dB$</tex-math></inline-formula> greater than the one required when white noise only is present.

NOMA Assisted Semi-Grant-Free Scheme for Scheduling Multiple Grant-Free Users

Nonorthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission is a powerful solution to significantly reduce the end-to-end latency of data transmission and improve spectrum utilization efficiency facing the future Internet of Everything (IoE). Moreover, the recently proposed hybrid successive interference cancellation (HSIC) scheme can avoid the user's outage probability error floor, which is unavoidable in traditional uplink NOMA. However, the existing NOMA assisted SGF transmission schemes using this HSIC scheme admit only one grant-free (GF) user into the grant-based (GB) user's channel at a time. In order to further improve the throughput and reduce the waiting delay of GF users, in this paper, we propose a NOMA assisted SGF scheme with a new HSIC scheme allowing a certain number of GF users to share GB user's channel. First, we propose a new HSIC scheme to decode the signals of multiple GF users and one GB user. Then based on the signal's decoding order at the BS and stochastic geometry, we provide a general mathematical framework to analyze the association success probability of GF users. Besides, in order to intuitively show the throughput improvement brought by multiple admitted GF users, we derive the analytical expressions of admitted GF users' association success probability, outage probability, and throughput when two GF users are allowed to share simultaneously GB user's channel. Finally, the simulation results validate the throughput improvement brought by two GF users being served simultaneously and the accuracy of the theoretical analyses.

Green Spectrum Sharing Framework in B5G Era by Exploiting Crowdsensing

With the recent development of wireless technologies, a massive number of Internet of Things (IoT) devices will access the Beyond 5G (B5G) networks for various advanced applications. How to support this huge amount of wireless data traffic over limited spectrum resource is a critical problem. To address this problem, dynamic spectrum sharing has been extensively investigated recently to improve the spectrum utilization efficiency for sub-6GHz frequency bands. The secondary users are allowed to access the spatio-temporal spectrum holes of the primary users with the help of a spectrum database. In this paper, we propose a green spectrum sharing framework in B5G era by exploiting crowdsensing. The goal is to construct an accurate crowdsourced spectrum database in a cost-efficient way. Specifically, a machine learning based classifier is used to select the spots that need to be interpolated by crowdsensing users, and both offline and online user incentive mechanisms are proposed to attract users to participate into the crowdsensing task. To validate the proposed framework, we conduct a vehicle-based measurement campaign over a 2km &#x00D7; 7km area, and evaluate the performance of the proposed framework by using the collected real data.

Energy-Efficient Resource Allocation Algorithm for CR-WSN-Based Smart Irrigation System under Realistic Scenarios

Cognitive radio wireless sensor networks (CR-WSNs) are a type of WSNs that use cognitive radio technology to enhance the spectrum utilization and energy efficiency. This paper proposes an energy-efficient resource allocation algorithm (EERAA) to prolong the lifetime of a WSN-based smart irrigation system under realistic scenarios. In the proposed algorithm, power allocation and subcarrier assignment are performed consecutively. Considering the impact of the intercarrier interference (ICI) caused by timing offset, the problem of maximizing network-averaged capacity is formulated considering power and interference constraints in realistic scenarios. The obtained results reveal that the proposed algorithm attempts to maximize the averaged capacity of the CR-WSN subject to the total power constraint and tolerable interference. Numerically, the proposed algorithm can reduce the network energy consumption by up to 30%, compared with conventional approaches, while maintaining a high level of system performance in terms of secondary users’ (SUs) averaged capacity.

Improved Q-Reinforcement Learning based Optimal Channel Selection in Cognitive Radio Networks

Cognitive Radio Networks are an emerging technologyin forwireless communication. With increasing number of wireless devices in wireless communication, there is a shortage of spectrum. Also, due to thestatic allocation of channels in wireless networks, there is a scarcity of spectrum underutilization. For efficient spectrum utilization, secondary users dynamically select the free channel of primary users for the transmission of packets. In this work, the performance of routing in a cognitive radio network is improved by the decision of optimal channel selection. The aim of this work is to maximize the throughput and reduce the end-to-end delay. Therefore, an Improved Q-Reinforcement learning algorithm is proposed for the optimal channel selection during the packet routing between source and destination. The performance of this work is compared with the existing routing protocols. It is simulated in network simulator-2 (NS2) with Cognitive Radio Cognitive Network (CRCN) simulation. After performance evaluation, it is observed thatthe proposed work performs better than existing work with respect to packet delivery ratio, throughput, delay, jitter, control overhead, call blocking probability, packet dropping ratio, goodput and normalized routing overhead.

Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications

Since the CubeSats have become inherently used for the Internet of space things (IoST) applications, the limited spectral band at the ultra-high frequency (UHF) and very high frequency should be efficiently utilized to be sufficient for different applications of CubeSats. Therefore, cognitive radio (CR) has been used as an enabling technology for efficient, dynamic, and flexible spectrum utilization. So, this paper proposes a low-profile antenna for cognitive radio in IoST CubeSat applications at the UHF band. The proposed antenna comprises a circularly polarized wideband (WB) semi-hexagonal slot and two narrowband (NB) frequency reconfigurable loop slots integrated into a single-layer substrate. The semi-hexagonal-shaped slot antenna is excited by two orthogonal +/−45° tapered feed lines and loaded by a capacitor in order to achieve left/right-handed circular polarization in wide bandwidth from 0.57 GHz to 0.95 GHz. In addition, two NB frequency reconfigurable slot loop-based antennas are tuned over a wide frequency band from 0.6 GHz to 1.05 GH. The antenna tuning is achieved based on a varactor diode integrated into the slot loop antenna. The two NB antennas are designed as meander loops to miniaturize the physical length and point in different directions to achieve pattern diversity. The antenna design is fabricated on FR-4 substrate, and measured results have verified the simulated results.

Quadrature Phase-Shift Keying Modulation With Random Coding Pulse for Long-Range ϕ-OTDR

Transient effect in erbium-doped fiber amplifier (EDFA) restricts the application of the phase-sensitive optical time-domain reflectometry (ϕ-OTDR) system with coding probe pulse (CPP) in long-distance vibration sensing. In this paper, a CPP modulation scheme based on phase coding is proposed to suppress the amplitude distortion after optical amplification. Quadrature phase-shift keying (QPSK), which has a high spectrum utilization efficiency, is designed on a I/Q modulator. The random sequence CPP is used in this system, because of the single coding sequence, the signal-to-noise ratio (SNR) can be improved without loss of the sensing bandwidth. Experimental results prove the effectiveness of the scheme. Compared to the conventional modulation scheme at a 50 mW pump power, the distortion coefficient of the CPP after amplification decreases from 57.67% to 9.23%, and the signal-to-interference ratio (SIR) increases from 13.89 dB to16.67 dB. Without distributed amplification, the proposed system successfully located the vibration at 85.652 km under the carrier period of 40 ns, and the SNR of the demodulated signal is up to 21.99 dB.

A Graph Neural Network Based Radio Map Construction Method for Urban Environment

Radio maps can enhance the capability of wireless sensor networks and improve the efficiency of spectrum utilization. However, the construction of an accurate radio map in the urban environment is a challenging task, since the dense buildings lead to a non-line-of-sight (NLOS) transmission environment. With this focus, we first transform the spatial sparse measurement points into a graph structure and extract the connected relation through the building map. Then, we propose a graph neural network (GNN) scheme to recover the complete radio map from sparse measurements. Numerical results show that our algorithm outperforms the benchmark methods in accuracy and is robust to noise.

A CSI Prediction Scheme for Satellite-Terrestrial Networks

Recently, low earth orbit (LEO) satellites have been suggested as an promising solution for global coverage of 5G networks. However, long propagation delay makes obtained channel state information (CSI) (which is employed for efficient spectrum utilization) outdated before usage. Note that CSI is closely related to elevation angle and relative location. Although elevation angle and relative location could be estimated by terrestrial device position and LEO ephemeris, maintaining these two information requires high cost for most IoT devices. Due to regular and high-speed orbital movement of LEO, for an arbitrary terrestrial device, LEOs repeat the same behavioral pattern (e.g., satellite rise and satellite set) again and again. Satellite rise and set result in different temporal correlations in elevation angle and relative location. By the aid of the temporal correlations mentioned above, we proposed a CSI prediction scheme for satellite-terrestrial networks without terrestrial device position and LEO ephemeris. Simulation results show that our scheme has rather low prediction error under various LEO altitude/obit, terrestrial device location/mobility, ground environments, weather and elevation angle when handover.

Intelligent Spectrum Management for Future Aeronautical Communications

The emergence of new aerial vehicles into the airspace and the continued growth of aviation operations will result in an increased demand for voice and data communications throughout the National Airspace System (NAS). However, recent studies have shown that the continued use of existing aviation spectrum allocations will not meet the anticipated needs of future aerospace applications; as a result, a new approach to aviation spectrum management is needed to support the forecasted needs of new airspace users. The National Aeronautics and Space Administration (NASA) is investigating the use of modern advancements in wireless communications, artificial intelligence (AI), machine learning (ML), and other advanced concepts in order to implement a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace. This technical investigation considers various airspace communications configurations, including air-ground and air-air communications, and can be applied to the existing air traffic control (ATC) system, as well as to future applications, such as the emerging Advanced Air Mobility (AAM) initiative. To support the evaluation of the proposed concepts and technologies, a modeling and simulation capability is currently under development, which will continue to evolve in support of new and advanced airspace applications. It is anticipated that this proposed spectrum management concept will allow for increased spectrum utilization efficiency and enhanced airspace capacity, which will better serve the needs of future NAS applications.

OPTIMAL SIGNAL-CODE CONSTRUCTIONS FOR INCREASE EFFICIENCY OF 5G AND 6G MOBILE INFOCOMMUNICATION RADIO SYSTEMS

The article discusses methods of increasing the efficiency of information communication radio systems of mobile communication, which includes increasing spectral efficiency and energy efficiency when used for telecommunication radio equipment. A comparison of block error coefficient values for f-OFDM, W-OFDM and OFDM methods with mixed numerology of uplink and downlink subscriber channels of access to information resources of the telecommunications network was made. As a result of the study of compression methods using several carriers, the dominant compression method for mobile communication systems, in particular, the CP-OFDM method due to its high efficiency of spectrum use, scalability and flexibility due to the introduction of a cyclic prefix, has been determined. It is proved that the CP-OFDM compression method is free from the Doppler range shift problem, thus the range and Doppler shift estimations can be considered as independent tasks in CP-OFDM. In addition, CP-OFDM parameters such as carrier distance, guard interval size, frame length, and pilot shape can be tailored to optimize the performance and robustness of detection and data transmission. However, such advantages are based on perfect synchronization (time and frequency) between the transmitter and receiver – although perfect synchronization may not be possible, especially for bistatic scanning, where the transmitter and receiver of the scan signal are not adjacent. In this case, the cyclic prefix may not provide any advantage for quality scanning, and multiple unprefixed waveforms may be considered. The main disadvantage of rejecting the prefix is the difficulty of data detection (due to intersymbol interference), which must be eliminated. Another important challenge for scanning, where energy efficiency is extremely important, is the large PAPR of multi-carrier (prefixed or unprefixed) signals. Single-carrier compression methods, which are based on the expansion of the code area of common radar signals and communication, in which the radar characteristics are affected by sequence autocorrelation, have been studied. However, the long spreading code, which leads to good autocorrelation, reduces the spectrum utilization efficiency for communication systems. In this case, the estimation of the Doppler effect is not trivial and requires more complex algorithms. The justification of the choice of the signal modulation scheme in 5/6G radio systems has been made.

Spectrum sensing-focused cognitive radio network for 5G revolution

The cognitive radio network (CRN), an instrumental part of the next-generation wireless communication systems, is mainly dependent on spectrum sensing to function properly. The radio spectrum can help in clean energy transition and load capacity factors by providing a more efficient and accurate spectrum utilization. By using it, the number of spectrums that is used for communication can be optimized, which can reduce the amount of energy consumed by the network. Additionally, 5G radio spectrum sensing can be used to identify and classify different types of signals, which can help reduce the amount of interference in the network and improve the efficiency of energy utilization. It can also allow for the digitization of clean energy infrastructure and facilitate better decision-making processes that take into account environmental impacts while optimizing energy use because of its efficient characteristics like non-linearity, detection, scalability, robustness, generalization, non-stationarity in wireless environments, dynamic entity, weighted sum of Gaussian functions centered at specific frequencies, and robustness against noise and interference. It can adapt to noise and interference by adjusting its parameters, and this allows accurate distinguishing between primary and secondary users in the wireless spectrum, which is why a radial basis function is a popular choice for spectrum sensing in 5G networks. Radial basis function networks (RBFNs) can work better in 5G spectrum sensing for better signal classification, dynamic adaptation, fast detection, faster decision-making, and improved noise and interference reduction. One of the most sought-after goals in the field of wireless research is the creation of spectrum-sensing technology that is dependable and intelligent because multilayer learning approaches are inappropriate for dealing with time-series data due to the higher misclassification rate and inherent computational complexity. To address this, the study proposed the radial basis function network that learns the temporal aspects from spectral data and makes use of additional environmental statistics such as frequency, efficiency, energy, spectrum allocation, distance, and duty-cycle time, which are considered environmental data that may be used to fine-tune sensor performance. The scheme is simulated with real-time parameters, and the results are quite promising in terms of evaluation parameters.

A Multi-Blockchain Scheme for Distributed Spectrum Sharing in CBRS System

To overcome the spectrum scarcity issues, the citizens broadband radio service (CBRS) presents a centralized spectrum management solution. The efficiency of spectrum utilization could be further improved by introducing spectrum trading. Blockchain-based spectrum trading has been considered as a decentralized, flexible, and secure approach. However, current studies rarely investigate the interference to incumbent users caused by spectrum trading between CBRS devices (CBSDs), and the scalability issues in blockchain-based spectrum trading are rarely discussed. To address the problems above, this paper develops the blockchain-based spectrum trading mechanisms for CBRS. Particularly, we propose a queuing mechanism for intra-coexistence group (CxG) trading, in which spectrum trading is leveraged to reduce the aggregated interference to incumbents. A new parameter termed “network feature” is proposed to prioritize spectrum transactions in different queues, which helps to achieve the trade-off between the interference to incumbents and resource requirements in spectrum transactions. Furthermore, we propose a multi-blockchain architecture and a corresponding cross-chain mechanism to improve the speed of inter-CxG spectrum trading. Simulation results show that the aggregated interference to incumbents can be reduced by adopting the proposed method. Meanwhile, when adopting the proposed cross-chain spectrum trading mechanism, the network throughput can be improved by up to 24% compared with the traditional CBRS spectrum management framework.

Harvesting Plasmonic Near-Infrared Photons by Hot Hole Transfer in Nonstoichiometric-Semiconductor Plasmonic Heterojunctions

Infrared light is an untapped energy source that accounts for half of solar energy. Thus, harvesting near-infrared photons for different applications needs further development for efficient utilization of the whole solar spectrum. Herein, we report harvesting infrared photons by hot hole transfer from a nonstoichiometric plasmonic semiconductor (Cu2–xSe) to an adjacent semiconductor (CdSe) in the heterostructure (HNCs). We found a decrease in transient signal intensity and hole–phonon scattering time in Cu2–xSe/CdSe HNCs compared to the pure Cu2–xSe nanocrystals, attributed to plasmon-induced hot hole transfer. A similar kind of TA kinetics has been observed for 800 and 930 nm laser pulse excitation for Cu2–xSe/CdSe HNCs. The spectroscopic results reveal anomalous carrier populations and slow carrier recovery dynamics at the 1P state of the CdSe phase in Cu2–xSe/CdSe HNCs, further establishing the plasmonic hot hole transfer mechanism. The estimated plasmon-induced hot hole transfer time from the nonstoichiometric semiconductor Cu2–xSe NC system is determined and found to be ∼177 fs. Fabricated plasmonic thin-film devices exhibit an excellent conductivity (0.15 A at 2 V) and photoconductivity (0.2 A at 2 V) under near-infrared light illustration (λlight = 1200 nm) in a plasmonic HNC system. These results reveal a novel approach for harvesting light photons from the visible to near-infrared region of the solar spectrum and provide a new avenue for developing hot carrier-based device applications of plasmonic semiconductor-based nanomaterials.

Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction

The advent of the Internet of Things and 5G has further accelerated the growth in devices attempting to gain access to the wireless spectrum. A consequence of this has been the commensurate growth in spectrum conflict and congestion across the wireless spectrum, which has begun to impose a significant impost upon innovation in both the public and private sectors. One potential avenue for resolving these issues, and improving the efficiency of spectrum utilisation can be found in devices making intelligent decisions about their access to spectrum through Dynamic Spectrum Allocation. Changing to a system of Dynamic Spectrum Allocation would require the development of complex and sophisticated inference frameworks, that would be able to be deployed at a scale able to support significant numbers of devices. The development and deployment of these systems cannot exist in isolation, but rather would require the development of tools that can simulate, measure, and predict Spectral Occupancy. To support the development such tools, this work reviews not just the available prediction frameworks for networked systems with sparse sensing over large scale geospatial environments, but also holistically considers the myriad of technological approaches required to support Dynamic Spectrum Allocation.

Optimization of Spectrum Utilization Efficiency in Cognitive Radio Networks

Cognitive radio (CR) is considered as a key technology to overcome the problem of spectrum scarcity for wireless applications by the researchers. Efficient spectrum utilization is the most important and core purpose of CR systems after successful spectrum sensing. In this letter, different cases are investigated in which secondary users (SUs) can transmit their data after declaring the primary user (PU) is absent as a result of spectrum sensing. SU’s transmission in some of these cases may lead to interference in PU’s communication, which may cause inefficient spectrum utilization. In this letter, an optimization problem is formulated to optimize the sensing time that maximizes the spectrum utilization efficiency (SUE) under the constraints of high target detection probability and low probability of interference to PU. A trade-off between sensing time and spectrum utilization efficiency is shown in a given time frame for all possible cases. Theoretical and simulation results of proposed system show good match to each others. Finally proposed system is compared with conventional systems and 45% better performance in terms of optimal sensing time is achieved with maximum SUE.

Dynamic and Efficient Spectrum Utilization for 6G With THz, mmWave, and RF Band

The number of wireless communication devices has grown exponentially with the evolution of wireless systems. Moreover, the development of technologies such as the Internet of things (IoT) and massive machine-type communications (mMTC) are also fueling the rapid increase in the number of communication devices. It is anticipated that through 6G, the wireless communication networks will move in the direction of ultra-dense distribution of enormous devices with assorted service and rate requirements. This requires a huge network energy, capacity, and efficient deployment of the spectrum. This paper employs the massive multi-input multi-output (mMIMO) and non-orthogonal multiple access (NOMA) techniques that increase the multiplexing gain and network capacity. This paper utilizes the benefits of millimeter Wave (mmWave) and Terahertz (THz) channels that poseess considerably higher communication capacity. However, these schemes are highly complex. To mitigate this problem, we use BS caching as diverse applications in 6G will have varied rate requirements. In addition, we propose an application-specific channel selection (ASCS) scheme with a cache-enabled two-tier 6G Heterogeneous Network (HetNet). In the proposed scheme, bestowing to the application requirement of the small cell users, the small cell base station (SBS) switches downlink channels dynamically. The simulation results illustrate that the proposed ASCS scheme can achieve higher spectral efficiency, energy efficiency, and throughput for the cache-enabled two-tier 6G HetNet.

Exploiting Short Block and Concatenated Codes for Reliable Communications Within the Coexistence of 5G-NR-U and WiFi

Unlicensed spectrum offers opportunities for cellular mobile network operators, where traffic can be offloaded from licensed to unlicensed bands. Modern heterogeneous technologies such as 5G and WiFi can simultaneously operate on ISM and UNII bands under strict coexistence rules. While satisfying harmonious coexistence has largely been studied in LTE/4G, an emerging issue for future deployment is to reduce latency and to guarantee reliable communications, in the direction of achieving URLLC. This work mainly focuses on the critical issues arising in the coexistence of LBT systems, sharing common infrastructure of 5G NR-U and WiFi when operating under imperfect energy detection sensing. We consider the application of short, concatenated and product erasure correcting codes to recover missing data due to collisions in LBT-based systems. The goal is to enhance spectrum utilization with reduced delay and to achieve reliable communications under various sensing impairments, with code parameters such as block length, rate and minimum distance. Using concatenation, we construct a series of sophisticated erasure block codes with less decoding complexity. Furthermore, we derive a closed-form expression using Gaussian approximation of spectrum utilization efficiency. The results are compared with the various LDPC correcting ensembles under various levels of user activities. We provide detailed comparisons of global throughput and failure probability of codes under varied number of coexisting users. Our results show that the proposed codes can achieve over 20% higher efficiency at 0.1 false alarm probability and 30% less failure probability under high contention scenarios.