We study task-oriented lossy compression through the lens of rate-distortion-classification (RDC) representations. The source is Bernoulli, the distortion measure is Hamming, and the binary classification variable is coupled to the source via a binary symmetric model. Building on the one-shot common-randomness formulation, we first derive closed-form characterizations of the one-shot RDC and the dual distortion-rate-classification (DRC) tradeoffs. We then use a representation-based viewpoint and characterize the achievable distortion-classification (DC) region induced by a fixed representation by deriving its lower boundary via a linear program. Finally, we study universal encoders that must support a family of DC operating points and derive computable lower and upper bounds on the minimum asymptotic rate required for universality, thereby yielding bounds on the corresponding rate penalty. Numerical examples are provided to illustrate the achievable regions and the resulting universal RDC/DRC curves.

We study the problem of estimating a parametric discrete memoryless channel \( p(y \mid x; \boldsymbolθ) \) when the transmitter selects its input distribution \( π\) to maximize mutual information under the true parameter \( \boldsymbolθ^* \). Using only i.i.d.\ observations of the channel output, we aim to jointly estimate the capacity-achieving input distribution \( \boldsymbolπ^* \) and the true channel parameter \( \boldsymbolθ^* \). In general, recovery of \( \boldsymbolπ^* \) and \( \boldsymbolθ^* \) can be challenging. To that end, we propose two efficient algorithms based on the Blahut--Arimoto (BA) optimality conditions: (i) a bilevel fixed-point method and (ii) an augmented Lagrangian method. Empirical results demonstrate that both proposed algorithms successfully recover the true \( \boldsymbolθ^* \) and \( \boldsymbolπ^* \), whereas a naive maximum-likelihood approach that ignores the mutual-information maximization constraint fails to do so.

Quantization plays a central role in digital communication by mapping continuous-valued signals to a finite set of levels with minimal distortion. Beyond mean-square error, mutual information between the channel input and the quantizer output provides a powerful metric for signal recovery. However, finding the quantizer that maximizes the mutual information is NP-complete for non-binary inputs. To that end, while not optimal, thresholding schemes, whether single-threshold or multi-threshold, are widely adopted. In this work, we study the parallel stochastic single-threshold quantizer architecture, provide some information-theoretic insights, and introduce a momentum-accelerated gradient ascent algorithm that efficiently tunes a single decision threshold to maximize the mutual information. We demonstrate convergence improvements over exhaustive search and quantify mutual information gains across binary and non-binary input distributions. We also validate our theoretical framework with simulations on the MNIST dataset, demonstrating that increasing the number of parallel quantization branches, i.e., mutual information, significantly improves classification accuracy, especially when quantization thresholds are learned and training data is limited.

We present a novel approach to perform multiplicative noise removal, utilizing the recent developments of diffusion models. We show that multiplicative noise, which commonly appears in images produced by synthetic aperture radar (SAR), laser, or optical lenses, can be well-modeled by a Geometric Brownian process in the logarithmic domain. This process admits a time-reversal stochastic differential equation (SDE), which is utilized to perform noise removal. We conduct extensive experiments to compare our approach with classical methods as well as state-of-the-art Deep Learning-based approaches. Our models significantly outperform others in terms of perception-based metrics such as LPIPS and FID, while remaining competitive in traditional pixel-based metrics like PSNR and SSIM.

In lossy compression, Wang et al. [1] recently introduced the rate-distortion-perception-classification function, which supports multi-task learning by jointly optimizing perceptual quality, classification accuracy, and reconstruction fidelity. Building on the concept of a universal encoder introduced in [2], we investigate the universal representations that enable a broad range of distortion-classification tradeoffs through a single shared encoder coupled with multiple task-specific decoders. We establish, through both theoretical analysis and numerical experiment, that for a Gaussian source under mean-squared error (MSE) distortion, the distortion-classification tradeoff region can be achieved using a single universal encoder. For general sources, we characterize the achievable region and identify conditions under which a universal encoder can produce a small distortion penalty. The experimental result on the MNIST dataset further supports our theoretical findings. We show that universal encoders can obtain distortion performance comparable to task-specific encoders. These results demonstrate the practicality and effectiveness of the proposed universal framework in multi-task compression scenarios.

This paper investigates a beamforming scheme designed to minimize the symbol error probability (SEP) for a legitimate user while guaranteeing that the likelihood of an eavesdropper correctly recovering symbols remains below a predefined threshold. The focus is on finding an optimal beamforming vector for binary antipodal signal detection in multiple-input multiple-output (MIMO) Gaussian wiretap channels. Finding the optimal beamforming vector is a non-convex problem, and thus conventional computationally efficient algorithms for convex problems cannot be applied in this context. To that end, our proposed algorithm relies on Karush–Kuhn–Tucker (KKT) conditions and the generalized eigen-decomposition method to find an exact solution. The numerical results are presented to assess the performance of the proposed method for various scenarios.

In recent years, the number of people counting systems in deployment has been increasing significantly. People counting systems can be used to automate the data collection for advertisement, and revenue projections as well as reduce energy costs using adaptive HVAC operations. However, a naive implementation of people counting systems may result in revealing some unintended information about the users/customers and higher power consumption from operating the systems continuously. In this paper, we study a mmWave Multiple Input Multiple Output (MIMO) radar sensor system for detecting the number of people in a confined space with the aims of low power consumption and minimal leakage of user information. In particular, we showed that a 3D convolutional neural network can accurately determine up to 4 people in a typical size room using a surprisingly minimal number of mmWave signatures ( less than 10 ) as its inputs.

We consider a communication channel whose input is modeled as a discrete random variable X with distribution pX. X is transmitted over a noisy channel and distorted by a continuous-valued noise to result in a continuous-valued output signal U at the receiver. A thresholding quantizer Q is applied to reconstruct a discrete signal V = Q(U) from the continuous-valued U. Our goal is to jointly design both the input distribution pXand the thresholding quantizer Q to maximize the mutual information I(X; V) between the input X and V since the accuracy of any decoding algorithm that estimates X from V fundamentally depends on I(X; V). In this paper, an alternating maximization algorithm is proposed that guarantees to achieve a locally optimal solution. In addition, we numerically show that by randomly selecting a set of initial starting points, the proposed algorithm is capable of achieving the globally optimal solution. Both the theoretical and numerical results are provided to justify our approach.

Partitioning algorithms play a key role in many scientific and engineering disciplines. A partitioning algorithm divides a set into a number of disjoint subsets or partitions. Often, the quality of the resulted partitions is measured by the amount of impurity in each partition, the smaller impurity the higher quality of the partitions. Let M be the number of N-dimensional elements in a set and K be the number of desired partitions, then an exhaustive search over all the possible partitions to find a minimum partition has the complexity of O(KM) which quickly becomes impractical for many applications with modest values of K and M. Thus, many approximate algorithms with polynomial time complexity have been proposed, but few provide the bounded guarantee. In this paper, we propose a linear time algorithm with bounded guarantee based on the maximum likelihood principle. Furthermore, the guarantee bound of the proposed algorithm is better than the state-of-the-art method in [1] for many impurity functions, and at the same time, for K ≥ N, the computational complexity is reduced from O(M3) to O(M).

Set partitioning is a key component of many algorithms in machine learning, signal processing and communications. In general, the problem of finding a partition that minimizes a given impurity (loss function) is NP-hard. As such, there exists a wealth of literature on approximate algorithms and theoretical analysis for the partitioning problem under different settings. In this paper, we formulate and solve a variant of the partition problem called the minimum weighted concave impurity partition under constraint (MIPUC). MIPUC finds an optimal partition that minimizes a given weighted concave loss function under a given concave constraint. MIPUC generalizes the recently proposed Deterministic Information Bottleneck problem which finds an optimal partition that maximizes the mutual information between the input and partitioned output while minimizing the partitioned output entropy. Our proposed algorithm is based on an optimality condition, which allows us to find a locally optimal solution efficiently. We also show that the optimal partitions are separated by some hyperplanes in the space of posterior probability mass functions.

In this paper, we consider the problem of one-bit (two-level) output-quantization maximizing mutual information between quantizer-output and channel-input using a single threshold for a discrete signal that is corrupted by a continuous additive noise. A necessary condition is constructed for which the thresholding quantizer is optimal. In addition, we show that if the distribution of the additive noise satisfies a mild condition, then a global optimal threshold can be found efficiently via a modified fixed-point algorithm.

Consider a channel whose the input alphabet set$\mathbb {X}=\{x_{1},x_{2}, {\dots },x_{K}\}$contains$K$discrete symbols modeled as a discrete random variable$X$having a probability mass function$\mathbf {p}(\mathbf {x}) = [p(x_{1}), p(x_{2}), {\dots }, p(x_{K})]$and the received signal$Y$being a continuous random variable.$Y$is a distorted version of$X$caused by a channel distortion, characterized by the conditional densities$p(y|x_{i})=\phi _{i}(y)$,$i=1,2, {\dots },K$. To recover$X$, a quantizer$Q$is used to quantize$Y$back to a discrete output$\mathbb {Z} =\{z_{1}, z_{2}, {\dots }, z_{N}\}$corresponding to a random variable$Z$with a probability mass function$\mathbf {p}(\mathbf {z}) = [p(z_{1}), p(z_{2}), {\dots }, p(z_{N})]$such that the mutual information$I(X;Z)$is maximized subject to an arbitrary constraint on$\mathbf {p}(\mathbf {z})$. Formally, we are interested in designing an optimal quantizer$Q^{*}$that maximizes$\beta I(X;Z) - C(Z)$where$\beta $is a positive number that controls the trade-off between maximizing$I(X;Z)$and minimizing an arbitrary cost function$C(Z)$. Let$\mathbf {p}(\mathbf {x}|y)=[p(x_{1}|y),p(x_{2}|y), {\dots },p(x_{K}|y)]$be the posterior distribution of$X$for a given value of$y$, we show that for any arbitrary cost function$C(.)$, the optimal quantizer$Q^{*}$separates the vectors$\mathbf {p}(\mathbf {x}|y)$into convex regions. Using this result, a method is proposed to determine an upper bound on the number of thresholds (decision variables on$y$) which is used to speed up the algorithm for finding an optimal quantizer. Numerical results are presented to validate the findings.

We study the problem of virtual machine (VM) placement and migration in a data center. In the current approaches, VMs are assigned to physical servers using on-demand provisioning. Such an approach is simple but it often results in a poor performance due to resource fragmentation. Additionally, sub-optimal VM placement usually generates unneeded VM migration and unnecessary cross network traffic. The efficiency of a datacenter therefore significantly depends on how VMs are provisioned and where they are placed. A good placement scheme will not only improve the quality of service but also reduce the operation cost of the data center. In this paper, we study the problem of optimal VM placement and migration to minimize resource usage and power consumption in a data center. We formulate the optimization problem as a joint multiple objective function and solve it by leveraging the framework of convex optimization. Due to the intractable nature of the combinatorial optimization, we then propose Multi-level Join VM Placement and Migration (MJPM) algorithms based on the relaxed convex optimization framework to approximate the optimal solution. The theoretical analysis demonstrates the effectiveness of our proposed algorithms that substantially increases data center efficiency. In addition, our extensive simulation results on different practical topologies show significant performance improvement over the existing approaches.

This article presents the frequency-locked high-power RF oscillator using a gallium nitride (GaN) high electron mobility transistor (HEMT) amplifier and phase-locked loop (PLL) for 2.4-GHz industrial, scientific, and medical (ISM) band applications. The proposed architecture exploits the GaN power amplifier in the positive-feedback loop, whereas the desired phase shift for the target oscillating frequency is regulated from the PLL. To the best of our knowledge, this work is the first to employ the frequency locking scheme for a high-power solid-state RF oscillator. A detailed analysis of the oscillation conditions and the efficiency is provided. The prototype circuit is implemented with hybrid phase shifters and a fractional- N frequency synthesizer. The implemented RF oscillator circuit operates from 2.3 to 2.575 GHz and achieves a low phase noise of -131.8 dBc/Hz at a 1-MHz offset frequency. The power efficiency of the proposed oscillator reaches 58%, and the PLL incurs only 0.2% efficiency degradation.

Given an original discrete source X with the distribution pX that is corrupted by noise to produce a noisy data Y with the given joint distribution p(X,Y). A quantizer/classifier Q : Y → Z is then used to classify/quantize Y to a discrete partitioned output Z having probability distribution pZ. Next, Z is transmitted over a discrete memoryless channel (DMC) with a given channel matrix A that produces the final discrete output T . One wants to design an optimal quantizer/classifier Q* to minimize the end-to-end impurity/cost function F(X, T) between the input X and the final output T. Our result generalizes some previous results. First, an iteration linear time complexity algorithm is proposed to find the locally optimal quantizer. Second, we show that the optimal quantizers produce the hard partitions that are equivalent to the cuts by hyper-planes in the space of the posterior distribution pX|Y. This result provides a polynomial-time complexity algorithm to find the globally optimal quantizer. Finally, in the special case where the source X is binary, an efficient algorithm is proposed to find the truly global optimal partition.

Partitioning algorithms play a key role in machine learning, signal processing, and communications. They are used in many well-known NP-hard problems such as k-means clustering and vector quantization. The goodness of a partition scheme is measured by a given impurity function over the resulted partitions. The optimal partition is one(s) with the minimum impurity. Practical algorithms for finding an optimal partitioning are approximate, heuristic, and often assume certain properties of the given impurity function such as concavity/convexity. In this paper, we propose a heuristic, efficient (linear time) algorithm for finding the minimum impurity for a broader class of impurity functions which includes popular impurities such as Gini index and entropy. We also make a connection to a well-known result which states that the optimal partitions correspond to the regions separated by hyperplane cuts in the probability space of the posterior distribution.

In this paper, we consider a channel whose the input is a binary random source X ∈ {x1,x2} with the probability mass function (pmf) pX= [px1,px2] and the output is a continuous random variable Y ∈ R as a result of a continuous noise, characterized by the channel conditional densities py|x1= φ1(y) and py|x2= φ2(y). A quantizer Q is used to map Y back to a discrete set Z ∈ {z1,z2,...,zN}. To retain most amount of information about X, an optimal Q is one that maximizes I(X;Z). On the other hand, our goal is not only to recover X but also ensure that pZ= [pz(1),pz(2),...,pz(N)] satisfies a certain constraint. In particular, we are interested in designing a quantizer that maximizes βI(X;Z)-C(pZ) where β is a tradeoff parameter and C(pZ) is an arbitrary cost function of pZ. Let the posterior probability px(1)|y= ry= [px(1)φ1(y)]/([px(1)φ1(y)] + [px(2)φ2(y)]), our result shows that the structure of the optimal quantizer separates ryinto convex cells. In other words, the optimal quantizer has the form: Q*(ry) = zi, if a8i-1≤ ryi* for some optimal thresholds a0* = 01*2*N-1*N* = 1. Based on this optimal structure, we describe some fast algorithms for determining the optimal quantizers.

Due to the limitation of the radio frequency (RF) spectrum, it is increasingly more difficult to support billions of wireless devices in the age of Internet-of-Things. Consequently, many recent wireless indoor communication systems have been developed using free space optical (FSO) communication technologies that exploit the extremely large light spectrum to transmit data. However, FSO technologies, especially when using On-Off Keying(OOK) modulation, the Light-Emitting-Diode (LED) transmitters produce an inherent non-linear distortion in the output. Effectively, the LED acts as a band-limited channel between the inputs and outputs. In this paper, we utilize a mathematical model to capture the distortion of the output for a given input. Based on the mathematical model, we developed a technique called Memory Decoding Algorithm (MDA) used at a receiver and effectively reduces the bit error rates via maximum likelihood decoding principle when On-Off Keying(OOK) modulation is used. Both theoretical analyses and simulation results show that the proposed technique outperforms the conventional methods such as linear equalization.

We consider a channel with discrete input X, a continuous noise that corrupts the input X to produce the continuous-valued output U. A thresholding quantizer is then used to quantize the continuous-valued output U to the final discrete output V. The goal is to jointly design a thresholding quantizer that maximizes the mutual information between input and quantized output I(X; V ) while minimizing a pre-specified function of the quantized output F(pV). A general dynamic programming algorithm is proposed having the time complexity O(KNM2) where N, M and K are the sizes of input X, output U and quantized output V, respectively. Moreover, we show that if F(pV) Σi=1Kgi(p(vi)) where gi(.) is a convex function, p(vi) ∈ pV{pv1,..., pvK} is the probability mass function of output vi∈ V and the channel conditional density p(u|x) satisfies the dominated condition (often true in practice), then the existing SMAWK algorithm can be applied to reduce the time complexity of the dynamic programming algorithm from O(KNM2) to O(KNM). Both theoretical and numerical results are provided to verify our contributions.

Consider a channel having the discrete input X that is corrupted by a continuous noise to produce the continuous-valued output U. A thresholding quantizer is then used to quantize the continuous-valued output U to the final discrete output V . One wants to design a thresholding quantizer that maximizes the mutual information between the input and the final quantized output I(X; V ). In this paper, the structure of optimal thresholding quantizer is established that finally results in two efficient algorithms having the time complexities O(NM + K log2(NM)) for finding the local optimal quantizer and O(K M log(NM)) for finding the global optimal quantizer where N, M, K are the size of input X, received output U and quantized output V , respectively. Both theoretical and numerical results are provided to verify our contributions.

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a 'top-down' pattern that occurs across four brain network states that arise at different time instants (0-170[Formula: see text]ms, 170-370[Formula: see text]ms, 380-620[Formula: see text]ms, and 620-1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

We consider a channel with a binary input X being corrupted by a continuous-valued noise that results in a continuous-valued output Y . An optimal binary quantizer is used to quantize the continuous-valued output Y to the final binary output Z to maximize the mutual information I(X; Z). We show that when the ratio of the channel conditional density r(y) = P (Y =y|X=0)/P (Y =y|X=1) is a strictly increasing or decreasing function of y, then a quantizer having a single threshold can maximize mutual information. Furthermore, we show that an optimal quantizer (possibly with multiple thresholds) is the one with the thresholding vector whose elements are all the solutions of r(y) = r* for some constant r* > 0. In addition, we also characterize necessary conditions using fixed point theorem for the optimality and uniqueness of a quantizer. Based on these conditions, we propose an efficient procedure for determining all locally optimal quantizers, and thus, a globally optimal quantizer can be found. Our results also confirm some previous results using alternative elementary proofs.

The ability to perceive and regulate emotion is a key component of cognition that is often disrupted by disease. Current neuroimaging studies regarding emotion regulation have implicated a number of cortical regions and identified several EEG features of interest, including the late positive potential and frontal asymmetry. Unfortunately, currently applied methods generally lack in the resolution necessary to capture focal cortical activity and explore the causal interactions between brain regions. In this paper, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data were simultaneously recorded from 20 subjects undergoing emotion processing and regulation tasks. Cortical activity with high-spatiotemporal resolution and accuracy was reconstructed using a novel multimodal EEG/fMRI integration method. A detailed causal brain network associated with emotion processing and regulation was then identified, and the network changes that facilitate different emotion conditions were investigated. The cortical activity of the ventrolateral prefrontal (VLPFC) and posterior parietal cortices depicted conditionally-sensitive spike and wave patterns evidenced in inter-regional communication. The VLPFC was found to behave as a main network source, with conditionally-specific interactions supporting emotional shifts. The results provide unique insight into the cortical activity that supports emotional perception and regulation, the origins of known EEG phenomena, and the manner in which brain regions coordinate to affect behavior.

In this paper, we study the channel capacity of discrete memoryless thresholding channels (DMTCs) that are used in Pulse Amplitude Modulation (PAM) for LED free-space transmissions. Although capacities of discrete memoryless channels (DMC) are well-studied and can be determined by various algorithms, the capacity of a DMTC is less explored and its capacity is more difficult to obtain. This is due to the fact that, unlike a typical DMC channel whose the capacity is a function input distribution, the capacity of a DTMC channel is a function of both input distribution and decision thresholds. To resolve this problem, we propose an algorithm for finding the channel capacity of a DMTC. Both theoretical and numerical results are provided to verify our approach.

While capacities of discrete memoryless channels are well studied, it is still not possible to obtain a closed form expression of the capacity for an arbitrary discrete memoryless channel. This paper shows an elementary technique based on Karush-Kuhn-Tucker (KKT) conditions to obtain a closed form expression for a good upper bound of an arbitrary discrete memoryless channel. Furthermore, using this technique, we are able to obtain the closed form expressions for the capacities of channels whose channel matrices satisfy a number of conditions.

The recent growth of markets for smart homes and the Internet of Things (IoT) create a significant demand in wireless access capacity. Consequently, much of current research has focused on efficient utilization of RF (Radio Frequency) spectrum. In this paper, an orthogonal approach using Free Space Optic (FSO) technology is proposed to increase capacities of indoor wireless networks. Specifically, we describe WiFO, a novel wireless indoor communication system based on the femtocell architecture that integrates both RF and FSO technologies. WiFO aims to increase the wireless capacities while retaining the mobility offered by the existing WiFi networks. Our preliminary prototype shows promising results to significantly boost up the capacity of the existing WiFi networks.

Free Space Optical (FSO) communication has received tremendous attention in recent years as an alternative approach to overcome the limited RF spectrum problem for wireless communication. Proposed FSO communication systems transmit information by modulating light. Thus, FSO transmissions do not interfere with existing RF transmissions, and can provide substantial capacity gain. In this paper, we briefly introduce WiFO, a hybrid indoor wireless communication system designed to substantially increase wireless capacity. We also describe a new channel model used for multi-user FSO communication. The main contributions of this work are (1) characterizing the multiuser achievable rate region for the new channel given the side information via a constructive embedded coding technique and (2) providing analytical result for maximizing the sum rate of users.

The recent increase in the number of wireless devices has been driven by the growing markets of smart homes and the Internet of Things (IoT). As a result, expanding and/or efficient utilization of the radio frequency (RF) spectrum is critical to accommodate such an increase in wireless bandwidth. Alternatively, recent free-space optical (FSO) communication technologies have demonstrated the feasibility of building WiFO, a high capacity indoor wireless network using the femtocell architecture. Since FSO transmission does not interfere with the RF signals, such a system can be integrated with the current WiFi systems to provide orders of magnitude improvement in bandwidth. A novel component of WiFO is its ability to jointly encode bits from different flows for optimal transmissions. In this paper, we introduce the WiFO architecture and a novel cooperative transmission framework using location assisted coding (LAC) technique to increase the overall wireless capacity. Specifically, achievable rate regions for WiFO using LAC will be characterized. Both numerical and theoretical analyses are given to validate the proposed coding schemes.

Recent advances in free-space optical technology promise a complementary approach to increasing wireless capacity with minimal changes to the existing wireless technologies. This paper puts forth the hypothesis that it is possible to simultaneously achieve high capacity and high mobility by developing a communication system called WiFiFO (WiFi Free space Optic) that seamlessly integrates the recent free- space optics technologies and the current WiFi technologies. We briefly describe the WiFIFO architecture then discuss the main contribution of this paper that is optimizing the capacity of the proposed WiFiFO system. Specifically, we consider the problem of power allocation for multiple FSO and WiFi transmitters in order to achieve maximum system capacity for given budget power. A mathematical model of the combined capacity of FSO and WiFi channel is derived. We show that the power allocation problem for WiFiFO can be approximated well as a convex optimization problem. To that end, an algorithm based on gradient decent method is developed. Simulation results indicate that the proposed algorithm, together with system architecture can provide an order-of-magnitude increase in capacity over the existing WiFi systems.

Designing network protocols that work well under a variety of network conditions typically involves a large amount of manual tuning and guesswork, particularly when choosing dynamic update strategies for numeric parameters. The situation is made more complex by adding the Quality of Service (QoS) requirements to a network protocol. A fundamentally different approach for designing protocols is via Reinforcement Learning (RL) algorithms which allow protocols to be automatically optimized through network simulation. However, getting RL to work well in practice requires considerable expertise and carries a significant implementation overhead. To help overcome this challenge, recent work has developed the programming paradigm of Adaptation-Based Programming (ABP), which allows programmers who are not RL-experts to write self-optimizing “adaptive programs”. In this work, we study the potential of applying ABP to the problem of designing network protocols via simulation. We demonstrate the flexibility of our design method via a number of case studies, each of which investigates the performance of an adaptive program written for the backoff mechanism of the MAC layer in the 802.11 standard. Our results show that the learned protocols typically outperform 802.11 on a number of evaluation metrics and network conditions.

“Always Best Connected” or simply ABC concept has been introduced to express the possibility for mobile users to experience with smartphone/computer a continuity of service at any place any time. In this context, the aim of the fourth generation of wireless networks is to not only support high speed connection but also implement ABC taking benefit of the numerous underlying wireless technologies. For that, smart-phones should implement sophisticated access network selection mechanism to take benefit of this diversity. In our previous works, we have used the utility theory to propose several utility functions that measures the value of each access network vs. the preferences of the end users and we have shown how these preferences can be used by the user terminal to select the most appropriate access network. In this paper, we extend that work with the implementation of the solution in a simulator of heterogeneous access networks and perform a set of simulations to highlight the value added of the proposed solution. The obtained results show similar results as those obtained analytically and confirm the validity of the approach for the end users and the operators.

Packet loss and end-to-end delay limit delay sensitive applications over the best effort packet switched networks such as the Internet. In our previous work, we have shown that substantial reduction in packet loss can be achieved by sending packets at appropriate sending rates to a receiver from multiple senders, using disjoint paths, and by protecting packets with forward error correction. In this paper, we propose a path diversity with forward error correction (PDF) system for delay sensitive applications over the Internet in which, disjoint paths from a sender to a receiver are created using a collection of relay nodes. We propose a scalable, heuristic scheme for selecting a redundant path between a sender and a receiver, and show that substantial reduction in packet loss can be achieved by dividing packets between the default path and the redundant path. NS simulations are used to verify the effectiveness of PDF system.