Network Coding in Wireless Communication

Network coding has great significance on wireless environment as the channel state is dynamic in wireless environment and wireless communication is of broadcast type. Network coding can be used in wireless environment for the optimal resource allocation and to reduce the interference with optimal power allocation to broadcasting nodes.

Opportunistic network coding for single hop as well as multi-hop network structure can be used which not only enhances the network throughput but also significant reduction in latency due to no queues in the nodes for the same generation of packets or messages. There might be queues for different generation or session and is still significantly shorter queue length compared to traditional store and forward mechanism which is routing in today's nodes.

Because of the throughput enhancement due to network coding and reduction in latency, Network coding technology can be one of the key candidate in next generation wireless communication that is 5G (IMT-2020).   

New Perspective over Information Flow

Information Theory

Information Theory has been introduced to represent source symbols randomly generated with encoding symbols accordingly how much information is carrying by the symbols. This equivalent representation minimizes the network loads by reducing number of flowing encoding symbols over the network and making the network non-degenerated. Random nature of a source or sender carries information and so there is existence of information theory. 

Network Coding Theory

There is another philosophy of generating a function which represents lots of information symbols with lesser number of information symbols in the same base field and the philosophy is Network Coding Theory. For digital representation of symbols, Network Coding Theory evaluate a functions which represents lots of information carrying binary base field symbols (bit) to lesser number of bits, encoding, which has capability of generating all information bits from the functions evaluated and the algorithm is decoding. 

International Mobile Telecommunication 2020 (IMT-2020) and Beyond

International Telecommunication Union kicked off global race towards next generation mobile Network IMT-2020 and beyond in early 2012. Major challenges in the next generation mobile communication network is enhanced spectral efficiency due to scares in frequency band standardized for mobile communication and green energy communication to reduce CO2 emission.

In user point of view, high speed data traffics and user following services are in demand. Higher user data traffic demands and lower energy consumption in network operations will be addressed with heterogeneous network deployment with densely deployed low power nodes which are used to transmit and receive user traffics and high mobility user demand and high probability interference within heterogeneous environments will be addressed with coordinated spatial signal processing in cloud and so the network structure is Heterogeneous Cloud Radio Access Network (H-CRAN). 

Micro-Base Stations (MBSs) are deployed for control signaling and seamless coverage and MBSs are high power nodes in H-CRAN. 

Network Information Theory and 5G

Once the LTE is introduced, Heterogeneous network is in existence with higher probability of interference since there is no static frequency planning rather dynamic. 

On the move to 5G, IMT-2020 cloud computing technique is introduced and large scale co-operative spatial signal processing will be done on cloud and one of the processing is network information coding and decoding. As spectral efficiency is one of the challenge in 5G, and the solution is H-CRAN which uses wide range of frequency bands and higher frequency bands also introducing severe interference in 5G. This network information processing could be promising key technology to battle against interference in next upcoming wireless communication 5G, which could be either inter-tier or intra-tier. 

Complexity In Information Theoretic Security

Information theoretic security study is based on minimal or no information leaked to malicious users. Secure network coding scheme could be simple but the study and optimality of the secure network coding is analysis is complex task. Complexity is due to the randomness and distribution of the source symbol though random key distribution can be modeled as per simplified coding scheme.

Secure Network Coding scheme not only promise to secure transmission but also challenge keep optimal information rate. Security level increases with the increasing random key field size and conversely information rate reduces. Hence optimality of secure network coding is realized very important study in Secure Network Coding theory. 

Secure Linear Network Code

According to secure network coding theory and information theoretic security to the network code, a linear network code with word length of (j+r), j-dimensional message and r-dimensional random key to be secured linearly, illegitimate users will obtain message of length lesser than j+r that is less information than a source message symbol can deliver to legitimate users.

For above network code, each network channel is to of unit capacity and the network code is linear. If a malicious user has access to r-channels then network code is r-secure network code. A network code is w-imperfectly secure if there r<w<j+r channels are accessible to malicious users. A network code is weakly secure if there is no randomness introduced to message and the malicious users get strictly less amount of message than j and the corresponding network code is efficient codes with optimal information rate. 

For linear and unit capacity network channels, entropy of a coded symbol is equivalent to j+r and that eavesdropped by malicious user is with w-channel tapped is equivalently to w, where w is in between r and j+r. 

Information Theory and Secure Network Codes

Information theory deals with the theory to generate replica of symbols at source nodes to sink nodes which defines the efficiency of the communication system.

Network coding theory deals with reliable transmission of symbol to sink nodes with reduced contamination and avoiding malicious tempering, spoofing and jamming by adversaries in the network.

Three approaches of security:

1. computational security 
2. physical security 
3. information theoretic security 

Secure Network coding deals with information theoretic security with no information gained by the wiretapper about the transmitting messages out of eavesdropped message. 

Shannon Ciphar system models sending of secrete key to destination through private channel without access of adversaries and message to sink node through the public channel, where without knowing secret key, adversaries gain no information about the source message. 

Another wiretap model sends secret message to sink nodes after encoding to code-word via noiseless channel and message symbol via public channels which could be tapped by adversaries.

Secret sharing model shares random secret symbols among the legitimate users and coded user message will be decoded with the secret symbol received by legitimate users and illegitimate user get no knowledge about the message transmitted over public channel without knowledge of secret message.