Joint Machine-Type Device Selection and Power Allocation for Buffer-Aided Cognitive M2M Communication

In this paper, a cognitive machine-to-machine (M2M) communication network is considered, in which a cellular network shares the spectrum with the M2M communication network with M machine-type devices (MTDs), one half-duplex relay, and one MTD gateway for data gathering. One key challenge is that in the future 5G wireless networks, there will be billions of those small MTDs, and therefore, a MTD selection protocol is required for managing data transmission between MTDs. A joint buffer-aided MTD selection and power allocation protocol is proposed to maximize the MTDs’ sum-rate provided that the induced interference to the cellular network is limited. In particular, in the proposed scheme, at each time slot and each subcarrier, the cognitive M2M network optimally decides on whether to be silent or to select either the relay or one of the MTDs for data transmission. To this end, for each MTD, there exists a buffer at the relay to avoid data loss. The closed-form expressions for the power coefficients of MTDs are calculated. Simulation results show that the proposed policy improves the sum-rate of the CM2M network in comparison with the other proposed schemes for M2M communication without buffe

Context aware medium access control for buffer-aided multichannel cognitive networks

In this paper, a novel context aware medium access control (MAC) scheme is proposed for multichannel buffer-aided cognitive networks. The proposed scheme allows management of the delay of the primary network (PN) and secondary network (SN) more efficiently by exploiting the packets' context. In the proposed multiple access policy, two different context aware approaches for packet prioritization are presented

Analyzing the Impact of Lockdown in Controlling COVID-19 Spread and Future Prediction

COVID-19 outbreaks are the critical challenge to the administrative units of all worldwide nations. India is also more concerned about monitoring the virus’s spread to control its growth rate by stringent behaviour. The present COVID-19 situation has huge impact in India, and the results of various preventive measures are discussed in this paper. This research presents different trends and patterns of data sources of States that suffered from the second wave of COVID-19 in India until 3rd July 2021. The data sources were collected from the Indian Ministry of Health and Family Welfare. This work reacts particularly to many research activities to discover the lockdown effects to control the virus through traditional methods to recover and safeguard the pandemic. The second wave caused more losses in the economy than the first wave and increased the death rate. To avoid this, various methods were developed to find infected cases during the regulated national lockdown, but the infected cases still harmed unregulated incidents. The COVID-19 forecasts were made on 3rd July 2021, using exponential simulation. This paper deals with the methods to control the second wave giving various analyses reports showing the impact of lockdown effects. This highly helps to safeguard from the spread of the future pandemic

Addressing the real world problem of managing wireless communication systems using explainable ai-based models through correlation analysis

In a general parlance, wireless communication tends to be investigated based on the available methods that support enhancing the optimized data link, especially the software-based methods. AI is mainly used to create and design efficient communication network systems and variable node locations. The major factors impacting wireless communications in the current context are enhanced channel frequency, efficiency of using the bandwidth, and modulation type. The software-defined ratio enables collecting the information and analyzing the overall signal-related components and processing them in real-time situations. This will support in detecting unnecessary information and identifying latency at each stage of communication. The study is intended to measure the influence of critical factors in enhancing the overall management of wireless communication systems through the application of AI technologies. The researchers used the questionnaire method in order to collect the data from the respondents and enable them to analyze the data using the SPSS data package.Campus At

Imperfect Channel Estimation and Its Effect on Uplink FDMA Resource Management

Abstract—In this paper, we investigate the capacity maximization problem for uplink spatial division multiple access (SDMA)/ single carrier frequency division multiple access (SC-FDMA), under imperfect channel conditions. In fact, we improve our previous work and introduce a pilot assisted estimation method along with a sub-optimal power projection method based on users ’ spatial correlation, which chooses the users for each resource block (RB). We assume a single-input multiple-output (SIMO) scheme with correlation at receive antenna. Numerical analysis verifies that the proposed scheme improves the SE considerably at a target defined fairness level. We also investigate the effect of variations of different system design parameters, i.e., RB size, number of receive antenna, etc, on the system spectral efficiency, when channel state information is not perfect