Zero-error communication over networks

Zero-Error communication investigates communication without any error. By defining channels without probabilities, results from Elias can be used to completely characterize which channel can simulate which other channels. We introduce the ambiguity of a channel, which completely characterizes the possibility in principle of a channel to simulate any other channel. In the second part we will look at networks of players connected by channels, while some players may be corrupted. We will show how the ambiguity of a virtual channel connecting two arbitrary players can be calculated. This means that we can exactly specify what kind of zero-error communication is possible between two players in any network of players connected by channels.Comment: 10 pages, full version of the paper presented at the 2004 IEEE International Symposium on Information Theor

Negligible Cooperation: Contrasting the Maximal- and Average-Error Cases

In communication networks, cooperative strategies are coding schemes where network nodes work together to improve network performance metrics such as the total rate delivered across the network. This work studies encoder cooperation in the setting of a discrete multiple access channel (MAC) with two encoders and a single decoder. A network node, here called the cooperation facilitator (CF), that is connected to both encoders via rate-limited links, enables the cooperation strategy. Previous work by the authors presents two classes of MACs: (i) one class where the average-error sum-capacity has an infinite derivative in the limit where CF output link capacities approach zero, and (ii) a second class of MACs where the maximal-error sum-capacity is not continuous at the point where the output link capacities of the CF equal zero. This work contrasts the power of the CF in the maximal- and average-error cases, showing that a constant number of bits communicated over the CF output link can yield a positive gain in the maximal-error sum-capacity, while a far greater number of bits, even numbers that grow sublinearly in the blocklength, can never yield a non-negligible gain in the average-error sum-capacity

Negligible Cooperation: Contrasting the Maximal- and Average-Error Cases

In communication networks, cooperative strategies are coding schemes where network nodes work together to improve network performance metrics such as the total rate delivered across the network. This work studies encoder cooperation in the setting of a discrete multiple access channel (MAC) with two encoders and a single decoder. A network node, here called the cooperation facilitator (CF), that is connected to both encoders via rate-limited links, enables the cooperation strategy. Previous work by the authors presents two classes of MACs: (i) one class where the average-error sum-capacity has an infinite derivative in the limit where CF output link capacities approach zero, and (ii) a second class of MACs where the maximal-error sum-capacity is not continuous at the point where the output link capacities of the CF equal zero. This work contrasts the power of the CF in the maximal- and average-error cases, showing that a constant number of bits communicated over the CF output link can yield a positive gain in the maximal-error sum-capacity, while a far greater number of bits, even numbers that grow sublinearly in the blocklength, can never yield a non-negligible gain in the average-error sum-capacity

Bit Error Rate Analysis in Multicast Multiple Input Multiple Output Systems

At the present time whole information and communication technology industry contributes to the global carbon emission. With the aim of reducing the carbon footprint and the operating cost of wireless networks, overall energy reduction is required in the region of two to three orders of magnitude. Meanwhile, significant increase of the network spectrum efficiency is needed to cope with the exponentially increasing traffic loads. Due to this factors spatial modulation (SM) has recently established itself as promising transmission concept which belongs to single-radio frequency large scale multiple input multiple output (MIMO) wireless system. Spatial modulation MIMO takes advantage of whole antenna array at the transmitter, while using limited number of radio frequency chains. The multiple input multiple output multiplies capacity by transmitting different signals over multiple antennas and orthogonal frequency division multiplexing (OFDM), which divides a radio channel into many closely spaced sub channels to provide more reliable communication at high speeds. The system calculate the bit error rate (BER) for multicast multiple input multiple output system with the spatial modulation (SM) and study the effect of signal to noise ratio on bit error rate. MATLAB software is use to simulate system. The simulation results show that bit error rate decreases as signal to noise ratio increases. System reaches zero bit error rate for the value of signal to noise ratio greater than 18dB. System has provided less bit error rate for large signal to noise ratio which improves system performance

Zero-error Function Computation on a Directed Acyclic Network

We study the rate region of variable-length source-network codes that are used to compute a function of messages observed over a network. The particular network considered here is the simplest instance of a directed acyclic graph (DAG) that is not a tree. Existing work on zero-error function computation in DAG networks provides bounds on the \textit{computation capacity}, which is a measure of the amount of communication required per edge in the worst case. This work focuses on the average case: an achievable rate tuple describes the expected amount of communication required on each edge, where the expectation is over the probability mass function of the source messages. We describe a systematic procedure to obtain outer bounds to the rate region for computing an arbitrary demand function at the terminal. Our bounding technique works by lower bounding the entropy of the descriptions observed by the terminal conditioned on the function value and by utilizing the Schur-concave property of the entropy function. We evaluate these bounds for certain example demand functions.Comment: 18 pages, 2 figures, submitted to IEEE Transactions on Information Theor