In this paper, we study the zero-error capacity of channels with memory, which are represented by graphs. We provide a method to construct code for any graph with one edge, thereby determining a lower bound on its zero-error capacity. Moreover, this code can achieve zero-error capacity when the symbols in a vertex with degree one are the same. We further apply our method to the one-edge graphs representing the binary channels with two memories. There are 28 possible graphs, which can be organized into 11 categories based on their symmetries. The code constructed by our method is proved to achieve the zero-error capacity for all these graphs except for the two graphs in Case 11.

The achievable rate of code over resistive random-access memory (ReRAM) channel with finite selector failures was published in our recent work. The rate was derived under the assumption of independent and identically distributed (i.i.d.) input. In this work, focusing on the ReRAM channel with a single selector failure in the memory array, we derive an upper bound on achievable rate under arbitrary input distribution. This upper bound is within 0.02 bits from the achievable rate of i.i.d. input, indicating that i.i.d. is very close to optimal for large memory arrays. Moreover, we analyze the achievable rate of random code over ReRAM channel with suboptimal decodings where the decoder ignores the channel correlation. Our result indicates that in this case the achievable rate is limited by the capacity of a memoryless channel. We reveal both weak and strong asymptotic properties of ReRAM channel to prove this. The proof can be directly extended to the case of ReRAM with an arbitrary number of selector failures in the memory array.

The achievable rate of code over resistive random-access memory (ReRAM) channel with finite selector failures was published in our recent work. The rate was derived under the assumption of independent and identically distributed (i.i.d.) input. In this work, focusing on the ReRAM channel in the case of single selector failure, we derive an upper bound on achievable rate under arbitrary input distribution. This upper bound is within 0.02 bits from the achievable rate of i.i.d. input, indicating that i.i.d. is very close to optimal for large memory arrays.

We introduce a class of channels, collectively referred to as the ‘chemical residual channel’, where the channel output is a probabilistic function of the current input and the previous output. When the channel is binary, there are 81 possible cases. For all these cases with known or unknown initial state, we completely characterize the maximal rate that can be achieved with zero error probability at any given finite block length. As a result, the zero-error capacities of these cases are obtained. We also show that feedback does not increase the zero-error capacity.

Semantic communication is an emerging research area that has gained a wide range of attention recently. Despite this growing interest, there remains a notable absence of a comprehensive and widely-accepted framework for characterizing semantic communication. This paper introduces a new conceptualization of semantic communication and formulates two fundamental problems, which we termlanguage exploitationandlanguage design. Our contention is that the challenge of language design can be effectively situated within the broader framework of joint source-channel coding theory, underpinned by a comprehensive end-to-end distortion metric. To tackle the language exploitation problem, we put forth three approaches: semantic encoding, semantic decoding, and a synergistic combination of both in the form of combined semantic encoding and decoding. Furthermore, we establish the semantic distortion-cost region as a critical framework for assessing the language exploitation problem. For each of the three proposed approaches, the achievable distortion-cost region is characterized. Overall, this paper aims to shed light on the intricate dynamics of semantic communication, paving the way for a deeper understanding of this evolving field.

In this paper, we study the zero-error capacity of binary channels with two memories. Among all 11 categories of "one-edge" channels, we solve 10 of them and give a lower and an upper bound on the capacity of the remaining one.

We consider a class of channels, collectively referred to as the ‘chemical residual channel’, where the channel output is determined by the current input and the previous output. When the channel is binary and the output is a deterministic function of the current input and the previous output, there are 16 possible cases. For all these cases, we characterize the maximal rate that can be achieved with zero error probability at any given finite block length. As a result, the zero-error capacities of these cases are obtained.

This paper studies the minimum-age scheduling problem in a wireless sensor network where an access point (AP) monitors the state of an object via a set of sensors. The freshness of the sensed state, measured by the age-of-information (AoI), varies at different sensors and is not directly observable to the AP. The AP has to decide which sensor to query/sample in order to get the most updated state information of the object (i.e., the state information with the minimum AoI). In this paper, we formulate the minimum-age scheduling problem as a multi-armed bandit problem with partially observable arms and explore the greedy policy to minimize the expected AoI sampled over an infinite horizon. To analyze the performance of the greedy policy, we 1) put forth a relaxed greedy policy that decouples the sampling processes of the arms, 2) formulate the sampling process of each arm as a partially observable Markov decision process (POMDP), and 3) derive the average sampled AoI under the relaxed greedy policy as a sum of the average AoI sampled from individual arms. Numerical and simulation results validate that the relaxed greedy policy is an excellent approximation to the greedy policy in terms of the expected AoI sampled over an infinite horizon.

This paper presents the first systematic study of the zero-error capacity regions of noisy networks. First, we consider two simple such networks, each consisting of a stationary memoryless multiple access channel with two binary inputs and one discrete output. There are two users in each network. Each of the two users transmits a message through the network, and the sink(s) of the network can decode both messages with zero error. A graph is used to represent the distinguishability of the inputs of the channel, and agraph setis used to represent the distinguishability of the inputs of the network. We show that for two networks represented by the same graph set, their zero-error capacity regions are the same. We list all the possible graph sets for the two networks and determine the zero-error capacity regions for some of these graph sets. Based on this result, we explore a relation between graph theory and set theory, and then redefine thecancellative pair of families of subsets. We further extend the problem formulation to a general network called theparallel network, which may consist of more than one channel with multiple inputs and multiple outputs.

Batched network coding is a practical branch of random linear network coding which encodes the input file into small batches of coded packets. Although burst erasures within the batches can reduce the advantage of network coding, it can be alleviated by applying a block interleaver which spreads the burst across multiple batches. On the other hand, the intermediate network nodes are required to perform recoding on the batches unlike the traditional forwarding strategy. The recoding of a batch can be started once all the packets in that batch which are not dropped by the channel are received. This means that the intermediate network nodes have to deinterleave the batches for recoding and reinterleave the batches again for transmission, which gives us a choice to use different interleaver depths at different network nodes. In this paper, we study the memory requirements of the schemes for transmitting batches which apply different interleaver depths. We investigate the periodic structure of buffer sizes and the connection between buffer sizes and the total delay induced by the interleaver. More importantly, we show that there exists a scheme which can achieve the lowest memory requirement and minimum total delay simultaneously.

This Research Work in Progress Paper aims to develop holistic competencies in engineering students through course-level teaching and learning activities. Information is the core of many engineering fields, such as communications engineering, signal processing, and computational intelligence. Despite its ambient existence in our everyday lives, most of the engineering students are not aware of the connection between information and their professional role as an engineer in the informational world. We seek to engage engineering students in understanding their professional and ethical responsibility, and promote their awareness of the impact of engineering solutions in a global and societal context. Our study is grounded in the intersection between two theoretical frameworks: (1) Holistic Competencies and (2) William Perry's scheme of intellectual and ethical development. Having students' learning evidence against William Perry's framework and identified students' cognitive changes along their course study, we suggest that holistic competencies can be fostered through cultivating students' multifaceted understanding of information and supplementing with real cases where social media serve as a channel for students to exchange ideas to address various global challenges.

Batched network coding is a practical realization of random linear network coding, which encodes the packets for transmission into small batches of coded packets. The advantage of network coding can be reduced when a burst loss occurs. Block interleaver is one of the approach to spread the burst across multiple batches. However, an intermediate node has to receive all the packets in a batch which are not dropped by the channel before it can perform recoding on the batch, which means that the node has to deinterleave for recoding and reinterleave again for transmission. This gives us the freedom to select heterogeneous interleaver depth among the intermediate nodes. In general, the larger the interleaver depth, the better the spread of the bursts. On the other hand, we want the delay as short as possible to enable real-time applications. In this paper, we investigate the delay induced by applying a block interleaver on batched network codes. We also show that a homogeneous interleaver depth is the largest interleaver depth we can use to achieve a minimum delay.

The zero-error capacity of a channel is defined as the maximum rate at which it is possible to transmit information with zero probability of error. In this paper, we settle all previously unsolved cases for the zero-error capacity of binary channels with one memory.

There has been a growing attention to engineering graduates' generic skills competency. Generic skills attainment is included as essential graduate attributes by accreditation bodies such as Accreditation Board for Engineering and Technology (ABET). Generic skills competency is often assessed at program level using indirect measures such as graduate surveys and student interviews. In this work-in-progress, we present the design, collection, and analysis of direct and indirect evidences of engineering students' generic skills learning at course level. In particular, we report an on-going implementation case study in a course for engineering undergraduates. Our study is rooted in the theoretical framework of social epistemic cognition which explains knowledge acquisition in social contexts. We also discuss our preliminary results collected from the online learning community participated by all students in the course (direct evidence) and a survey on the participants' beliefs associated to social epistemic cognition and collaborative learning (indirect evidence).

The zero error capacity of a channel is defined as the least upper bound of rates at which it is possible to transmit information with zero probability of error. Unsolved cases are addressed in this paper to complement the table of the zero error capacity of noisy binary channels with 1-memory. All cases are classified according to the isomorphic property of their confusability graphs, and then we determine the zero error capacity of the rest binary channels with 1-memory one by one.