scholarly journals Forward-Reverse Orthogonal Matching Pursuit-Union-Subspace Pursuit-Based Multiuser Detector for Uplink Grant-Free NOMA Networks

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 125
Author(s):  
Olutayo Oyeyemi Oyerinde
Keyword(s):  
Communication Networks ◽  
Matching Pursuit ◽  
Testing Procedure ◽  
Base Station ◽  
Orthogonal Matching Pursuit ◽  
Wireless Communication Networks ◽  
Support Set ◽  
Multiuser Detector ◽  
Complexity Cost ◽  
The Cost

Multiuser Detection (MUD) is quite challenging in uplink grant-free non-orthogonal multiple access wireless communication networks in which users sporadically transmit data. The reason for this is that the base station (BS) must perform detection of both multiuser activity and user signals concurrently, because knowledge of user activity status is not available at the BS. In this paper, a new multiuser detector, named the Forward-Reverse Orthogonal Matching Pursuit–Union–Subspace pursuit (FROMPUS)-based MUD, is proposed. The detector takes advantage of the concept of an initial support set. This serves as initial knowledge that is then employed in the reconstruction of active users’ signals. In addition, the detector uses the “serial-include” technique of incorporating a likely support set element candidates and a reliability testing procedure in which the most prominent elements of the support set are selected. To assess the performance of the proposed detector, computer simulations are performed. The results obtained for various parameter settings show that the FROMPUS performs better than any of the other five detectors considered in this paper. However, this excellent performance comes with a slightly higher computational complexity cost. Nonetheless, the cost is inconsequential, since the detector operates at the BS where complexity is of low priority in comparison to performance.

Multiuser Detection schemes based on families of OMP and MUSIC algorithms for Uplink Grant free NOMA Wireless communications systems

2020 ◽  
Vol 10 ◽  
Author(s):  
Olutayo O. Oyerinde
Keyword(s):  
Communication Networks ◽  
Matching Pursuit ◽  
Base Station ◽  
Wireless Communication Networks ◽  
Multiple Measurement Vectors ◽  
Multiuser Detector ◽  
Access Scheme ◽  
Successful Recovery ◽  
Multi Access ◽  
Wireless Communications Systems

The non-orthogonal multiple access (NOMA) technology is a multi-access scheme that overcomes most of the disadvantages of its predecessor, the OMA technology. Specifically, NOMA technology supports massive connectivity of multiple users by employing the same non-orthogonal spectrum resource. In an uplink NOMA system with grant-free transmission mode, the base station (BS) is unaware of which users are active in the networks at a given time. Consequently, there is a need for mechanism to ensure successful recovery of users’ transmitted signals. This paper presents some new multiuser detector (MUD) schemes for uplink grant-free NOMA wireless communication networks with system’s model involving multiple measurement vectors (MMV) rather than the single measurement vector (SMV) that many previous works have considered. These MUDs include those that are based on differential orthogonal matching pursuit (OMP), adaptive simultaneous OMP (SOMP), compressive-multiple signal classification (MUSIC), and sequential compressive-MUSIC algorithms. The MUDs are employed in the detection of users’ signals in the uplink NOMA systems. Comparative performances of these MUDs with another one that is also based on the MMV system model, the SOMP-based MUD are presented for the scenarios when the system is under-loaded, fully loaded and over-loaded. The results suggest that the sequential compressive-MUSIC-based MUD, though shows weak performance at lower range of SNR, outperforms all the other MUDs including the SOMP-based MUD at higher SNR. Its performance is quite outstanding during the over-loaded scenarios, especially at higher SNR. However, its computational complexity is higher that the closely performing compressive-MUSIC-based MUD and SOMP-based MUD.

scholarly journals A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 231 ◽  
Author(s):  
Hanfei Zhang ◽  
Shungen Xiao ◽  
Ping Zhou
Keyword(s):  
Energy Distribution ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Selection Criterion ◽  
Critical Factor ◽  
Maximum Energy ◽  
Orthogonal Matching Pursuit ◽  
Support Set ◽  
Reconstruction Efficiency ◽  
Matching Pursuit Algorithm

The signal reconstruction quality has become a critical factor in compressed sensing at present. This paper proposes a matching pursuit algorithm for backtracking regularization based on energy sorting. This algorithm uses energy sorting for secondary atom screening to delete individual wrong atoms through the regularized orthogonal matching pursuit (ROMP) algorithm backtracking. The support set is continuously updated and expanded during each iteration. While the signal energy distribution is not uniform, or the energy distribution is in an extreme state, the reconstructive performance of the ROMP algorithm becomes unstable if the maximum energy is still taken as the selection criterion. The proposed method for the regularized orthogonal matching pursuit algorithm can be adopted to improve those drawbacks in signal reconstruction due to its high reconstruction efficiency. The experimental results show that the algorithm has a proper reconstruction.

scholarly journals No-Regret Matching Game Algorithm for NOMA Based UAV-Assisted NB-IoT Systems

2021 ◽  
Vol 26 (1) ◽  
pp. 79-85
Author(s):  
Samar Shaker Metwaly ◽  
Ahmed. M. Abd El-Haleem ◽  
Osama El-Ghandour
Keyword(s):  
Communication Networks ◽  
Multiple Access ◽  
Learning Algorithm ◽  
Base Station ◽  
Throughput Capacity ◽  
System Capacity ◽  
Wireless Communication Networks ◽  
Total Rate ◽  
Small Base ◽  
Matching Game

NB-IoT is the standardized technology for machine type communication (MTC) in Long Term Evolution (LTE). NB-IoT can achieve IoT requirements nevertheless, it suffers a low rate and capacity. On the other hand, Unmanned aerial vehicles (UAV) and Non-Orthogonal Multiple Access (NOMA) are promising technology used to enhance the throughput, capacity, and coverage of wireless communication networks. In this paper, we propose a heterogeneous network scenario where a UAV small Base Station (UBS) is used to assist the LTE Macro Base Station (MBS) with the help of the Non-Orthogonal Multiple Access technique to solve the NB-IoT throughput and capacity issues. Matching game based no-regret learning algorithm is proposed to optimize the NB-IoT device association and using NOMA pairing at each base station to provide the maximum system total rate and capacity. Simulation results show that our proposed scheme increases the total rate of the system by 60% and the system capacity by at least 80%, compared to NOMA without UAV and the total rate and capacity of the system by 200% and 85% respectively, with OMA scheme.

Cell Planning for Base Station in Mobile Communication Using an Improved Genetic Algorithm

2013 ◽  
Vol 284-287 ◽  
pp. 2699-2703 ◽  
Author(s):  
Hung Jen Liao ◽  
Chun Hung Richard Lin ◽  
Kuang Yuan Tung ◽  
Ying Chih Lin ◽  
Cheng Fa Tsai ◽  
...  
Keyword(s):  
Communication Networks ◽  
Mobile Communication ◽  
Mobile Networks ◽  
Base Station ◽  
Base Stations ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Cell Planning ◽  
Planning Methods ◽  
The Cost

Cell planning problem is one of the most important issues in mobile communication networks. To tackle the problem, one should address the location management issue because it significantly affects the cost of cell planning in mobile networks. The partition of location areas is developed to minimize the total costs of considering user location and search operation simultaneously in cellular networks, which has been shown to be NP-complete and is commonly solved by metaheuristics in previous works. In this paper, we propose novel cell planning methods for base stations using genetic algorithms with initialization, local search, and particular mechanisms of area and cell crossovers. Several simulations are conducted on various cell networks with previous, random and real configurations. The simulation results reveal that our schemes are superior to the considered algorithms.

scholarly journals Intercell Interference Coordination through Limited Feedback

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Lingjia Liu ◽  
Jianzhong (Charlie) Zhang ◽  
Jae-Chon Yu ◽  
Juho Lee
Keyword(s):  
Wireless Communication ◽  
Communication Networks ◽  
Limited Feedback ◽  
Base Station ◽  
Base Stations ◽  
System Level ◽  
Wireless Communication Networks ◽  
Interference Coordination ◽  
Average Cell ◽  
Intercell Interference

We consider the applications of multicell transmission schemes to the downlink of future wireless communication networks. A multicell multiple-input multiple output-(MIMOs) based scheme with limited coordination among neighboring base stations (BSs) is proposed to effectively combat the intercell interference by taking advantage of the degreesoffreedom in the spatial domain. In this scheme, mobile users are required to feedback channel-related information to both serving base station and interfering base station. Furthermore, a chordal distance-based compression scheme is introduced to reduce the feedback overhead. The performance of the proposed scheme is investigated through theoretical analysis as well as system level simulations. Both results suggest that the so-called “intercell interference coordination through limited feedback” scheme is a very good candidate for improving the cell-edge user throughput as well as the average cell throughput of the future wireless communication networks.

scholarly journals Intelligent Resource Allocation Method for Wireless Communication Networks Based on Deep Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hancheng Hui
Keyword(s):  
Resource Allocation ◽  
Communication Networks ◽  
Base Station ◽  
Resource Control ◽  
Network Throughput ◽  
Base Stations ◽  
Wireless Communication Networks ◽  
Mobile Location ◽  
Small Base ◽  
And Task

In this paper, a deep learning approach is used to conduct an in-depth study and analysis of intelligent resource allocation in wireless communication networks. Firstly, the concepts related to CSCN architecture are discussed and the throughput of small base stations (SBS) in CSCN architecture is analyzed; then, the long short-term memory network (LSTM) model is used to predict the mobile location of users, and the transmission conditions of users are scored based on two conditions, namely, the mobile location of users and whether the small base stations to which users are connected have their desired cache states, and the small base stations select the transmission. The small base station selects several users with optimal transmission conditions based on the scores; then, the concept of game theory is introduced to model the problem of maximizing network throughput as a multi-intelligent noncooperative game problem; finally, a deep augmented learning-based wireless resource allocation algorithm is proposed to enable the small base station to learn autonomously and select channel resources based on the network environment to maximize the network throughput. Simulation results show that the algorithm proposed in this paper leads to a significant improvement in network throughput compared to the traditional random-access algorithm and the algorithm proposed in the literature. In this paper, we apply it to the fine-grained resource control problem of user traffic allocation and find that the resource control technique based on the AC framework can obtain a performance very close to the local optimal solution of a matching-based proportional fair user dual connection algorithm with polynomial-level computational complexity. The resource allocation and task unloading decision policy optimization is implemented, and at the end of the training process, each intelligent body independently performs resource allocation and task unloading according to the current system state and policy. Finally, the simulation results show that the algorithm can effectively improve the quality of user experience and reduce latency and energy consumption.

scholarly journals Reconfigurable intelligent surfaces assisted wireless communication networks: ergodic capacity and symbol error rate

2022 ◽  
Vol 25 (1) ◽  
pp. 358
Author(s):  
Dinh-Thuan Do ◽  
Chi-Bao Le
Keyword(s):  
Monte Carlo ◽  
Communication Networks ◽  
Error Rate ◽  
System Performance ◽  
Performance Metrics ◽  
Ergodic Capacity ◽  
Symbol Error Rate ◽  
Base Station ◽  
Wireless Communication Networks ◽  
Relaying System

By enabling reconfigurable intelligent surfaces (RIS), we can deploy intelligent reflecting signals from the base station to destinations. Different from traditional relaying system, RIS relies on programmable metasurfaces and mirrors to improve system performance of destinations. We derive the formulas of main system performance metrics such as ergodic capacity and symbol error rate (SER). Based on types of modulation, we need to demonstrate other parameters which make influence to system performance. We show analytically that the number of reflecting elements along with the transmit power at the source can improve system performance. Moreover, we check the exactness of derived expressions by matching Monte-Carlo with analytical simulations. Finally, we find the best performance can be achieved at specific parameters and results are verified by explicit simulations.

scholarly journals Data Ridden Pilots Based Estimation of Sparse Multipath Massive MIMO Channels Using Orthogonal Matching Pursuit

2019 ◽  
Vol 25 (4) ◽  
pp. 81-87 ◽  
Author(s):  
Babar Mansoor ◽  
Moazzam Islam Tiwana ◽  
Syed Junaid Nawaz ◽  
Abrar Ahmed ◽  
Abdul Haseeb ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Matching Pursuit ◽  
Minimum Mean Square Error ◽  
Research Work ◽  
Base Station ◽  
Orthogonal Matching Pursuit ◽  
Mean Square ◽  
Mimo Channels

Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology for the upcoming 5th generation (5G) of wireless communication networks. This research work presents a novel Compressed Sensing (CS) and Superimposed Training (SiT) based technique for estimating the sparse uplink channels in massive MIMO systems. The proposed technique involves arithmetic addition of a periodic, but low powered training sequence with each user’s information sequence. Consequently, separately dedicated resources for the pilot symbols are not needed. Moreover, to attain the estimates of the Channel State Information (CSI) in the uplink, the sparsity exhibited by the MIMO channels is exploited by incorporating CS based Orthogonal Matching Pursuit (OMP) algorithm. For decoding the transmitted information symbols of each user, a Linear Minimum Mean Square Error (LMMSE) based equalizer is incorporated at the receiving Base Station (BS). Based on the obtained simulation results, the proposed SiT-OMP technique outperforms the existing Least Squares (SiT) channel estimation technique. The comparison is done using performance metrics of the Bit Error Rate (BER) and the Normalized Channel Mean Square Error (NCMSE).

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