Noise Cancellation Employing Adaptive Digital Filters for Mobile Applications

The persistent improvement of the hybrid adaptive algorithms and the swift growth of signal processing chip enhanced the performance of signal processing technique exalted mobile telecommunication systems. The proposed Artificial Neural Network Hybrid Back Propagation Adaptive Algorithm (ANNHBPAA) for mobile applications exploits relationship among the pure speech signal and noise corrupted signal in order to estimate of the noise. An adaptive linear system responds for changes in its environment as it is operating. Linear networks are gets adjusted at each time step based on new input and target vectors can find weights and biases that minimize the networks sum squared error for recent input and target vectors. Networks of this kind are quite oftenly used for error cancellation, speech signal processing and control systems.    Noise in an audio signal has become major problem and hence mobile communication systems are demanding noise-free signal. In order to achieve noise-free signal various research communities have provided significant techniques. Adaptive noise cancellation (ANC) is a kind of technique which helps in estimation of un-wanted signal and removes them from corrupted signal. This paper introduces an Adaptive Filter Based Noise Cancellation System (AFNCS) that incorporates a hybrid back propagation learning for the adaptive noise cancellation in mobile applications. An extensive study has been made to explore the effects of different parameters, such as number of samples, number of filter coefficients, step size and noise level at the input on the performance of the adaptive noise cancelling system. The proposed hybrid algorithm consists all the significant features of Gradient Adaptive Lattice (GAL) and Least Mean Square (LMS) algorithms. The performance analysis of the method is performed by considering convergence complexity and bit error rate (BER) parameters along with performance analyzed with varying some parameters such as number of filter coefficients, step size, number of samples and input noise level. The outcomes suggest the errors are reduced significantly when the numbers of epochs are increased. Also incorporation of less hidden layers resulted in negligible computational delay along with effective utilization of memory. All the results have been obtained using computer simulations built on MATLAB platfor

Throughput Maximization by Adaptive Switching with Modulation Coding Scheme and Frequency Symbol Spreading

It is required to realize higher transmission rate and higher reliability for mobile communication due to the increase in Internet use. However, wireless channel capacity can not be used with maximum efficiency due to fluctuating channels affected by shadowing, multipath fading and mobility.Adaptive modulation and coding (AMC) scheme is now commonly implemented to maximize the throughput performance under the given link qualities. Forward Error Correction (FEC) based link adaptation is effective to improve throughput in a lower SNR regime, however, it immolates maximal throughput in good channel condition. Frequency symbol spreading (FSS) has been proposed that can improve BER even without FEC. It fully exploits the frequency diversity gain by spreading symbol per subcarrier to all frequency components. This paper proposes a new adaptation control scheme for OFDM by switching FSS and legacy AMC. Simulation result verifies its maximized throughput performance harvesting both of frequency diversity gain and coding gain

Throughput Maximization by Adaptive Switching with Modulation Coding Scheme and Frequency Symbol Spreading

It is required to realize higher transmission rate and higher reliability for mobile communication due to the increase in Internet use. However, wireless channel capacity can not be used with maximum efficiency due to fluctuating channels affected by shadowing, multipath fading and mobility.Adaptive modulation and coding (AMC) scheme is now commonly implemented to maximize the throughput performance under the given link qualities. Forward Error Correction (FEC) based link adaptation is effective to improve throughput in a lower SNR regime, however, it immolates maximal throughput in good channel condition. Frequency symbol spreading (FSS) has been proposed that can improve BER even without FEC. It fully exploits the frequency diversity gain by spreading symbol per subcarrier to all frequency components. This paper proposes a new adaptation control scheme for OFDM by switching FSS and legacy AMC. Simulation result verifies its maximized throughput performance harvesting both of frequency diversity gain and coding gain

Enhanced snr-based admission control algorithm for vehicular ad-hoc network

Vehicular Ad-hoc Network (VANET) becomes a fundamental subcategory of mobile ad-hoc networks that provides vehicles to communicate with each other and with roadside infrastructure smartly. Data traffic in VANET can be categorized into safety and non-safety, where safety is a very critical point and non-safety is related to entertainment. Various VANET performance challenges are considered in terms of Quality of Service (QoS) which cause performance degradation as performance anomaly where high rates of vehicles wait for the low rates of vehicle transmitting time and starvation problem where some vehicles cannot transfer their data. Three main achievements have been accomplished. Starting with the impact of the increasing vehicle speed on performance anomaly problem consequences has been investigated. Followed by high-speed effects on data delivery is illustrated and how 802.11p has outperformed 802.11 in terms of data delivery is also demonstrated. Lastly, starvation problem is investigated where results showed increased data loss when vehicle nodes unable to deliver data correctly. Finally, a QoS-aware Signal to Noise Ratio (SNR) admission control mechanism (QASAC) is proposed to handle the performance anomaly problem while maintaining the QoS levels for high and low traffics. This can result in wasting throughput and cause data loss. The investigation results show that 802.11p has enhanced the number of dropped packets up to 70%. Also, the 802.11p end to end delay has decreased up to 12% less than the results of the 802.11 MAC protocol. The packet delivery ratio has been enhanced by up to 41% by 802.11p. The starvation problem investigation phase shows that 802.11p perform better than 802.11 which mainly affected by the increased speed of the vehicle. QASAC assigned different SNR values to different vehicles group based on the sending SNR values and in each group. Unlike recently proposed admission control in VANET networks, the proposed architecture differentiate between both high priority and low priority traffic QASAC has been compared against the latest SNR based admission control mechanism. QASAC has enhanced the performance of data delivery up to 23% in terms of data dropping rates for high priority traffic