Using of Nanoparticles in treating of Hydatid Disease in Domestic Animals

The parasite Echinococcus granulosus is the cause of unilocular hydatid disease, which is a serious health risk to people and domestic animals worldwide. Livestock with hydatid disease suffers substantial financial losses due to the slaughterhouse's disapproval of the diseased animal parts, productivity losses (such as lowered live weight gain, milk yield, reproductive rates, and hide and skin value), and expenses related to caring for both humans and animals. Because of the parasite's complex life cycle and the difficulties associated with traditional treatment techniques, new strategies are needed to handle this crippling illness more successfully. In the treatment of hydatid illness, nanomedicine and nanoparticles have shown great promise, providing new approaches to medication distribution, focused therapy, diagnosis, and control measures. The possible roles and applications of nanomedicine and nanoparticles in treating hydatid illness in domestic animals are reviewed in this article. Owing to their distinct physicochemical characteristics at the nanoscale, nanoparticles enable tailored medication administration, enhancing anthelmintic agent potency while reducing systemic side effects. Therapeutic drugs like praziquantel or albendazole can be encapsulated in these nanoparticles, allowing for improved permeability and retention at the location of the parasite cysts. Additionally, imaging agents and diagnostic instruments at the nanoscale enable.  Additionally, nanotechnology offers avenues for developing innovative control measures, including environmental disinfection and targeted delivery of parasiticides. Collaborative efforts between researchers, veterinarians, and experts in nanotechnology are crucial to harnessing the full potential of nanoparticles and nanomedicine in effectively managing the infection in domestic animals.&nbsp

CQI-MCS Mapping for Green LTE Downlink Transmission

Maximizing the data rate and spectral efficiency are the main targets inLTE cellular systems. However, any increase in data rate or spectral efficiency willresult in an undesirable increase in energy consumption. Hence, both energyefficiency (EE) and spectral efficiency (SE) necessitate to be optimized. In this paper,a multi-objective optimization framework is formulated to prove the tradeoff betweenboth EE and SE in LTE downlink transmission. A new mapping algorithm betweenchannel quality indicator (CQI) and modulation and coding scheme (MCS) isproposed by using a multi-criteria decision making technique. The proposed CQIMCSmapping algorithm will search for the most suitable MCS according to thereported CQI value by considering the tradeoff between SE and EE while keepingblock error rate (BLER) below its threshold value. Simulation results have shown thatthe proposed mapping algorithm can provide an efficient compromise solutioncompared to the existing 3GPP-LTE mapping. Therefore, the proposed CQI-MCSmapping can optimize the EE and SE based on different mapping values depending onthe operator preferences

An adaptive threshold feedback compression scheme based on channel quality indicator (CQI) in long term evolution (LTE) system

Channel quality indicator (CQI) feedback in long-term evolution (LTE) system is an essential technique in describing the instantaneous channel state information. The CQI calculations highly depend on the accuracy of the channel estimation process in order to assign appropriate modulation and coding scheme. However, one of the critical issues affecting the LTE system performance is obtaining the CQI for each transmission period which will inevitably cost many resources. Therefore, an appropriate method for reducing CQI feedback overhead along with accurate channel estimation technique is required to manage the allocated resources and obtains significant improvements in system performance. In this paper, an adaptive threshold feedback compression scheme based on CQI scheme is proposed to obtain better system performance in terms of system throughput and error rate in LTE system. This proposed adaptive scheme dynamically adapts its threshold level to the signal to noise ratio variations, thus increasing the throughput and reducing the CQI feedback overhead. Results show that the proposed CQI based adaptive threshold feedback compression scheme enhances the tradeoff between system throughput and block error rate

Review of channel quality indicator estimation schemes for multi-user MIMO in 3GPP LTE/LTE-a systems

Multiple-in multiple-out (MIMO) in long-term evolution (LTE) is an essential factor in achieving high speed data rates and spectral efficiency. The unexpected growth in data rate demand has pushed researchers to extend the benefits of multi-user MIMO. The multi-user MIMO system can take full advantage of channel conditions by employing efficient adjustment techniques for scheduling, and by assigning different modulation and coding rates. However, one of the critical issues affecting this feature is the appropriate estimation of channel quality indicator (CQI) to manage the allocated resources to users. Therefore, an accurate CQI estimation scheme is required for the multi-user MIMO transmission to obtain significant improvements on spectral efficiency. This paper presents overviews of multi-user MIMO in LTE/LTE-advanced systems. The link adaptation, scheduling process, and different factors that affect the reliability of CQI measurements are discussed. State-of-the-art schemes for the post-processing CQI estimation, and the comparisons of various CQI estimation schemes to support multi-user MIMO are also addressed

An adaptive threshold based CQI compression scheme for LTE cellular networks

The frequency domain scheduling gain can be maximized when precise channel information is available at the eNodeB for the whole bandwidth. To compensate for the continuous variations of channel conditions in time and frequency domains, a huge undesirable amount of signaling overhead is required to report the channel quality indicator (CQI). On the contrary, partial channel state information detriments the downlink performances and does not guarantee the quality-of-service (QoS) when real-time multimedia services are applied. Thus, in this paper, the impact of CQI signaling overhead on the downlink performances is formulated. Then, an adaptive signal-to-interference-plus-noise ratio (SINR) threshold based CQI scheme is proposed by using multi-objective swarm intelligence to find the optimal feedback threshold. Based on an LTE system-level simulation, significant enhancements of 20% and 38% in throughput and packet loss ratio (PLR) respectively are obtained compared to a fixed threshold feedback with reasonable cost of feedback overhead. The proposed algorithm provides a high flexibility in responding to certain variations without complex modifications

A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system

In Multi User-Multiple-in Multiple-Out - Long Term Evolution (MU-MIMO-LTE) networks, Channel Quality indicator (CQI) plays a vital role. CQI is crucial in describing the channel information to assign appropriate modulation and coding scheme (MCS). However, obtaining CQI values for each transmission time interval (TTI) inevitably entails use and can lead to an undesirable degradation in spectral efficiency (SE) as well as increasing the error rate. Therefore, providing an accurate and reliable CQI with low overhead is an intricate task. In this paper, a CQI prediction scheme using Feed Forward-Neural Network (FF-NN) algorithm for MU-MIMO-LTE Advanced systems is proposed. Initially, a channel model for MU-MIMO-LTE advanced network is carried out. Through this model, CQI is predicted and the obtained values are compressed using a feedback compression technique. Finally, the proposed technique makes use of FF-NN algorithm to train and achieve enhanced CQI values. Further, an enhanced and accurate CQI values are acquired. Results show that the system SE of single user (SU)-MIMO proportionally increases with the SNR values at the cost of BER. Therefore, a MU-MIMO CQI prediction scheme is recommended to improve the tradeoff between BER and SE

Energy efficient transmission for LTE cellular system

With the introduction of Long Term Evolution (LTE) cellular networks, the spectral efficiency should be maximized according to the required high data rate. However, any increase in spectral efficiency will lead to an undesirable increase in power consumption, and thus lower energy efficiency will be obtained. Therefore, a new problem in radio resource allocation is addressed to manage the bandwidth and power resources according to LTE and Green communication requirements. So far, the main objective of the most radio resource allocation algorithms is to increase the data rate without much consideration of the energy or power metrics. Some of them had to minimize the power consumption at a given data rate requirements. In this paper, we propose a multi-objective radio resource function which aims to maximize energy efficiency at a given data rate requirement. This function will consider dynamic subcarrier assignment to assign different subcarriers to different users. A multiuser orthogonal frequency division multiplexing (OFDMA) represents the physical layer of LTE system along with a single transmitting antenna to transmit the data in a frequency flat fading channel. It has been shown that the transmission bandwidth and modulation order have impacts on the energy efficiency

Approximate linear minimum mean square error estimation based on channel quality indicator feedback in LTE systems

The fast fading channel produced by the fast user mobility requires a powerful channel estimation to report the most accurate channel status. Such estimation techniques are usualy suffer from large number of computational processes, and thus, their complexity needs to be minimized. However, the calculation reliability of channel coefficients depends mainly on the accuracy of the channel estimation model. Therefore, obtaining a joint optimized solution for channel estimation error, feedback overhead, and complexity is very crucial. In this paper, two different channel estimation schemes; Linear Minimum Mean Square Error (LMMSE) and Approximate Linear Minimum Mean Square Error (ALMMSE) are used to calculate Channel Quality Indicator (CQI) and Precoding Matrix Indicator (PMI) in the 3GPP-LTE fast fading channel. It is found that, by using a low-complexity ALMMSE, the estimation error is reduced with relatively small reduction in throughput. Therefore, the proposed method is recommended to be used when the network is not fully loaded for better tradeoff concerning MSE and throughput taking into account the fixed and mobility scenarios, and thus, reliable transmission will be targeted

A threshold feedback compression scheme of channel quality indicator (CQI) in LTE systems

The channel quality indicator (CQI) feedback is an essential technique in describing channel state information, especially at high-speed mobility for LTE and LTE-A systems. The number of reported CQI feedback increases when a large number of users are served by eNodeB. However, this feedback inevitably entails uses and can lead to severe degradation in system throughput. Therefore, an appropriate method for reducing CQI feedback overhead must be developed. In this paper, the system throughput and bit error rate (BER) are investigated by using a threshold feedback compression scheme of CQI at low and high user speeds. Results show that the system throughput proportionally increases with the threshold values at the cost of BER. Therefore, a high threshold level is recommended under high user speed conditions to improve the tradeoff between BER and throughput. In contrast, a low threshold level is targeted to provide reliable transmission when the network is fully loaded and the user is in a bad channel state

Energy-driven scheduling for Digital Video Broadcasting - Satellite Second Generation (DVB-S2)

The continuous growth in wireless data traffic results in the increase in total energy consumption of wireless networks. Therefore, energy-efficient solutions are extremely required to minimize the energy consumption over the entire network. In this paper, an energy-driven scheduling algorithm with optimized throughput, termed as Energy Efficiency Fair (EEF) queuing algorithm, is proposed. Based on energy efficiency of each modulation and coding scheme (MODCOD) available in Digital Video Broadcasting - Satellite Second Generation (DVB-S2), the EEF algorithm improves a scheduling mechanism of a two-step scheduler by selecting frames to be transmitted next according to “energy efficiency” policy developed. The EEF is compared with Round Robin (RR) algorithm, and a gain in energy efficiency of 47% is obtained when different modulation schemes with common code rate are implemented. Furthermore, EEF outperforms RR by 264% concerning the useful transmitted bits when QPSK modulation scheme with different coding rates is used