In this paper a low-density pairwise check(LDPC) coded three-way relay system is considered, where three user nodes desire to exchange messages with the help of one relay node. Since physical-layer network coding is a...In this paper a low-density pairwise check(LDPC) coded three-way relay system is considered, where three user nodes desire to exchange messages with the help of one relay node. Since physical-layer network coding is applied, two time slots are sufficient for one round information exchange. In this paper, we present a decode-and-forward(DF) scheme based on joint LDPC decoding for three-way relay channels, where relay decoder partially decodes the network code rather than fully decodes all the user messages. Simulation results show that the new DF scheme considerably outperforms other common schemes in three-way relay fading channels.展开更多
Network Coding (NC) is a recent technique which is used to improve the transmission data rate and the power efficiency. These goals are obtained by combining data together before transmitting them, resulting to less t...Network Coding (NC) is a recent technique which is used to improve the transmission data rate and the power efficiency. These goals are obtained by combining data together before transmitting them, resulting to less transmitted data that carry the same amount of information. NC research work over the physical layer and the upper layers are popular and needed to be more investigated. In this paper, we propose a practical system of large-number of connected multi-source network coding (LMSNC), at the physical layer that exploits the broadcast nature of the wireless channel, using the practical and bandwidth-efficient schemes decode-and-forward (DF) and then compare it with Amplify and Forward (AF). The theoretical analysis and the simulation results show the effect of the noise when it cumulates in AF system and how DF is solving this severe default. Moreover, we consider the MSNC for Small-number of connected sources (SMSNC) and the two-way communication setup where two users exchange their information over an intermediate network node (ideally called Base Station), as two reference cases to compare with. With SMSNC, the number of necessary downlink transmissions from the intermediate node to the users is reduced, and thus the throughput is increased. Simulation results obtained using high-performance non-binary turbo codes, based on Partial Unit Memory (PUM) codes (4, 2, 1, 4) and (8, 4, 3, 8);confirm that combining PUM Turbo Code (PUMTC) and NC in the proposed MSNC setup gives almost the same BER performance as that for SMSNC at the small number of processing steps mainly when PUMTC (8, 4, 3, 8) is performed, which is required to retrieve the received coded messages. In the scenario of AF, combining packets results to cumulate the noise, which justifies the reason we decided to increase the number of transmitted coded messages in the network, i.e., the BER performance improves when sending extra coded messages. Finally, the possibility for a trade-off among BER, data rate and the number of transmitted coded messages is shown for LMSNC through graphics and simulation results.展开更多
The existing physical-layer network coding(PNC) can be grouped into three generic schemes,which are XOR-based PNC,superposition-based PNC,and denoising-and-forward(DNFbased) PNC.Generally speaking,DNF-based PNC has be...The existing physical-layer network coding(PNC) can be grouped into three generic schemes,which are XOR-based PNC,superposition-based PNC,and denoising-and-forward(DNFbased) PNC.Generally speaking,DNF-based PNC has better performance of rate pair region compared with the other two schemes when the transmission is symmetric.When the transmission is asymmetric,its performance is degraded severely.However,superposition-based PNC does not have that limitation even if its rate pair region performance is inferior to that of DNF-based PNC and XOR-based PNC.In this paper,we focus on the combined use of the two PNC schemes,superposition-based PNC and DNFbased PNC,and present a novel PNC scheme called joint superposition and DNF physical-layer network coding(JSDNF-based PNC) as well as the information theory analysis of the achievable rate pair region.At the same time,in the proposed scheme,an adaptive power allocation factor is introduced.By changing the power factor,the system can adapt its rate pair region flexibly.The numerical results show that the proposed scheme achieves the largest rate pair region when the rate difference of two source signals is very large.At the same time,the support on asymmetric transmission is also an important profit of the scheme.展开更多
The Base Station (BS) or access point is the building block of wireless networks, so, we propose exploiting it together with the Network Coding (NC) principle. NC suffers from the complexity of the decoding processes,...The Base Station (BS) or access point is the building block of wireless networks, so, we propose exploiting it together with the Network Coding (NC) principle. NC suffers from the complexity of the decoding processes, i.e., complicated Jordan Gaussian Elimination (JGE) processes. So, this paper proposes a deterministic NC algorithm to reduce the number of sequential network decoding steps, and hence minimizing the complexity of JGE process resulting to better time delay and processing time. We propose an algorithm that combines higher number of the transmitted packets resulting to better data-rate but worse Bet Error Rate (BER). However, using such strong Forward error correction channel code, which is Partial Unit Memory Turbo Code (PUMTC) results to minimize the losses in the BER to a very acceptable lever, in fact, in Decode-and-Forward (DF) BS, the BER can be regarded as minimum. Simulation results, for both Amplify-and-Forward (AF) and DF BS schemes using PUMTC based on (8, 4, 3, 8) component codes, confirm that using PUMTC mitigates the problem of noise aggregation resulting from applying NC in the proposed schemes.展开更多
In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned...In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.展开更多
Providing efficient packet delivery in vehicular ad hoc networks （VANETs） is particularly challenging due to the vehicle move- ment and lossy wireless channels. A data packet can be lost at a forwarding node even wh...Providing efficient packet delivery in vehicular ad hoc networks （VANETs） is particularly challenging due to the vehicle move- ment and lossy wireless channels. A data packet can be lost at a forwarding node even when a proper node is selected as the for- warding node. In this paper, we propose a loss-tolerant scheme for unicast routing protocols in VANETs. The proposed scheme employs multiple forwarding nodes to improve the packet reception ratio at the forwarding nodes. The scheme uses network coding to reduce the number of required transmissions, resulting in a significant improvement in end-to-end packet delivery ratio with low message overhead. The effectiveness of the proposed scheme is evaluated by using both theoretical analysis and computer sim-展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 61201187by the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions under Grant YETP0110+2 种基金by the Tsinghua University Initiative Scientific Research Program under Grant 20121088074by the Foundation of Zhejiang Educational Committee under Grant Y201121579by the Visiting Scholar Professional Development Project of Zhejiang Educational Committee under Grant FX2014052
文摘In this paper a low-density pairwise check(LDPC) coded three-way relay system is considered, where three user nodes desire to exchange messages with the help of one relay node. Since physical-layer network coding is applied, two time slots are sufficient for one round information exchange. In this paper, we present a decode-and-forward(DF) scheme based on joint LDPC decoding for three-way relay channels, where relay decoder partially decodes the network code rather than fully decodes all the user messages. Simulation results show that the new DF scheme considerably outperforms other common schemes in three-way relay fading channels.
文摘Network Coding (NC) is a recent technique which is used to improve the transmission data rate and the power efficiency. These goals are obtained by combining data together before transmitting them, resulting to less transmitted data that carry the same amount of information. NC research work over the physical layer and the upper layers are popular and needed to be more investigated. In this paper, we propose a practical system of large-number of connected multi-source network coding (LMSNC), at the physical layer that exploits the broadcast nature of the wireless channel, using the practical and bandwidth-efficient schemes decode-and-forward (DF) and then compare it with Amplify and Forward (AF). The theoretical analysis and the simulation results show the effect of the noise when it cumulates in AF system and how DF is solving this severe default. Moreover, we consider the MSNC for Small-number of connected sources (SMSNC) and the two-way communication setup where two users exchange their information over an intermediate network node (ideally called Base Station), as two reference cases to compare with. With SMSNC, the number of necessary downlink transmissions from the intermediate node to the users is reduced, and thus the throughput is increased. Simulation results obtained using high-performance non-binary turbo codes, based on Partial Unit Memory (PUM) codes (4, 2, 1, 4) and (8, 4, 3, 8);confirm that combining PUM Turbo Code (PUMTC) and NC in the proposed MSNC setup gives almost the same BER performance as that for SMSNC at the small number of processing steps mainly when PUMTC (8, 4, 3, 8) is performed, which is required to retrieve the received coded messages. In the scenario of AF, combining packets results to cumulate the noise, which justifies the reason we decided to increase the number of transmitted coded messages in the network, i.e., the BER performance improves when sending extra coded messages. Finally, the possibility for a trade-off among BER, data rate and the number of transmitted coded messages is shown for LMSNC through graphics and simulation results.
基金supported in part by National Natural Science Foundation of China under Grant No. 61071090Postgraduate Innovation Program of Scientific Research of Jiangsu Province under Grant No. CX10B -184Z
文摘The existing physical-layer network coding(PNC) can be grouped into three generic schemes,which are XOR-based PNC,superposition-based PNC,and denoising-and-forward(DNFbased) PNC.Generally speaking,DNF-based PNC has better performance of rate pair region compared with the other two schemes when the transmission is symmetric.When the transmission is asymmetric,its performance is degraded severely.However,superposition-based PNC does not have that limitation even if its rate pair region performance is inferior to that of DNF-based PNC and XOR-based PNC.In this paper,we focus on the combined use of the two PNC schemes,superposition-based PNC and DNFbased PNC,and present a novel PNC scheme called joint superposition and DNF physical-layer network coding(JSDNF-based PNC) as well as the information theory analysis of the achievable rate pair region.At the same time,in the proposed scheme,an adaptive power allocation factor is introduced.By changing the power factor,the system can adapt its rate pair region flexibly.The numerical results show that the proposed scheme achieves the largest rate pair region when the rate difference of two source signals is very large.At the same time,the support on asymmetric transmission is also an important profit of the scheme.
文摘The Base Station (BS) or access point is the building block of wireless networks, so, we propose exploiting it together with the Network Coding (NC) principle. NC suffers from the complexity of the decoding processes, i.e., complicated Jordan Gaussian Elimination (JGE) processes. So, this paper proposes a deterministic NC algorithm to reduce the number of sequential network decoding steps, and hence minimizing the complexity of JGE process resulting to better time delay and processing time. We propose an algorithm that combines higher number of the transmitted packets resulting to better data-rate but worse Bet Error Rate (BER). However, using such strong Forward error correction channel code, which is Partial Unit Memory Turbo Code (PUMTC) results to minimize the losses in the BER to a very acceptable lever, in fact, in Decode-and-Forward (DF) BS, the BER can be regarded as minimum. Simulation results, for both Amplify-and-Forward (AF) and DF BS schemes using PUMTC based on (8, 4, 3, 8) component codes, confirm that using PUMTC mitigates the problem of noise aggregation resulting from applying NC in the proposed schemes.
文摘In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.
基金supported in part by JSPS KAKENHI under Grant Number25730053
文摘Providing efficient packet delivery in vehicular ad hoc networks （VANETs） is particularly challenging due to the vehicle move- ment and lossy wireless channels. A data packet can be lost at a forwarding node even when a proper node is selected as the for- warding node. In this paper, we propose a loss-tolerant scheme for unicast routing protocols in VANETs. The proposed scheme employs multiple forwarding nodes to improve the packet reception ratio at the forwarding nodes. The scheme uses network coding to reduce the number of required transmissions, resulting in a significant improvement in end-to-end packet delivery ratio with low message overhead. The effectiveness of the proposed scheme is evaluated by using both theoretical analysis and computer sim-
