Simultaneous Wireless Information and Power Transfer using Green Communication Based on 5G Wireless Communication

Simultaneous wireless information and power transfer (SWIPT) has given new opportunities for dealing with the energe shortage problem in wireless networks.Green transmission for 5G cellular networks of mobile cloud access networks based on SWIPT is being examined. Considering SWIPT as a future potential solution for increasing the battery life, this technique improves energy efficiency (EE). One of the technologies is wireless communication to transfter the power used to give sufficient resources to energy-constrained networks that have consequences for 5G and the internet of things (IoT), energy efficiency, co-operative communication and suitable are supported by the SWIPT. To enhance the capacity, data rate improvement, and better performance of quality of services of further networks. In addition to these criteria, it is also our moral responsibility to protect the environment of wireless networks by lowering power usage. As a result, green communication is a critical requirement. We looked at a variety of strategies for power optimization in the impending 5G network in this article. The utilization of relays and microcells to enhance the network’s energy efficiency is the main focus. The many relaying scenarios for next-generation networks have been discussed.

EFL Reading Fluency Development and Its Effects

<p>Speed reading courses have been considered an effective method to improve learners' reading rate. Research in this area has concentrated on the effect of a speed reading course on students' speed improvement, but not on how to structure the course or the effects of speed improvement on other aspects of language and other types of reading. This thesis, in the first place, deals with the issue of scheduling a speed reading course, in terms of lesson frequency and course length, to achieve the best effect. The thesis also seeks to determine if speed development in the course leads to rate improvement in reading texts outside the course. Finally, the thesis looks at the effects of speed improvement on oral reading rate, language accuracy and language complexity. In the first of two experiments, a speed reading course was delivered to the four experimental groups, who followed the course on different scheduling. Four scoring methods were used to measure the participants' speed improvement and it was found that one group made smaller increases than the others in all scoring methods. A pre-test and a post-test for reading other types of texts were administered and the speeds on these texts by the four treatment groups were compared with those by the control group. The results demonstrated that all but one group from the treatment category outperformed the control group. The second experiment was both a replication of the first experiment in order to confirm the reliability of the first experiment's results and an expansion from the first experiment to explore other issues. It involved two control groups, one of which followed the usual English program at the university and two treatment groups, one of which received consultation sessions during the treatment. The results on speed increases within the speed reading course corroborate the findings in the first experiment. Reading rate transfer from the speed reading course to other texts was significant (p<.001). Comparisons within the treatment groups and within the control groups demonstrated that the usual English program did not noticeably affect the speed increase transfer to other texts, oral reading fluency improvement, or language memory span development, but the consultation sessions substantially affected speed improvement in the course and speed improvement on other types of texts. With respect to oral reading rate the experiment found that the difference between the control groups and the treatment groups was statistically significant (p<.05). The relationships between reading fluency, language accuracy, and language complexity were also explored by looking at the comprehension scores and memory span results. It was found that reading fluency improvement does not necessarily negatively affect comprehension. It, however, does not assist language accuracy development to a remarkable degree. More importantly, the experiment showed that the treatment groups considerably expanded their memory span, which implies that reading speed improvement facilitates language complexity. High correlations between speed increases in the speed reading course, reading rate improvement in other types of texts and memory span development were also found.</p>

EFL Reading Fluency Development and Its Effects

<p>Speed reading courses have been considered an effective method to improve learners' reading rate. Research in this area has concentrated on the effect of a speed reading course on students' speed improvement, but not on how to structure the course or the effects of speed improvement on other aspects of language and other types of reading. This thesis, in the first place, deals with the issue of scheduling a speed reading course, in terms of lesson frequency and course length, to achieve the best effect. The thesis also seeks to determine if speed development in the course leads to rate improvement in reading texts outside the course. Finally, the thesis looks at the effects of speed improvement on oral reading rate, language accuracy and language complexity. In the first of two experiments, a speed reading course was delivered to the four experimental groups, who followed the course on different scheduling. Four scoring methods were used to measure the participants' speed improvement and it was found that one group made smaller increases than the others in all scoring methods. A pre-test and a post-test for reading other types of texts were administered and the speeds on these texts by the four treatment groups were compared with those by the control group. The results demonstrated that all but one group from the treatment category outperformed the control group. The second experiment was both a replication of the first experiment in order to confirm the reliability of the first experiment's results and an expansion from the first experiment to explore other issues. It involved two control groups, one of which followed the usual English program at the university and two treatment groups, one of which received consultation sessions during the treatment. The results on speed increases within the speed reading course corroborate the findings in the first experiment. Reading rate transfer from the speed reading course to other texts was significant (p<.001). Comparisons within the treatment groups and within the control groups demonstrated that the usual English program did not noticeably affect the speed increase transfer to other texts, oral reading fluency improvement, or language memory span development, but the consultation sessions substantially affected speed improvement in the course and speed improvement on other types of texts. With respect to oral reading rate the experiment found that the difference between the control groups and the treatment groups was statistically significant (p<.05). The relationships between reading fluency, language accuracy, and language complexity were also explored by looking at the comprehension scores and memory span results. It was found that reading fluency improvement does not necessarily negatively affect comprehension. It, however, does not assist language accuracy development to a remarkable degree. More importantly, the experiment showed that the treatment groups considerably expanded their memory span, which implies that reading speed improvement facilitates language complexity. High correlations between speed increases in the speed reading course, reading rate improvement in other types of texts and memory span development were also found.</p>

Redefining Wireless Communication for 6G:Signal Processing Meets Deep Learning with Deep Unfolding

The year 2019 witnessed the rollout of the 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning approaches for enhancing wireless communications and empowering them with much-desired intelligence holds immense potential for redefining wireless communication for 6G. The evolving communication systems will be bottlenecked in terms of latency, throughput, and reliability by the underlying signal processing at the physical layer. In this position paper, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks. To this end, we begin by presenting the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning (DL) approaches in the context of 6G networks. Specifically, deep unfolded signal processing is presented by sketching the interplay between domain knowledge and DL. The deep unfolded approaches reviewed in this article are positioned explicitly in the context of the requirements imposed by the next generation of cellular networks. Finally, this article motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks.<br>

Redefining Wireless Communication for 6G:Signal Processing Meets Deep Learning with Deep Unfolding

The year 2019 witnessed the rollout of the 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning approaches for enhancing wireless communications and empowering them with much-desired intelligence holds immense potential for redefining wireless communication for 6G. The evolving communication systems will be bottlenecked in terms of latency, throughput, and reliability by the underlying signal processing at the physical layer. In this position paper, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks. To this end, we begin by presenting the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning (DL) approaches in the context of 6G networks. Specifically, deep unfolded signal processing is presented by sketching the interplay between domain knowledge and DL. The deep unfolded approaches reviewed in this article are positioned explicitly in the context of the requirements imposed by the next generation of cellular networks. Finally, this article motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks.<br>

Redefining Wireless Communication for 6G:Signal Processing Meets Deep Learning with Deep Unfolding

The year 2019 witnessed the rollout of the 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning approaches for enhancing wireless communications and empowering them with much-desired intelligence holds immense potential for redefining wireless communication for 6G. The evolving communication systems will be bottlenecked in terms of latency, throughput, and reliability by the underlying signal processing at the physical layer. In this position paper, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks. To this end, we begin by presenting the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning (DL) approaches in the context of 6G networks. Specifically, deep unfolded signal processing is presented by sketching the interplay between domain knowledge and DL. The deep unfolded approaches reviewed in this article are positioned explicitly in the context of the requirements imposed by the next generation of cellular networks. Finally, this article motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks.<br>