MIMO–OFDM communication systems for traffic data transmission in 5G drone small cells

Vahidi, Vahid; Saberinia, E.

doi:10.1049/iet-com.2018.6158

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…However, for the problems where the dimension of vector x is high, the classifier problem (2) may become complicated to be solved. Toward this issue, we can introduce the dual problem of ALGORITHM 1 Picker algorithm (2) in problem (3), and by applying the kernel trick, the original feature space can be mapped into to a higher-dimensional feature space where the training set could become more separable [30].…”

Section: Svm-based Learning Model and Its Solutionmentioning

confidence: 99%

“…In this paper, SVM algorithm is implemented by libsvm which consists of two parts (svm_train and svm_predict) and procedures including svm_save_model and svm_load_model etc. According to [32], both svm_train and svm_predict have the highest complexity of O(l 3 ), so the complexity of SBOS algorithm is O(l 3 ) where l is the number of input data in problem (2), that is, pairs of beam allocation schemes and corresponding labels. Recall that the termination criteria of SBOS algorithm are that the Picker (Algorithm 1) cannot pick out new samples or reaches the maximum iteration count ITER.…”

Section: Generate the Scheme Set For The Next Iteration (Line 31 Inmentioning

confidence: 99%

“…MmWave cellular systems are expected to be densely deployed to provide multi-Gbps links to support high data rate by using the small-size antennas [1][2][3]. Especially, multi-Gbps links will be necessary for future vehicle-to-everything (V2X) communications to support the intensive sensory data transmission for (semi)-autonomous driving or the augmented reality (AR) and virtual reality (VR) services for passengers [4,5].…”

Section: Introductionmentioning

confidence: 99%

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SVM‐based online learning for interference‐aware multi‐cell mmWave vehicular communications

Zhu

Zhao

et al. 2021

IET Communications

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This paper proposes a data-driven method of mmWave beam selection in multi-cell systems to achieve a near-optimal fast beam allocation with low complexity. In particular, an online learning algorithm based on support vector machine (SVM) equipped with the radial basis function kernel, namely SVM-based online beam selection (SBOS) algorithm is proposed. The proposed algorithm starts with an adaptive beam selection process for certain traffic pattern that uses an SVM learning model to adaptively refine the beam selection strategy. Specifically, SVM-based model labels the feedback (the average information rate) from the cellular system, then learns from samples, and makes the scheme space smaller by maximising samples' minimum distances to all labelled samples in the sample space constrained by newly learned boundaries. Then, according to the aggregated data about the traffic patterns and the performance of corresponding beam selection strategy, SBOS algorithm exploits beam selection schemes recorded in the database or explores new schemes for unknown situations, respectively, and how to tune the hyperparameters for the SBOS algorithm is discussed. Furthermore, the extensive simulation results show that the proposed algorithm achieves a better performance versus upper confidence bound and Random methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Svm-based Learning Model and Its Solutionmentioning

confidence: 99%

Section: Generate the Scheme Set For The Next Iteration (Line 31 Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SVM‐based online learning for interference‐aware multi‐cell mmWave vehicular communications

Zhu

Zhao

et al. 2021

IET Communications

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“…In recent years, fifth-generation (5G) and beyond 5G (B5G) technologies are being developed to increase the data rates and network capacity for conventional cellular networks. Multiple input multiple output (MIMO) and network traffic offloading techniques are used for achieving such objectives [1][2][3]. Equipping multiple antennas needs more hardware, cost, and size.…”

Section: Introductionmentioning

confidence: 99%

Drone assisted device to device cooperative communication for critical environments

2021

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This paper proposes drone assisted device-to-device cooperative communication (DA-DDCC) for critical situations during post-disaster management. The proposed network utilizes the autonomous mode of D2D communication for setting up the link in the absence of a central node. This network incorporates cooperative communication using drone in D2D session for improving reliability of the overall system. A probability-based statistical channel model for such networks is proposed by taking the statistical independence of links into consideration. Unlike the existing air-to-ground (A2G) channel models that use either Rayleigh or Rician distribution for uplink (UL) and downlink (DL) channel modelling, our approach takes the probability of occurrence of line of sight (LoS) into account while predicting the appropriate channel distribution for UL and DL separately. For performance evaluation of the proposed network, average outage probability and average capacity are derived using the proposed channel model. Monte Carlo simulations are conducted to verify our analysis. Moreover, a multi-cluster DA-DDCC scenario is also being analyzed through simulations from an interference perspective to justify the usefulness of the proposed channel model. Results obtained through this investigation can be utilized in selecting various crucial system parameters judiciously for enhanced performance during post-disaster scenario. 1 INTRODUCTION In recent years, fifth-generation (5G) and beyond 5G (B5G) technologies are being developed to increase the data rates and network capacity for conventional cellular networks. Multiple input multiple output (MIMO) and network traffic offloading techniques are used for achieving such objectives [1-3]. Equipping multiple antennas needs more hardware, cost, and size. Consequently, these methods are not preferred for most of the battery operated tiny devices. To enhance the cellular network performance, traffic offloading techniques are also used in literature. Traffic offloading may be achieved mainly by two methods, one is through small cell (micro/pico/femto) deployment in macro-cells and the other is using device-to-device (D2D) communications [4]. However, the first method may be inconvenient due to a lack of dynamism that occurs because of This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…With the development of artificial intelligence and communication technologies, the global mobile data traffic is expected to reach 77 exabytes per month by 2022 [1][2][3]. To meet these demands, various technologies have been proposed, such as full duplex (FD), millimetre wave (mmWave) communication, massive multiple-input multiple-output (MIMO), ultra-dense network (UDN), and intelligent reflecting surface (IRS) [4][5][6]. Due to the flexibility and low-cost, IRS stands out from these technologies and attracts tremendous researchers' interest.…”

Section: Introductionmentioning

confidence: 99%

Joint active and passive beamforming optimization for multigroup multicast system aided by intelligent reflecting surface

2021

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An intelligent‐reflecting‐surface aided multigroup multicast communication system is investigated in this paper. We develop energy‐efficient designs for both the precoding matrix at the base station and passive reflecting beamforming at intelligent‐reflecting‐surface, subject to the minimum signal‐to‐interference‐plus‐noise ratio constraint of each user and the unit‐modulus constraint imposed by passive intelligent‐reflecting‐surface. This leads to a complex non‐convex problem for which to tackle we propose two novel algorithms based on alternating optimization techniques. Specifically, one algorithm employs semi‐definite relaxation to obtain the suboptimal reflection coefficient vector and suboptimal precoding vector for each group. In order to reduce the computational complexity, the second algorithm transforms this non‐convex problem into two second‐order cone programming problems. Numerical results show that intelligent‐reflecting‐surface can significantly reduce the power consumption of the base station.

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MIMO–OFDM communication systems for traffic data transmission in 5G drone small cells

Cited by 4 publications

References 31 publications

SVM‐based online learning for interference‐aware multi‐cell mmWave vehicular communications

SVM‐based online learning for interference‐aware multi‐cell mmWave vehicular communications

Drone assisted device to device cooperative communication for critical environments

Joint active and passive beamforming optimization for multigroup multicast system aided by intelligent reflecting surface

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