Noisy Broadcast Channel Research Articles

An extension of Cover–Chiang work on point to point channel to less noisy broadcast channel and analysis of the receivers cognition impacts

An extension of Cover–Chiang work on point to point channel to less noisy broadcast channel and analysis of the receivers cognition impacts

Distributed Hypothesis Testing over Noisy Broadcast Channels

This paper studies binary hypothesis testing with a single sensor that communicates with two decision centers over a memoryless broadcast channel. The main focus lies on the tradeoff between the two type-II error exponents achievable at the two decision centers. In our proposed scheme, we can partially mitigate this tradeoff when the transmitter has a probability larger than 1/2 to distinguish the alternate hypotheses at the decision centers, i.e., the hypotheses under which the decision centers wish to maximize their error exponents. In the cases where these hypotheses cannot be distinguished at the transmitter (because both decision centers have the same alternative hypothesis or because the transmitter’s observations have the same marginal distribution under both hypotheses), our scheme shows an important tradeoff between the two exponents. The results in this paper thus reinforce the previous conclusions drawn for a setup where communication is over a common noiseless link. Compared to such a noiseless scenario, here, however, we observe that even when the transmitter can distinguish the two hypotheses, a small exponent tradeoff can persist, simply because the noise in the channel prevents the transmitter to perfectly describe its guess of the hypothesis to the two decision centers.

On Hypothesis Testing Against Conditional Independence With Multiple Decision Centers

A distributed binary hypothesis testing problem is studied with one observer and two decision centers. Achievable type-II error exponents are derived for testing against conditional independence when the observer communicates with the two decision centers over one common and two individual noise-free bit pipes and when it communicates with them over a noisy broadcast channel. The results are based on a coding and testing scheme that splits the observations into subblocks, so that transmitter and receivers can independently apply to each subblock either Gray-Wyner coordination coding with side-information or hybrid joint source-channel coding with side-information, followed by a Neyman-Pearson test over the subblocks at the receivers. This approach allows to avoid introducing further error exponents that one would expect from the receivers’ decoding operations related to binning or the noisy transmission channel. The derived exponents are shown to be optimal in some special cases when communication is over noise-free links. The results reveal a tradeoff between the type-II error exponents at the two decision centers.

Characterizing the Number of Optimal Index Codes and Their Relation to Min–Max Probability of Error Performance

An index coding problem arises when there is a single source with a number of messages and multiple receivers each wanting a subset of messages knowing a different set of messages a priori . The noiseless index coding problem is to identify the minimum number of transmissions (optimal length) to be made by the source through a noiseless channel so that all receivers can decode their wanted messages using the transmitted symbols and their respective prior information. Recently (A. Thomas, R. Kavitha, A. Chandramouli, and B. Sundar Rajan, “Optimal index coding with min-max probability of error over fading channels,” in Proc. IEEE 26th Annu. Int. Symp. Pers. Indoor Mobile Radio Commun. , Hong Kong, 2015, pp. 889–894), it is shown that different optimal length codes perform differently in a noisy channel for single-uniprior index coding problems. Towards identifying the best optimal length index code, one needs to know the number of optimal length index codes. In this paper, we consider unicast index coding problems and present results on the number of optimal length index codes making use of the representation of an index coding problem by an equivalent network code. Our formulation results in matrices of smaller sizes compared to the approach of Koetter and Medard (R. Koetter and M. Medard, “An algebraic approach to network coding,” IEEE/ACM Trans. Netw. , vol. 11, no. 5, pp. 782–795, Oct. 2003.). Our formulation leads to a lower bound on the minimum number of optimal length codes possible for all unicast index coding problems (L. Ong and C. K. Ho, “Optimal index codes for a class of multicast networks with receiver side information,” in Proc. IEEE Int. Conf. Commun. , 2012, pp. 2213–2218), which is met with equality for several special cases of the unicast index coding problem. Furthermore, for noisy broadcast channels, a method to identify the best among the optimal length codes for unicast index coding problems in terms of minimum–maximum error probability is presented.

Wiretapped Oblivious Transfer

In this paper, we study the problem of obtaining 1-of-2 string oblivious transfer (OT) between users Alice and Bob, in the presence of a passive eavesdropper Eve. The resource enabling OT in our setup is a noisy broadcast channel from Alice to Bob and Eve. Apart from the OT requirements between the users, Eve is not allowed to learn anything about the users’ inputs. When Alice and Bob are honest-but-curious and the noisy broadcast channel is made up of two independent binary erasure channels (connecting Alice–Bob and Alice–Eve), we derive the 1-of-2 string OT capacity for both 2-privacy (when Eve can collude with either Alice or Bob) and 1-privacy (when no such collusion is allowed). We generalize these capacity results to 1-of- $N$ string OT and study other variants of this problem. When Alice and/or Bob are malicious, we present a different scheme based on interactive hashing. This scheme is shown to be optimal for certain parameter regimes. We present a new formulation of multiple, simultaneous OTs between Alice–Bob and Alice–Cathy. For this new setup, we present schemes and outer bounds that match in all but one regime of parameters. Finally, we consider the setup where the broadcast channel is made up of a cascade of two independent binary erasure channels (connecting Alice–Bob and Bob–Eve) and 1-of-2 string OT is desired between Alice and Bob with 1-privacy. For this setup, we derive an upper and lower bound on the 1-of-2 string OT capacity which match in one of two possible parameter regimes.

Superposition Coding Is Almost Always Optimal for the Poisson Broadcast Channel

This paper shows that the capacity region of the continuous-time Poisson broadcast channel is achieved via superposition coding for most channel parameter values. Interestingly, the channel in some subset of these parameter values does not belong to any of the existing classes of broadcast channels for which superposition coding is optimal (e.g., degraded, less noisy, more capable). In particular, we introduce the notion of effectively less noisy broadcast channel and show that it implies less noisy but is not in general implied by more capable. For the rest of the channel parameter values, we show that there is a gap between Marton's inner bound and the UV outer bound.

An Information Theoretic Approach to Secret Sharing

A novel information theoretic approach is proposed to solve the secret sharing problem, in which a dealer distributes one or multiple secrets among a set of participants in such a manner that for each secret only qualified sets of users can recover this secret by pooling their shares together while nonqualified sets of users obtain no information about the secret even if they pool their shares together. While existing secret sharing systems (implicitly) assume that communications between the dealer and participants are noiseless, this paper takes a more practical assumption that the dealer delivers shares to the participants via a noisy broadcast channel. Thus, in contrast to the existing solutions that are mainly based on number theoretic tools, an information theoretic approach is proposed, which exploits the channel randomness during delivery of shares as additional resources to achieve secret sharing requirements. In this way, secret sharing problems can be reformulated as equivalent secure communication problems via wiretap channel models, and can hence be solved by employing the powerful information theoretic security techniques. This approach is first developed for the classic secret sharing problem, in which only one secret is to be shared. This classic problem is shown to be equivalent to a communication problem over a compound wiretap channel. Thus, the lower and upper bounds on the secrecy capacity of the compound channel provide the corresponding bounds on the secret sharing rate, and the secrecy scheme designed for the compound channel provides the secret sharing schemes. The power of the approach is further demonstrated by a more general layered multisecret sharing problem, which is shown to be equivalent to the degraded broadcast multiple-input multiple-output (MIMO) channel with layered decoding and secrecy constraints. The secrecy capacity region for the degraded MIMO broadcast channel is characterized, which provides the secret sharing capacity region. Furthermore, the secure encoding scheme that achieves the secrecy capacity region provides an information theoretic scheme for sharing the secrets.

On capacity region of certain classes of three‐receiver broadcast channels with side information

The fact that the results for 2-receiver broadcast channels (BCs) are not generalised to the 3-receiver ones is of information theoretical importance. In this study, the authors study two classes of discrete memoryless BCs with non-causal side information, that is, multilevel BC (MBC) and 3-receiver less noisy BC. First, they obtain an achievable rate region and a capacity outer bound for the MBC. Second, they provide the capacity region for a special class of 3-receiver less noisy BC. Third, the obtained special capacity region for the 3-receiver less noisy BC is evaluated for the continuous alphabet fading Gaussian version. It is worth mentioning that the previous works are special cases of the authors works.

Source Coding Problems With Conditionally Less Noisy Side Information

A computable expression for Heegard and Berger's rate-distortion function has eluded information theory for nearly three decades. Heegard and Berger's single-letter achievability bound is well known to be optimal for physically degraded side information; however, it is not known whether the bound is optimal for arbitrarily correlated side information (general discrete memoryless sources). In this paper, we consider a new setup where the side information at one receiver is conditionally less noisy than that at the other. The new setup includes degraded side information as a special case, and it is motivated by the literature on degraded and less noisy broadcast channels. Our key contribution is a converse proving the optimality of Heegard and Berger's achievability bound in a new setting, where the side information is conditionally less noisy and one distortion function is deterministic. The less noisy setup is also generalized to two different successive-refinement problems.

On Index Coding in Noisy Broadcast Channels with Receiver Message Side Information

This letter investigates the role of index coding in the capacity of AWGN broadcast channels with receiver message side information. We first show that index coding is unnecessary where there are two receivers; multiplexing coding and superposition coding are sufficient to achieve the capacity region. We next show that, for more than two receivers, multiplexing coding and superposition coding alone can be suboptimal. We give an example where these two coding schemes alone cannot achieve the capacity region, but adding index coding can. This demonstrates that, in contrast to the two-receiver case, multiplexing coding cannot fulfill the function of index coding where there are three or more receivers.

Polar Codes for Broadcast Channels

Polar codes are introduced for discrete memoryless broadcast channels. For $m$-user deterministic broadcast channels, polarization is applied to map uniformly random message bits from $m$ independent messages to one codeword while satisfying broadcast constraints. The polarization-based codes achieve rates on the boundary of the private-message capacity region. For two-user noisy broadcast channels, polar implementations are presented for two information-theoretic schemes: i) Cover's superposition codes; ii) Marton's codes. Due to the structure of polarization, constraints on the auxiliary and channel-input distributions are identified to ensure proper alignment of polarization indices in the multi-user setting. The codes achieve rates on the capacity boundary of a few classes of broadcast channels (e.g., binary-input stochastically degraded). The complexity of encoding and decoding is $O(n*log n)$ where $n$ is the block length. In addition, polar code sequences obtain a stretched-exponential decay of $O(2^{-n^{\beta}})$ of the average block error probability where $0 < \beta < 0.5$.

Key Agreement over a Generalized Multiple Access Channel Using Noiseless and Noisy Feedback

A secret key agreement framework involving three users is considered in which each of the users 1 and 2 intends to share a secret key with user 3 and users 1 and 2 are eavesdroppers with respect to each other. There is a generalized discrete memoryless multiple access channel (GDMMAC) from users 1 and 2 to user 3 where the three users receive outputs from the channel. Furthermore, there is a feedback channel from user 3 to users 1 and 2 through which user 3 sends information extracted from the received output from the GDMMAC to increase the key rates. We consider both noiseless and noisy feedback. In the case of noiseless feedback, a public channel of unlimited capacity from user 3 to users 1 and 2 is used only once. In the case of noisy feedback, a noisy broadcast channel (BC) from user 3 to users 1 and 2 can be repeatedly used, like GDMMAC. In both setups, inner bounds of the secret key capacity region are derived. The secret key capacity region is derived in some special cases where the channel inputs and outputs form Markov chains in certain orders. For illustration, the corresponding results are also derived and discussed for Gaussian channels. The cases with noiseless feedback, noisy feedback, and no feedback at all are compared with each other.

Secrecy via Sources and Channels

Alice and Bob want to share a secret key and to communicate an independent message, both of which they desire to be kept secret from an eavesdropper Eve. We study this problem of secret communication and secret key generation when two resources are available -- correlated sources at Alice, Bob, and Eve, and a noisy broadcast channel from Alice to Bob and Eve which is independent of the sources. We are interested in characterizing the fundamental trade-off between the rates of the secret message and secret key. We present an achievable solution and prove its optimality for the parallel channels and sources case when each sub-channel and source component satisfies a degradation order (either in favor of the legitimate receiver or the eavesdropper). This includes the case of jointly Gaussian sources and an additive Gaussian channel, for which the secrecy region is evaluated.

The Capacity Region of the Three Receiver Less Noisy Broadcast Channel

We determine the capacity region of a 3-receiver less noisy broadcast channel. The difficulty in extending the two-receiver result to three-receivers involves extending the Csiszar-sum lemma to three or more sequences, a standard difficulty in this area. In this work we bypass the difficulty by using a new information inequality, for less noisy receivers, that is employed in the converse. We also generalize our result to obtain the capacity region for a class of less noisy receivers.

Average consensus on digital noisy networks

We propose a class of distributed algorithms for computing arithmetic averages (average consensus) over networks of agents connected through digital noisy broadcast channels. Our algorithms do not require the agents to have knowledge of the network structure, nor do they assume any noiseless feedback to be available. We prove convergence to consensus, with both number of channel uses and computational complexity which are poly-logarithmic in the desired precision.