MU‐MIMO Downlink Capacity Analysis and Optimum Code Weight Vector Design for 5G Big Data Massive Antenna Millimeter Wave Communication

Abdo, Adam Mohamed Ahmed; Zhao, Xiongwen; Zhang, Rui; Zhou, Zhenyu; Zhang, Jianhua; Zhang, Yu; Memon, Imran

doi:10.1155/2018/7138232

Cited by 21 publications

(10 citation statements)

References 23 publications

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“…Channel information is always important and becomes more difficult when fewer RF chains are used. Therefore techniques like power iterative adaptation [59] can be deployed, which when added to the benefit of mmWave sparsity allows channel estimation just by a few measurements. The disadvantage of this technique is the codebook burden that requires signaling and reconfiguration in every coherence time.…”

Section: Beamformers At Mmwavementioning

confidence: 99%

Beamformer development challenges for 5G and beyond

Abbasi

Fusco

2020

Antennas and Propagation for 5G and Beyond

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Antenna's beamforming technology [1] is vital in the utilization of the microwave and millimeter-wave (mmWave) frequency bands for the fifth-generation (5G) communication technology, and beyond, applications that include the sixth generation (6G), Internet of Things and Industry 4.0 [2,3]. Antenna beamforming can be defined as the ability of an antenna array to steer the maximum radiation toward a prescribed direction, or, conversely, the ability of the antenna array to estimate the direction of arrival (DOA) of an impinging signal. Beamforming is generally achieved by using multiple antennas, spatially colocated to either function as an independent radiator or as a unit cell in larger array arrangements. Recently, the term multiple-input-multiple-output (MIMO) has been used in conjugation with the beamforming technology since it also involves multiple antenna systems, thus linking it to the beamforming technology [4]. When the number of antennas used in the MIMO operation becomes very large, it is generally termed massive MIMO technology [5]. Different beamforming concepts are used for the two frequency spectrum classifications in cellular technology, i.e., sub-6 GHz and mmWave. The majority of beamformer challenges at sub-6 GHz are already resolved, and prototypes are now available [6]; however, there are still major fundamental challenges that engineers face while developing beamformers for use in the mmWave bands, i.e., >28 GHz. Challenges include, but are not limited to, high path loss, power generation, adaption to account for realistic channel estimation, hardware impairments, energy leakage in circuits, high development cost, spatial-temporal channel variations, signal blockages due to the presence of obstacles such as beamformer casing, user body and hand. Also, multiuser MIMO techniques are not easily implementable in mmWave frequencies. In addition to this, beam coordination at transmitter and receiver antenna systems especially in mmWave spectrum is very challenging. In this chapter, we will first classify the beamformers based on the system architecture, operational frequency

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Section: Beamformers At Mmwavementioning

confidence: 99%

Beamformer development challenges for 5G and beyond

Abbasi

Fusco

2020

Antennas and Propagation for 5G and Beyond

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“…[12]. Security of the wellbeing messages may be accomplished by authentication [13]. Ensuring security for data transmission and storage became one of the biggest concerns and challenges of IoT [14].…”

Section: Introductionmentioning

confidence: 99%

“…In [10], framework disintegration strategies on a psychological oppressor investigation framework have been proposed. In [13], deterioration techniques are involved and additionally utilized in a basic segment methodology proposed to partition information into numerous networks. In [15], Memon et al proposed highlight determination and its essentialness for breaking down the information later on.…”

Section: Introductionmentioning

confidence: 99%

Protect Mobile Travelers Information in Sensitive Region Based on Fuzzy Logic in IoT Technology

Memon

Shaikh

Hasan

et al. 2020

Security and Communication Networks

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The Internet of Things (IoT) is susceptible to several identities, primarily based on attacks. However, these attacks are controlling for IoT due to extraordinary growth in consumers’ density and slight analysis with low power access nodes. In this work, we explore the possible flaws associated with security for IoT environment insensitively meant for transfer conditions. We proposed a novel design aimed at detecting a spoofing attack that inspects the probability distributions of received power founded for the regions designed for mobile (moving) users. Additionally, we examine the influence on the Confidentiality Scope of targeted consumers in the absence and presence of observer. Our approaches were done through simulation results used for three diverse regions. Grounded on outcomes, we suggest an algorithm called MTFLA, which will guarantee detection and protection techniques intended to protect vastly sensitive areas, i.e., wherever the chance of an attack is maximized. We provide a comparison among various security algorithms prepared for the energy consumption of different patterns. Simulation results revealed that the proposed algorithm for protection (MTFL) is verified to be energy-proficient (secure garnering). It decreases the energy prerequisite for encrypting the data. We evaluated our techniques over simulation results for sensitive region information built on fuzzy logic.

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“…3) User Equipment throughput: For the MU mode, we consider the Block Diagonal (BD) precoding technique [10] because the BD precoder is focused on interference management and therefore limits the reduction of the bit rate due to the intracell interference. The bit rate for UE i is given by [11] :…”

Section: Introductionmentioning

confidence: 99%

Study of MIMO Channel Matrices Correlation to Optimize Resource Allocation Algorithms in Multi-Users 5G

Manini

Gueguen

Legouable

et al. 2019

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)

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Resource allocation is a major research field in wireless networks. The main challenge is to find the most suitable user for each time and frequency resource to reach the quality of service requirements. The necessity to always increase the capacity of wireless networks leads to systems using more and more antennas. Namely, the use of Massive-MIMO (Multiple Input Multiple Output) allows to simultaneously schedule several users on the same frequency resource. This is a new paradigm for resource scheduling. In this context, resource allocation algorithms have to include in their decision mechanism the choice of users association on the same time and frequency resource. A possible strategy, seen in the state of the art, is to base this user grouping on their channel-matrix correlation to minimize the interference level. In this paper we show the limited use of this approach in a system using block diagonal precoder.

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MU‐MIMO Downlink Capacity Analysis and Optimum Code Weight Vector Design for 5G Big Data Massive Antenna Millimeter Wave Communication

Cited by 21 publications

References 23 publications

Beamformer development challenges for 5G and beyond

Beamformer development challenges for 5G and beyond

Protect Mobile Travelers Information in Sensitive Region Based on Fuzzy Logic in IoT Technology

Study of MIMO Channel Matrices Correlation to Optimize Resource Allocation Algorithms in Multi-Users 5G

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