In many practical learning problems, training samples are not i.i.d., and there is an intrinsic dependency among samples. Therefore, theoretical study of learning with dependent data has recently gained attention. In this paper, we provide a uniform convergence bound for the class of Lipschitz functions with bounded values at zero, under the assumption that the data samples are scalar and have a possibly dependent joint Gaussian distribution. Since other than Lipschitzness, there is no heavy assumption such as convexity or boundedness on the function class, the results are applicable for many practical models including neural networks. We showcase the strength and applicability of our theorems by numerical simulation and real-data analysis.

We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, we allow the unlabeled samples to deviate slightly (in total variation sense) from the in-domain distribution. The core idea behind our framework is to combine Distributionally Robust Optimization (DRO) with self-supervised training. As a result, we also leverage efficient polynomial-time algorithms for the training stage. From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in $\mathbb{R}^d$, where in addition to the $m$ independent and labeled samples from the true distribution, a set of $n$ (usually with $n\gg m$) out of domain and unlabeled samples are gievn as well. Using only the labeled data, it is known that the generalization error can be bounded by $\propto\left(d/m\right)^{1/2}$. However, using our method on both isotropic and non-isotropic Gaussian mixture models, one can derive a new set of analytically explicit and non-asymptotic bounds which show substantial improvement on the generalization error compared ERM. Our results underscore two significant insights: 1) out-of-domain samples, even when unlabeled, can be harnessed to narrow the generalization gap, provided that the true data distribution adheres to a form of the "cluster assumption", and 2) the semi-supervised learning paradigm can be regarded as a special case of our framework when there are no distributional shifts. We validate our claims through experiments conducted on a variety of synthetic and real-world datasets.

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of $T\ge2$ data distributions $P_1,\ldots,P_T$ undergoing a gradual shift, where each pair of consecutive measures $P_i,P_{i+1}$ are close to each other in Wasserstein distance. We have a supervised dataset of size $n$ sampled from $P_0$, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all $P_i$s can be suitably bounded. Our bounds rely on a newly introduced {\it {compatibility}} measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results.

While the Mean Opinion Score (MOS) is the most well-known way to quantify Quality of Experience (QoE), it only provides average insight. In this paper, we will demonstrate that instead of only relying on the MOS value, predicting users’ perceived quality through probabilistic metrics provides service providers with more accurate insight to improve their network decisions, relying on more accurate measures of how many of them are satisfied or not with the provided service. Furthermore, a hybrid non-intrusive ensemble learner based on the selection of multiple base learners is designed to estimate the QoE of the VoIP signal. The performance of our proposed model is compared to that of individual learners, demonstrating that the proposed scheme outperforms earlier schemes. In addition, in contrast with earlier schemes that relied on their own laboratory-generated dataset, another key advantage of our approach is that it extracts a wide variety of different system parameters, such as noise type and echo delay, and signal parameters, such as fundamental frequency, only from the degraded signal. Finally, we show how the more accurate predicted QoE values can be used by service providers to properly modify network parameters to get closer to the required QoE levels.

In wireless communications, focusing on end-user satisfaction and maximizing network operators' revenue are emerging business challenges. In this paper, we investigate the price-based power allocation problem where, first, base stations (BSs) set prices by maximizing users' utility modeled by their mean opinion score (MOS). Then, each user's optimal power is set by maximizing operator revenue while ensuring the minimum data rate for each user. We propose a hybrid MOS-based pricing method to model users' utility instead of the conventional achievable rate approach. Our hybrid approach applies a machine learning algorithm to model the MOS of the call service in terms of the received signal strength indicator (RSSI). In terms of MOS and outage probability, our proposed method outperforms the rate-based pricing method and the conventional objective MOS models. In addition, we consider a joint admission control and price-based power allocation problem. When a new user requests to connect, a central controller determines whether or not to accept the new connection based on the system's MOS and average outage probability. The results demonstrate a trade-off between the number of users admitted and their level of satisfaction, providing operators with crucial knowledge about how to use their network resources more effectively.

In this paper, a generalization of deep learning-aided joint source channel coding (Deep-JSCC) approach to secure communications is studied. We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdroppers over complex-valued fading channels. Both scenarios of colluding and non-colluding eavesdroppers are studied. For the colluding strategy, eavesdroppers share their logits to collaboratively infer private attributes based on ensemble learning method, while for the non-colluding setup they act alone. The goal is to prevent eavesdroppers from inferring private (sensitive) information about the transmitted images, while delivering the images to a legitimate receiver with minimum distortion. By generalizing the ideas of privacy funnel and wiretap channel coding, the trade-off between the image recovery at the legitimate node and the information leakage to the eavesdroppers is characterized. To solve this secrecy funnel framework, we implement deep neural networks (DNNs) to realize a data-driven secure communication scheme, without relying on a specific data distribution. Simulations over CIFAR-10 dataset verifies the secrecy-utility trade-off. Adversarial accuracy of eavesdroppers are also studied over Rayleigh fading, Nakagami-m, and AWGN channels to verify the generalization of the proposed scheme. Our experiments show that employing the proposed secure neural encoding can decrease the adversarial accuracy by 28%.

In this paper, we propose sample complexity bounds for learning a simplex from noisy samples. A dataset of size $n$ is given which includes i.i.d. samples drawn from a uniform distribution over an unknown arbitrary simplex in $\mathbb{R}^K$, where samples are assumed to be corrupted by a multi-variate additive Gaussian noise of an arbitrary magnitude. We prove the existence of an algorithm that with high probability outputs a simplex having a $\ell_2$ distance of at most $\varepsilon$ from the true simplex (for any $\varepsilon>0$). Also, we theoretically show that in order to achieve this bound, it is sufficient to have $n\ge\tilde{\Omega}\left(K^2/\varepsilon^2\right)e^{\Omega\left(K/\mathrm{SNR}^2\right)}$ samples, where $\mathrm{SNR}$ stands for the signal-to-noise ratio and is defined as the ratio of the maximum component-wise standard deviation of the simplex (signal) to that of the noise vector. This result solves an important open problem in this area of research, and shows as long as $\mathrm{SNR}\ge\Omega\left(\sqrt{K}\right)$ the sample complexity of the noisy regime has the same order to that of the noiseless case. Our proofs are a combination of the so-called sample compression technique in (Ashtiani et al., 2018), mathematical tools from high-dimensional geometry, and Fourier analysis. In particular, we have proposed a general Fourier-based technique for recovery of a more general class of distribution families from additive Gaussian noise, which can be further used in a variety of other related problems.

Digital healthcare services are rapidly evolving for new methodologies, including hospital-to-home (H2H) services and intelligent Internet-of-Medical-Things (IIoMT). The sixth generation (6G) technology is considered as the fabric that facilitates the realization of these technologies, creating a paradigm shift towards personalized e-health services. To deal with the high security requirements and the energy constraints of 6G-enabled e-health services, we propose a lightweight learning-based key generation scheme for a pair of wireless-powered nodes in a cooperative communication system, where the legitimate nodes and the intermediate node have low-cost hardware-impaired transceivers. We utilize an echo state network (ESN) to enhance the “randomness distillation” phase, in which the legitimate parties try to obtain a common source of randomness as the raw data for key agreement. The PHY-based observed data is passed to the ESN, containing a reservoir of sparsely connected neurons to compensate for observation mismatches caused by the unbalanced hardware impairments. The output of the ESN can then be utilized to extract the secret key between e-health endpoints. Numerical experiments verify the performance gain of our proposed echo-based approach, resulting in 50% less required inference time compared with a fully-connected neural network (FCNN). Moreover, a performance gain of about 32% in terms of mean-square error (MSE) is achieved compared with a conventional PHY-only scheme.

In this paper, a learning-aided content-based image transmission scheme is proposed, where a multi-antenna source wishes to securely deliver an image to a legitimate destination in the presence of randomly-distributed passive eavesdroppers (Eves). We take into account the fact that not all regions of an image have the same importance from the security perspective. Hence, we employ a hybrid method to realize both the error-free data delivery of public regions—containing less-important pixels; and an artificial noise (AN)-aided transmission scheme for securing the confidential packets. To reinforce system’s security, fountain-based packet delivery is also adopted, where the source node encodes images into fountain-like packets prior to sending them over the air. The secrecy is achieved when the legitimate destination correctly receives the entire source packets before Eves obtain the important regions, while conforming to the latency limits of the system. Accordingly, the secrecy performance of our scheme is characterized by deriving a closed-form expression for the quality-of-security (QoSec) violation probability. Moreover, our proposed image delivery scheme leverages a deep neural network (DNN) and learns to maintain optimized transmission parameters, while achieving a low QoSec violation probability. Simulation results are provided to illustrate that our proposed learning-assisted scheme outperforms the state-of-the-arts by achieving considerable gains in terms of security and delay requirement.

Wireless secret key generation (WSKG) allows efficient key agreement protocols for securing the sixth generation (6G) wireless networks. Nevertheless, due to external adversaries or internal impairments, WSKG schemes might become vulner-able during the randomness distillation, where the legitimate nodes try to observe their source of common randomness. In this paper, we investigate the WSKG scheme with legitimate parties suffering from hardware impairments (HIs), while an active adversary acts as a man-in-the-middle (MiM) via injecting fake pilot signals. We first utilize randomized pilots to overcome the MiM. We also leverage the concept of recurrent neural networks (RNNs) to further enhance the randomness distillation. More specifically, the long short-term memory networks (LSTMs)-as a well-established type of RNNs-are implemented to learn the long-term dependencies between the observations of legitimate parties. The achievable secret key rate (SKR) and the impact of MiM on system's performance are analyzed. Our numerical results verify the performance gain of our proposed learning-based approach compared with the state-of-the-art methods and provide useful insights on system design. We show that our RNN-based approach achieves 30% and 15% improvement in terms of observation mismatches compared with the naïve scheme and the fully-connected benchmarks, respectively.

Software defined networking (SDN) is a promising technology in fifth generation wireless networks (5G) where due to the adoption of a centralized SDN-controller, resources such as processing and storage, can be utilized in an optimal manner. Although SDN was first considered with a logically centralized controller, due to delay, reliability, and scalability challenges, moving towards multiple distributed controllers is inevitable. In distributed control schemes, an assignment that associates a controller with each switch leads to three challenges of (1) Computational complexity, since the assignment is an NP-hard problem, (2) Resource and energy efficiency, to obtain an assignment with the lowest number of controllers in order to reduce resource and energy consumption, and (3) Dynamicity, where a dynamic approach of assignment is required to adapt to the network’s traffic changes. In this paper, we investigate the controller-switch assignment problem given the aforementioned challenges, and propose efficient algorithms for static and dynamic scenarios, that even achieve quantitative optimality guarantees in special cases. As shown through simulations, the proposed lower complexity algorithms not only outperform earlier works but also approach the performance of exhaustive search schemes, in some scenarios.

A multi-antenna broadcast channel scenario is considered where a base station delivers contents to cache-enabled user terminals. A joint design of coded caching (CC) and multigroup multicast beamforming is proposed to benefit from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. The developed general content delivery strategies utilize the multiantenna multicasting opportunities provided by the CC technique while optimally balancing the detrimental impact of both noise and inter-stream interference from coded messages transmitted in parallel. Flexible resource allocation schemes for CC are introduced where the multicast beamformer design and the receiver complexity are controlled by varying the size of the subset of users served during a given time interval, and the overlap among the multicast messages transmitted in parallel, indicated by parameters α and β, respectively. Degrees of freedom (DoF) analysis is provided showing that the DoF only depends on α while it is independent of β. The proposed schemes are shown to provide the same degreesof-freedom at high signal-to-noise ratio (SNR) as the state-of-art methods and, in general, to perform significantly better, especially in the finite SNR regime, than several baseline schemes.

This paper considers a cloud-RAN architecture with cache-enabled multi-antenna Edge Nodes (ENs) that deliver content to cache-enabled end-users. The ENs are connected to a central server via limited-capacity fronthaul links, and, based on the information received from the central server and the cached contents, they transmit on the shared wireless medium to satisfy users' requests. By leveraging cooperative transmission as enabled by ENs' caches and fronthaul links, as well as multicasting opportunities provided by users' caches, a close-to-optimal caching and delivery scheme is proposed. As a result, the minimum Normalized Delivery Time (NDT), a high-SNR measure of delivery latency, is characterized to within a multiplicative constant gap of 3/2 under the assumption of uncoded caching and fronthaul transmission, and of one-shot linear precoding. This result demonstrates the interplay among fronthaul links capacity, ENs' caches, and end-users' caches in minimizing the content delivery time.

MOTIVATION: The molecular mechanisms of self-organization that orchestrate embryonic cells to create astonishing patterns have been among major questions of developmental biology. It is recently shown that embryonic stem cells (ESCs), when cultured in particular micropatterns, can self-organize and mimic the early steps of pre-implantation embryogenesis. A systems-biology model to address this observation from a dynamical systems perspective is essential and can enhance understanding of the phenomenon. RESULTS: Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principles of Waddington epigenetic landscape with cell-cell communication, in order to enable pattern and tissue formation. We have shown the sufficiency of a simple mechanism by using a minimal number of parameters in the model, in order to address a variety of experimental observations such as the formation of three germ layers and trophectoderm, responses to altered culture conditions and micropattern diameters and unexpected spotted forms of the germ layers under certain conditions. Moreover, we have tested different boundary conditions as well as various shapes, observing that the pattern is initiated from the boundary and gradually spreads towards the center. This model provides a basis for in-silico modeling of self-organization. AVAILABILITY AND IMPLEMENTATION: https://github.com/HFooladi/Self_Organization. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Caching of the popular content in the storages of local nodes near to the users has been proposed as a promising approach to handle the network data traffic and the backhaul constraint of access points in the cellular networks. These local storages, also called caches or helpers nodes, are the nodes with the limited communication range but with considerable storage capacities which are generally established condensed to realise the above goals. This dense distribution makes possible the collaboration among the helper nodes for exchanging their embedded files and delivery of the requested files to the users. In this study, to manage the traffic in smaller areas and also to establish more users' accesses to their requested files, clustered dense helper scenario is considered. More precisely, the helper nodes are grouped in disjoint clusters, through the clustering strategy induced by Matern hard core point process. Then the closed‐form expressions based on stochastic geometry are derived for some performance metrics of the network. The analytical derivations are validated with Monte Carlo simulations which also compare two cases of clustered dense helper nodes and fewer helper nodes without clustering scenarios. The results show the advantage of the former from the outage probability perspective.

In this paper, we consider K-user interference channels with M antennas per node and with backhaul collaboration in one side (among the transmitters or among the receivers), for M, K ∈ N, and investigate the tradeoff between the rate in the channel versus the communication load in the backhaul. In this investigation, each node is equipped with M antennas and we focus on a first order approximation result, where the rate of the wireless channel is measured by the degrees of freedom (DoF) per user, and the load of the backhaul is measured by the entropy of backhaul messages per user normalized by log of transmit power, at high power regimes. This tradeoff is fully characterized for the case of even values of K and approximately characterized for the case of odd values of K, with vanishing approximation gap as K grows. To achieve DoF of M per user, this result establishes the asymptotic optimality of the most straightforward scheme, called central processing, in which the messages are collected at one of the nodes, centrally processed, and forwarded back to each node. In addition, this result shows that the gain of the schemes, relying on distributed processing, through pairwise communication among the nodes (e.g., cooperative alignment) does not scale with the size of the network. For the converse, we develop a new outer-bound on the tradeoff based on splitting the set of collaborative nodes (transmitters or receivers) into two subsets and assuming full cooperation within each group. We further present a sufficient condition on the wireless channel connectivity, which although more relaxed, guarantees the validity of the above tradeoff. Finally, we show that verifying this condition takes a polynomial time in the network size.

We investigate the potentials of applying the coded caching paradigm in wireless networks. In order to do this, we investigate physical layer schemes for downlink transmission from a multiantenna transmitter to several cache-enabled users. As the baseline scheme, we consider employing coded caching on the top of max-min fair multicasting, which is shown to be far from optimal at high-SNR values. Our first proposed scheme, which is near-optimal in terms of DoF, is the natural extension of multiserver coded caching to Gaussian channels. As we demonstrate, its finite SNR performance is not satisfactory, and thus we propose a new scheme in which the linear combination of messages is implemented in the finite field domain, and the one-shot precoding for the MISO downlink is implemented in the complex field. While this modification results in the same near-optimal DoF performance, we show that this leads to significant performance improvement at finite SNR. Finally, we extend our scheme to the previously considered cache-enabled interference channels, and moreover we provide an ergodic rate analysis of our scheme. Our results convey the important message that although directly translating schemes from the network coding ideas to wireless networks may work well at high-SNR values, careful modifications need to be considered for acceptable finite SNR performance.

A single cell downlink scenario is considered where a multiple-antenna base station delivers contents to cache-enabled user terminals. Using the ideas from multi-server coded caching (CC) scheme developed for wired networks, a joint design of CC and general multicast beamforming is considered to benefit from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. The proposed multicast beamforming strategies utilize the multiantenna multicasting opportunities provided by the CC technique and optimally balance the detrimental impact of both noise and inter-stream interference from coded messages transmitted in parallel. The proposed scheme is shown to provide the same degrees-of-freedom at high SNR as the state-of-art methods and, in general, to perform significantly better than several baseline schemes including, the joint zero forcing and CC, max-min fair multicasting with CC, and basic unicasting with multiuser beamforming.

A wireless coded caching (CC) setup is considered, where a multi-antenna transmitter delivers contents to multiple cache-enabled users. Exploiting multicasting opportunities provided by the coded caching paradigm, novel interference management schemes are proposed by assigning carefully designed beamforming vectors to different multicast messages. Thereby, the proposed design benefits from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. In addition, a novel multicast mode selection scheme is proposed which determines the optimum multicast group sizes providing the best complexity-performance tradeoff for a given SNR range. While the proposed scheme exhibits the same near-optimal degrees-of-freedom (DoF) performance as previously proposed methods, it will surpass them at the practical finite SNR regimes. In addition to reducing the complexity, the proposed mode selection feature also provides significantly better rate than previously proposed schemes.

In future wireless networks, network functions virtualization lays the foundations for establishing a new dynamic resource management framework to efficiently utilize network resources. In this paper, a network service can be viewed as a chain of virtual network functions (VNFs), called a service function chain (SFC), served via placement, admission control (AC), and embedding into network infrastructure, based on the resource management objectives and the state of network. To fully exploit such a potential and reach higher network performance, resource management stages should be jointly performed. To this end, two main challenges are: how to present a system model that formulates the desired resource allocation problem for different types of SFCs as well as different features, and how to tackle the computational complexity of the problem and solve it in a tractable manner. In this paper, we address these two issues and solve the joint problem of AC and SFC embedding. We introduce a comprehensive system model, and formulate the joint task as a mixed integer linear programming. This formulation encompasses splittable VNF and multi-path routing scenarios. We employ relaxation, reformulation, and successive convex approximation methods to solve the problem. Simulation results demonstrate that the proposed schemes outperform the earlier works.

In this paper, we study the problem of delay minimization in network function virtualization-based networks. In such systems, the ultimate goal of any request is to compute a sequence of functions in the network, where each function can be computed at only a specific subset of network nodes. In conventional approaches, for each function, we choose one node from the corresponding subset of the nodes to compute that function. In contrast, in this paper, we allow each function to be computed in more than one node, redundantly in parallel, to respond to a given request. We argue that such redundancy in computation not only improves the reliability of the network but also, perhaps surprisingly, reduces the overall transmission delay. In particular, we establish that by judiciously choosing the subset of nodes which compute each function, in conjunction with a linear network coding scheme to deliver the result of each computation, we can characterize and achieve the optimal end-to-end transmission delay. In addition, we show that using such technique, it is possible to significantly reduce the transmission delay as compared to the conventional approaches. In fact, in some scenarios, such reduction can even scale with the size of the network, where by increasing the number of nodes that can compute the given function in parallel by a multiplicative factor, the end-to-end delay will also decrease by the same factor. Moreover, we show that while finding the subset of nodes for each computation, in general, is a complex integer program, approximation algorithms can be proposed to reduce the computational complexity. In fact, for the case where the number of computing nodes for a given function is upper bounded by a constant, a dynamic programming scheme can be proposed to find the optimum subsets in polynomial times. Our numerical simulations confirm the achieved gain in performance in comparison with conventional approaches.

Considering cache enabled networks, optimal content placement minimizing the total cost of communication in such networks is studied, leading to a surprising fundamental 0-1 law for non-redundant cache placement strategies, where the total cache sizes associated with each file does not exceed the file size. In other words, for such strategies, we prove that any non-redundant cache placement strategy can be transformed, with no additional cost, to a strategy in which at every node, each file is either cached completely or not cached at all. Moreover, we obtain a sufficient condition under which the optimal cache placement strategy is in fact non-redundant. This result together with the 0-1 law reveals that situations exist, where optimal content placement is achieved just by uncoded placement of whole files in caches.

A semicentralized joint cell muting and user scheduling scheme for interference coordination in a multicell network is proposed under two different temporal fairness criteria. In the proposed scheme, at a decision instant, each base station (BS) in the multicell network employs a cell‐level scheduler to nominate one user for each of its inner and outer sections and their available transmission rates to a network‐level scheduler which then computes the potential overall transmission rate for each muting pattern. Subsequently, the network‐level scheduler selects one pattern to unmute, out of all the available patterns. This decision is shared with all cell‐level schedulers which then forward data to one of the two nominated users provided the pattern they reside in was chosen for transmission. Both user and pattern selection decisions are made on a temporal fair basis. Although some pattern sets are easily obtainable from static frequency reuse systems, we propose a general pattern set construction algorithm in this paper. As for the first fairness criterion, all cells are assigned to receive the same temporal share with the ratio between the temporal share of a cell center section and that of the cell edge section being set to a fixed desired value for all cells. The second fairness criterion is based onmax-min temporal fairnessfor which the temporal share of the network‐wide worst case user is maximized. Extensive numerical results are provided to validate the effectiveness of the proposed schemes and to study the impact of choice of the pattern set.

We consider a K-user Interference Channel where each transmitter is interested in conveying a message to its corresponding receiver. In addition, we assume a fully connected noiseless backhaul network through which receivers can collaborate and help each other recover their desired messages. In this paper, we fully characterize the trade-off between the rate in wireless link (per user) in terms of degrees of freedom (DoF) versus backhaul load (per user) for large values of K. In particular, we characterize the optimal trade-off for all values of K, where K is an even number. For odd values of K, we characterize the trade-off within a gap of 2(k - 1)/k(k + 1), which goes to zero as K increases. For achievability we use time-sharing between two corner points: (i) using interference alignment for the case where backhaul load is zero, and (ii) collecting a quantized version of all the received signals at one of the receivers to jointly decode the messages, for the case where DoF of one per user is desired. For the converse, we develop a new outer-bound based on the results from two-user multiple antenna interference channel with limited backhaul cooperation. Recently, it was shown that for the case of three-user interference channel, the optimal trade-off is achieved by some sort of alignment in the backhaul messaging, known as Cooperation Alignment. Our result shows that unlike the gain of interference alignment, the gain of cooperation alignment does not scale with the number of users K.

In this paper we consider a single-cell downlink scenario where a multiple-antenna base station delivers contents to multiple cache-enabled user terminals. Based on the multicasting opportunities provided by the so-called Coded Caching technique, we investigate three delivery approaches. Our baseline scheme employs the coded caching technique on top of max-min fair multicasting. The second one consists of a joint design of Zero-Forcing (ZF) and coded caching, where the coded chunks are formed in the signal domain (complex field). The third scheme is similar to the second one with the difference that the coded chunks are formed in the data domain (finite field). We derive closed-form rate expressions where our results suggest that the latter two schemes surpass the first one in terms of Degrees of Freedom (DoF). However, at the intermediate SNR regime forming coded chunks in the signal domain results in power loss, and will deteriorate throughput of the second scheme. The main message of our paper is that the schemes performing well in terms of DoF may not be directly appropriate for intermediate SNR regimes, and modified schemes should be employed.

BACKGROUND: Current development of sequencing technologies is towards generating longer and noisier reads. Evidently, accurate alignment of these reads play an important role in any downstream analysis. Similarly, reducing the overall cost of sequencing is related to the time consumption of the aligner. The tradeoff between accuracy and speed is the main challenge in designing long read aligners. RESULTS: We propose Meta-aligner which aligns long and very long reads to the reference genome very efficiently and accurately. Meta-aligner incorporates available short/long aligners as subcomponents and uses statistics from the reference genome to increase the performance. Meta-aligner estimates statistics from reads and the reference genome automatically. Meta-aligner is implemented in C++ and runs in popular POSIX-like operating systems such as Linux. CONCLUSIONS: Meta-aligner achieves high recall rates and precisions especially for long reads and high error rates. Also, it improves performance of alignment in the case of PacBio long-reads in comparison with traditional schemes.

We study the pilot design problem for sparse channel estimation in OFDM systems where multiple channels are estimated at a single antenna receiver. Such design is applicable to downlink of massive-MIMO systems and also to scenarios where multiple users transmit to a base station at the same carrier frequency. In our design, we deviate from the conventional orthogonal pilot arrangements by assigning the same pilot subcarriers to all transmitters. In the proposed setting, the achieved improvement in spectral efficiency (by reducing pilot overhead) may come at the expense of a more challenging channel estimation block at the receiver. To address this challenge and distinguish between different signals that are arriving at the receiver at the same subcarrier, we propose to select pilot subcarriers through minimizing the coherence of the associated Fourier submatrix, as well as properly assigning different pilot values (complex numbers) to each individual transmitter. We demonstrate that if the channels are sparse enough in time domain, there are simple sparse recovery techniques to simultaneously estimate all the channels, although all transmitters share the same pilot subcarriers. Simulation results demonstrate that the proposed design outperforms existing methods in terms of both mean-square channel estimation error and bit error rate.

We consider a cellular wireless system including several interfering multi-user multi-antenna uplink channels, where the base station of each cell has to locally recover the messages of its corresponding users. We use a linear Wyner model, where each base station experiences interference only from the users in the two neighboring cells. Each base station is connected to the two nearby base stations through a backhaul link. The objective is to achieve the maximum degrees of freedom per cell, with minimum aggregated load in the backhaul. We propose a successive cooperative alignment scheme, in which each base station forms backhaul messages by combining the previous received backhaul messages with the received signals at its wireless terminal. By some alignment schemes in signaling over wireless links as well as developing proper messages over backhaul links, each base station can help the neighboring base stations to peel off the aggregated interference with minimum help. This is done without propagating interference throughout the network. In this conference paper, we focus on linear one shot schemes and prove the optimality of the proposed scheme, in a robust set-up for a system with two antennas per base station and two users per cell, where each user is equipped with two antennas.

Caching has been widely considered an efficient way of reducing and balancing the growing traffic in communications networks in recent years. The cache network of interest consists of one content server connected via a shared link to a number of caching nodes, also known as a single bottleneck caching network. In this paper, for the first time, the stochastic requests traffic model in such networks is considered and a performance analysis is provided based on such a realistic assumption. In addition, we introduce new comprehensive performance metrics, which simultaneously take into account, the cache hit probability, load on the bottleneck link, and requests arrival rates. The main contribution of this paper is to present a system model based on queuing theory and provide an analysis of the stability, maximum stable throughput, load on the bottleneck link, and average response delay for various coded and uncoded caching schemes. Moreover, we propose a novel hybrid scheme that improves the shared link utilization factor, maximum stable throughput, and delay of single bottleneck caching networks compared to existing methods. Our results, validated against simulations and real trace-driven experiments, provide interesting insights into the performance of single bottleneck caching networks.

In this paper, we consider multiple cache-enabled clients connected to multiple servers through an intermediate network. We design several topology-aware coding strategies for such networks. Based on the topology richness of the intermediate network, and types of coding operations at internal nodes, we define three classes of networks, namely, dedicated, flexible, and linear networks. For each class, we propose an achievable coding scheme, analyze its coding delay, and also compare it with an information theoretic lower bound. For flexible networks, we show that our scheme is order-optimal in terms of coding delay and, interestingly, the optimal memory-delay curve is achieved in certain regimes. In general, our results suggest that, in the case of networks with multiple servers, type of network topology can be exploited to reduce service delay.

Femtocell networks are a common means of offloading in current mobile networks. Game theoretic and learning-based self-organization schemes are known as two viable approaches for interference control in co-existing macro/femtocell networks. Nevertheless, extensions of these schemes to concurrent multi-agent scenarios often face several challenges including high computational complexity, cyclic behaviors, and lack of convergence. This paper addresses such challenges by introducing local games based on a graph representation of the network. The goal of the games is to optimize the aggregate network capacity through power control. Fairness among femtocells and quality of service (QoS) for macrocell users are also addressed. Finally, we prove that our algorithm converges to an equilibrium point.

The authors consider the effect of caching in interference‐limited wireless networks where fading is the dominant channel effect. First, the authors propose a one‐hop transmission strategy for cache‐enabled wireless networks, which is based on exploiting multi‐user diversity gain. Then, they derive a closed‐form result for throughput scaling of the proposed scheme in large networks, which reveals the inherent trade‐off between cache memory size and network throughput. Their results show that substantial throughput improvements are achievable in networks with sources equipped with large cache size, compared with previous works where each transmitter could just store one file. Also, the authors provide extensive simulations through which they analyse network throughput and verify their analytical results. Their simulations suggest that the scaling results also hold true even by introducing correlation between channels, making the results more general.

In this paper, we use delay-amplify-and-forward relays in the uplink direction of a coordinated multi-point network based on orthogonal frequency division multiplexing. The key novelty is that we assume delay from users to different base stations (BSs), in general, may exceed cyclic prefix (CP) length as might be the case in large cells and cannot be completely compensated by traditional timing advance strategies. Consequently, interchannel interference and intersymbol interference may arise under such assumptions. We first optimize the adjustable delays added at users, relays and BS nodes in order to reduce the effective delay spread at the receive sites, then we optimize relays' weights and post-processing matrices at BS nodes to reduce the residual interference. For this purpose, we propose an iterative method that at each step first fixes either the relay weights or the post-processing matrices and then optimizes the signal-to-interference-plus-noise-ratio (SINR) over the other variable. Furthermore, we investigate the optimization problem of minimizing sum mean square error (SMSE) at the receive side under total relay power constraint. Simulation results show significant improvement in terms of SINR and SMSE in comparison with traditional systems in which no relay is deployed and the limited CP length is not addressed properly.

Many applications in wireless networks impose locally distributed traffic on the network. We consider the effect of traffic localization on throughput of large wireless networks in the scaling sense. We show that by careful exploitation of traffic localization, one can enhance the throughput of various schemes in wireless networks. Also, the important case of Small World traffic distribution in wireless networks is addressed. In this type of traffic, in addition to the local traffic, a non-negligible amount of global traffic should be handled by the network. It is shown that a non-negligible amount of network-wide traffic can be tolerated by network without any performance degradation in the scaling sense.

We consider the effect of caching in wireless networks where fading is the dominant channel effect. First, we propose a one-hop transmission strategy for cache-enabled wireless networks, which is based on exploiting multi-user diversity gain. Then, we derive a closed-form result for throughput scaling of the proposed scheme in large networks, which reveals the inherent trade-off between cache memory size and network throughput. Our results show that substantial throughput improvements are achievable in networks with sources equipped with large cache size. We also verify our analytical result through simulations.

The capacity of multiple-input multiple-output (MIMO) systems in vehicular communication is investigated throughout this paper. The goal is to consider a link between a vehicle-side and a road-side unit in presence of line-of-sight in a way that the road-side end uses all available antennas but the vehicle-side end chooses a subset of available antennas such that the link capacity becomes maximized. In order to maximize the channel capacity a criterion is derived that forces some circumstances about the configuration of antenna array in vehicle-side. If such circumstances are satisfied the channel capacity will be at its maximum. By proposing an antenna selection algorithm, the conditions will be satisfied and the channel capacity will become maximized.

In this paper, we study the sum mean square error (SMSE) performance of all users by applying joint MIMO filter-and-forward relay and post-processing at base stations (BSs) for uplink coordinated multi-point (CoMP) reception in case of users-to-relay and relay-to-BSs frequency-selective fading channels. In order to combat the interference due to frequency-selectivity of channels, MIMO relay passes its received signals through a finite impulse response (FIR) filter. For further reducing SMSE, each BS applies a post-processing to its received signals. The goal is minimizing the SMSE subject to transmitted power constraint of MIMO relay. An iterative algorithm is proposed to jointly optimize the filter matrices of the relay and post-processing matrices of the coordinated BSs. Simulation results show effectiveness of the proposed scheme in comparison with previous schemes utilizing joint precoding relay and post-processing BS.

In this study, the authors propose a novel power allocation scheme in time division multiple access (TDMA)‐based distributed multiple‐input multiple‐output (MIMO) channels. By modelling the problem with a TDMA MIMO interference channel, the authors have derived closed‐form expressions for power and time slot assigned to each MIMO transmission. Although our scheme is sub‐optimal, with the aid of Jensen's inequality, it is shown to be superior to solutions based on channel inversion. In addition, the proposed scheme is compared with the optimal solution, and also with the scheme proposed by Dohler. By applying our proposed power allocation to the hierarchical cooperation strategy, the authors have shown that this scheme significantly improves the overall network throughput compared with the channel inversion solution. However, the improvement gain (which is at high‐signal‐to‐noise ratio values) reduces when the method is compared with Dohler's solution.

In this paper, we consider the problem of optimum rate adaptation and power allocation to maximize the delay-limited throughput (DLT) in a time-correlated 2×1 MISO Rayleigh fading channel with Hybrid ARQ in Chase-Combining mode (HARQ-CC). To this aim, we have first formulated the DLT equation based on the outage probability for 2×1 MISO-HARQ channel. We show that exact analytical calculation of the outage probability is not tractable. Therefore, we approximate the outage probability using a log-normal approximation approach. Based on such approximation, optimal rate and power allocations are derived. Finally, using Monte-Carlo simulation, we show that the approximation method provides results which are very close to the exact solution.

In this paper, we use the ascending reverse auction method in incentive-based cooperative networks in order to increase the cooperation level of malicious users. Our approach integrates the reverse auction method with traditional pricing and reputation based algorithms and uses fuzzy thresholds to increase freedom of service providers in making the decision whether to cooperate or not. In contrast with earlier works, we do not concentrate on a specific network or protocol. The simulation results show that while the average welfare of malicious users is decreased dramatically, the average welfare of all users does not decrease significantly.

In this paper, we consider a cellular cognitive radio network that uses opportunistic spectrum access for its transmission in the downlink direction from base stations toward cognitive radio users. Since the spectrum is licensed to a primary network, proper resource management should be employed in the secondary network to protect primary users against excessive interference while providing acceptable utilization for secondary users. We use game theory to propose a distributed method for joint frequency and power allocation in the downlink of cognitive radio networks. Earlier works in this area were based on centralized optimization-based approaches, whereas our proposed method solves the problem of joint resource allocation in a distributed way at lower complexity and less message passing. Comparing the proposed decentralized method with a centralized method shows that our proposed joint resource allocation scheme results in significantly less power consumption while supporting more secondary users.

Smartphones can be viewed as resource pools capable of communicating with their outside world. Such communication capabilities provide unique opportunity for sharing resources, which would result in new possibilities for end users. In this paper, we present a new framework designed for managing mobile local networks, also referred to as Mobile Clouds. In this framework, mobility of nodes is managed by selecting one leader for each cloud, which is responsible for all cloud-level decisions, as well as processing all incoming data from other users. We also evaluate and discuss our proposed framework under different scenarios.

In this paper, we derive the rate of route discovery process for finding a randomly chosen destination in large wireless networks. The well-known protocol for route discovery is flooding in which each node simply rebroadcasts the route request packet once. Rate of route discovery process is defined as the inverse of time between transmitting two successive route request packets which shows how fast a node can request a route to its destination. Discovery time is another parameter of interest. For a single node discovering routes to its destination, at most the rate of È (1/3√n) is feasible when n identical nodes are optimally placed in network area and the discovery time is of order È(3√n). Achievability of such rate is obtained through optimal placement and hierarchical scheduling of nodes transmissions.

Underutilisation and scarcity of the available bandwidth have led to the idea of spectrum sharing as primary users partially transfer their rights for spectrum access to others in return for rewards. In this study, we introduce a new distributed game-theoretic approach in order to analyse the dynamic spectrum leasing problem in the uplink side of cellular cognitive radio networks. In such scenarios, it is necessary to define utility functions for primary and secondary users according to their incentives in order to model such interaction properly. We prove the existence of the Nash equilibrium point as well as its uniqueness for our model in the framework of standard power control algorithms and demonstrate fast convergence rate of the proposed method through numerical results. Owing to cellular architecture of the problem, the improvement in power consumption and signal-to-interference ratio (SIR) levels as users are given the opportunity to switch between different base stations is then investigated. Furthermore, we propose a simple admission control scheme and show the resulting performance improvement through simulations. In addition, the proposed framework provides the possibility of investigating trade-offs in terms of various system parameters.

Power control mechanisms play a critical role in underlay cognitive radio networks where primary users' quality of service should not be degraded by the secondary users transmission. Consequently, the inflicted interference power of the secondary transmitters on the primary receivers should be less than certain threshold levels that is required to be considered in designing power control algorithms for such networks. In this paper, we first propose a power reduction algorithm for the set of secondary transmitting links in order to guarantee that the members of this set do not violate the interference constraints set by primary users while maintaining their specific SINR quality of service requirements. In addition, in scenarios where it is not possible to meet the constraints while all transmitters are active, a distributed stochastic elimination algorithm is proposed that turns off transmission of a selected number of links. The main advantage of the proposed elimination algorithm is that it can be used in a distributed manner resulting in a more practical solution.

The focus of the paper is on cross-layer transmission based on rateless coding for relay networks. In the proposed method, rateless coding is used at the packet level and a separate physical layer coding is used at the physical layer. As rateless codes are originally designed and optimized for erasure channels, using these codes at the physical layer can lead to significant performance degradation. The proposed scheme has the advantage of a cross-layer design which uses a rateless code at the packet level modeled by an erasure channel. Based on the analysis and simulation results provided in this paper, the cross-layer scheme results in earlier successful decoding at the destination.

In this paper, we introduce a new cost function for SIR-Based uplink power control algorithms that takes into account transmission power level as well as target Signal-to-Interference Ratio (SIR) deviation. Existence of Nash equilibrium point, its uniqueness and the algorithm convergence are proved in the framework of so-called standard power control schemes. As shown through simulations, the proposed method results in significant transmit power reduction while achieving almost the same average SIR level. In addition, it is shown that the new scheme results in improved overall network fairness in comparison with another algorithm.

In this paper, we analyze the role of the number of quantization bits on the third phase of the hierarchical cooperation scheme proposed by Ozgur et al. in 2007. The hierarchical cooperation scheme has a digital architecture and requires quantization of MIMO (Multiple-Input Multiple-Output) observations in the third phase. The choice of the number of quantization bits Q has direct effect on the pre-constant factor of the network throughput. By increasing the number of quantization bits, the MIMO capacity in the second phase increases at the expense of higher computational complexity of the third phase. We investigate such trade-off and show that there is an optimum number of quantization bits which maximizes the throughput. In addition, it will be shown that due to quantization noise, it is not possible to increase throughput pre-constant factor as large as desired by increasing transmit power. As a result, there is a saturation value for the pre-constant value. Finally, we will demonstrate that by proper scaling of Q as Q = β log(SNR) for any β independent of SNR, an increasing pre-constant factor of order √(log (SNR)) can be achieved.

In this paper, the impact of both time offsets (TOs) and carrier frequency offsets (CFOs) on the average output signal-to-interference-plus-noise ratio (SINR) in a frequency selective channel is investigated with a novel approach. First, TOs are partitioned into Ave cases based on how they occur. Then the mathematical models for different cases are derived. Considering CFOs and benefiting from the obtained models, closed-form expression for the SINR in each case is derived. It is proved that, SINR is independent of the observed subcarrier for the given TO and CFO. Besides, it is shown that the performance of the affected system deteriorates rapidly as the number of subcarriers increases. It is also revealed that, as far as the guard interval is longer than the channel delay spread, the SINR is not much sensitive to the number of channel taps and power delay profile.

The authors consider the problem of finding a minimum cost multicast sub-graph based on network coding, where delay values associated with each link, limited buffer-size of the intermediate nodes and link capacity variations over time are taken into account. The authors consider static multicast (where membership of the multicast group remains constant for the duration of the connection) in lossless wireline networks. For such networks, first the continuous-time (asynchronous packet transmissions) model is formulated. Subsequently, the discrete-time model for the synchronous packet transmissions scenario is derived. Then, by using an auxiliary time-expanded network, a decentralised algorithm in the presence of link delays, limited buffer-size and time-varying link capacity is proposed. The proposed algorithm when coupled with decentralised code construction schemes results in a fully decentralised approach for achieving minimum-cost in such multicast networks. Also, how adding buffering capability at intermediate nodes reduces the overall cost of the optimal sub-graph is discussed in this study. In addition, as will be shown, inclusion of buffering capacity at intermediate nodes makes it possible to find minimum multicast solution in scenarios that such solutions do not exist otherwise.

Exact spread-spectrum code tracking in the presence of multipath is an important issue in distance and time measurement systems. Different tracking algorithms based on feedback, feedforward or a combination of them reduce multipath effects, however, the accuracy of the tracking in presence of short-delay multipath signals is still a problem. In this paper, we consider feedback delay lock loops (DLL) and gating function schemes that have shown better performance with lower complexity under multipath. We develop a shot-noise process model for the control signal of a DLL in the presence of multipath signals and determine the range of tracking errors they cause. Then, we propose a method based on computation of parallel forward and backward correlations with the incoming signal in order to reduce tracking errors due to multipath. As will be shown, the multipath signals do not change the zero-crossing of the forward correlation output which is used to estimate the exact phase of the received signal. The new structure alleviates the problem of short-delay multipath while adding not much complexity to the system.

In this paper, we analyze the cooperative diversity of amplify-and-forward half-duplex relay networks under long-term power constraints. The system model we consider consists of one transmit-receive pair and an arbitrary number of relays. This paper analyzes bit error rate and network diversity for two different power distribution scenarios in network. Using the derived network error probability, the distributed signal to noise ratio for network is introduced.

Multi-hop random access networks have received much attention because of their distributed nature which facilitates deploying many new applications over the sensor and computer networks. Recently, utility maximisation framework is applied in order to optimise performance of such networks; however, proposed algorithms result in large transmission delays. In this study, the authors will analyse delay in random access multi-hop networks and solve the delay-constrained utility maximisation problem. We define the network utility as a combination of rate utility and energy cost functions and solve the following two problems: ‘optimal medium access control with link delay constraint’ and, ‘optimal congestion and contention control with end-to-end delay constraint’. The optimal tradeoff between delay, rate and energy is achieved for different values of delay constraint and the scaling factors between rate and energy. Eventually, linear and super-linear distributed optimisation solutions are proposed for each problem and their performance are compared in terms of convergence and complexity.

A new analytical model based on Parallel Space-Time Markov Chain concept is presented for performance evaluation of IEEE 802.11 DCF MAC in multi-hop ad hoc networks. The proposed framework is able to model hidden-terminal problem and the aforementioned unreachability phenomena in such networks precisely. To the best of our knowledge, the proposed framework is one of the leading approaches in finite load analysis of multi-hop ad hoc networks taking into account backoff and post-backoff processes, in addition to the MAC sub-layer transmission queue status in an integrated fashion. Based on the proposed model, we provide an extensive throughput performance evaluation of practical multi-hop networks based on IEEE 802.11 DCF MAC.

The use of cooperative schemes in wireless networks has recently attracted much attention in scenarios where application of multiple-antenna systems is impractical. In such scenarios, the requirement of having full channel state information (CSI) at the receiver side can be relaxed by using differential distributed (DD) transmission schemes. However, in the DD schemes proposed so far, the decoding complexity as well as the delay requirements increase with the number of relays. In this paper, we propose a low-rate feedback-based DD approach (with one-bit feedback per relay) that enjoys full diversity, linear maximum likelihood (ML) decoding complexity, and unrestrictive delay requirements. In addition, the proposed feedback scheme does not require any CSI knowledge at the receiver, and its implementation is simple. Computer simulations demonstrate substantial performance improvements of the proposed techniques as compared to several popular cooperative transmission schemes.

Rate, energy and delay are three main parameters of interest in ad-hoc networks. In this paper, we discuss the problem of maximizing network utility and minimizing energy consumption while satisfying a given transmission delay constraint for each packet. We formulate this problem in the standard convex optimization form and subsequently discuss the tradeoff between utility, energy and delay in such framework. Also, in order to adapt for the distributed nature of the network, a distributed algorithm where nodes decide on choosing transmission rates and probabilities based on their local information is introduced.

In this paper, we investigate the error probability, diversity analysis and outage capacity of a general multiple-antenna equal-gain receive combining (EGC) scheme with an arbitrary number of antennas at high signal-to-noise (SNR) ratio. As will be shown, the proposed approach which is based on Taylor expansion provides a powerful tool for analysis of EGC schemes in terms of diversity and outage capacity. The approximations used in this approach lead to low complexity solutions that are very close to exact values, as shown by the simulation results.

Counter-based broadcasting improves the performance of flooding by preventing the network from being swamped by a large number of rebroadcast messages. In such approach, the rebroadcast decision is made based on counting duplicated packets in order to restrict the number of extra retransmissions. In this article, using a rigorous theoretic analysis, an upper bound on the performance of counter-based broadcasting for saturated networks is derived. Our model assumes a CSMA-based static multi-hop ad-hoc network. The derived analytical model can be used to predict the behavior of counter-based broadcasting schemes. It especially provides the required insight on the impact of different network parameters on the overall system performance. Simulation results are also provided to validate the proposed models and demonstrate the tightness of the achieved upper bound.

In this paper, using the so-called parallel space-time Markov chain (PSTMC) framework we analyze the IEEE 802.11 Distributed Coordination Function (DCF) frame service time, jitter, and queuing delay in a single-hop non-saturated wireless network. PSTMC framework provides the possibility of simultaneous modeling of backoff and post-backoff procedures, in addition to the transmission queue status of a non-saturated 802.11 station. To the best of our knowledge, the presented contribution is the first analysis of service time, i.e., access delay and retransmission delay, plus queuing delay at the same time, when the precise modeling of binary exponential backoff (BEB) scheme in medium access control (MAC) layer is the main issue of concern. The model is validated by extensive simulations, showing its remarkable level of accuracy.

In this work we have examined the problem of consensus information fusion from a novel point of view, challenging the fundamental assumption of mutual trust among the fusion parties. In quest for a method to make information fusion possible while preserving the mutual confidentiality and anonymity of the fused information even in case of collusion of the malicious nodes, we propose the Blind Information Fusion Framework (BIFF). In BIFF, which is a secure information fusion framework, the nodes are not aware of the actual information they are processing, yet converging to the intended result(s). We formulate BIFF according to the anonymization transform and discuss its robustness against collusions for privacy violation. As an example, two secure consensus averaging methods are formulated according to BIFF.

In this paper we consider the issue of energy efficiency in random access networks and show that optimizing transmission probabilities of nodes can enhance network performance in terms of energy consumption and fairness. First, we propose a heuristic power control method that improves throughput, and then we model the Utility Constrained Energy Minimization (UCEM) problem in which the utility constraint takes into account single and multi node performance. UCEM is modeled as a convex optimization problem and Sequential Quadratic Programming (SQP) is used to find optimal transmission probabilities. Numerical results show that our method can achieve fairness, reduce energy consumption and enhance lifetime of such networks.

Energy consumption is a main issue of concern in wireless networks. Energy minimization increases the time that networks' nodes work properly without recharging or substituting batteries. Another criterion for network performance is data transmission rate which is usually quantified by a network utility function. There exists an inherent tradeoff between these criteria and enhancing one of them can deteriorate the other one. In this paper, we consider both network utility maximization (NUM) and energy minimization in a bi-criterion optimization problem. The problem is formulated for random access (RA) medium access control (MAC) for ad-hoc networks. First, we optimize performance of the MAC and define utility as a monotonically increasing function of link throughputs. We investigate the optimal tradeoff between energy and utility in this part. In the second part, we define utility as a function of end to end rates and optimize MAC and transport layers simultaneously. We calculate optimal persistence probabilities and end-to-end rates. Finally, by means of duality theorem, we decompose the problem into smaller subproblems, which are solved at node and network layers separately. This decomposition avoids need for a central unit while sustaining benefits of layering.

As data rates are increasing continuously in wireless networks, additional ways of improving system performance and providing higher rates is needed. One scheme that has recently attracted a lot of attention, is using relays in order to provide additional diversity, especially in scenarios that use of multiple antennas may not be practical at transmit or receive sites. In this paper, we will present a novel channel assignment scheme at the relay which will provide full diversity for the case in which two non-cooperative independent users communicate with their destination through a single relay and by using only two frequency channels. As the simulation results show, through use of this approach, significant performance improvement is achieved in comparison with the case in which each user and its corresponding relay use an independent channel for transmission. In addition, the proposed scheme is applied to two main relay-based transmission systems, namely amplify and forward (AF) and decode and forward (DF). As the results show, the performance of DF algorithms is especially superior in scenarios in which the relay is closer to the receiver.

Space-time transmit diversity results in coupling of transmitted symbols across different antennas, which increases the complexity of maximum-likelihood decoding. Symbol coupling can be completely or partially avoided if the space-time code (STC) satisfies specific decoupling conditions; examples of such codes are orthogonal space-time block codes and quasi-orthogonal codes. In this letter, we study decoupling conditions for a linear full-diversity STC. Quasi-decoupled codes are proposed as a partially decoupled full-diversity STC of any rate for any number of transmit antennas with minimum decoding delay. By optimizing the coding gain of quasi-decoupled codes, it is shown that quasi-orthogonal codes have competitive performance with respect to the Alamouti code, and the more-recent threaded algebraic space-time (TAST) codes and ABBA codes. A general full-diversity decoupling condition is considered, and the general solution to this case, which also encompasses previously known orthogonal STCs, is derived

In this paper, we discuss the optimum random and deterministic power selection algorithms in Aloha networks, where nodes run Exponential Backoff (EB) for contention resolution. In random case, the transmission power of a packet is selected from the available power levels, based on a predetermined probability mass function, while with the deterministic algorithms the transmission power of a packet is a deterministic function of the number of collisions the packet has experienced. Most of the related works in the literature have not addressed the power-throughput characteristics of the power selection algorithms for use in practical system designs and, therefore, this subject has been the major motivation of this paper. For the random case, we will derive optimum random power selection algorithms for unconstrained and constrained power budget scenarios and the corresponding optimum power-throughput characteristics will be presented for the latter case based on perfect capture model. Next, we will introduce a method to extend these results to SIR-based capture model, which will result in sub- optimum power steps and the sub-optimum power-throughput characteristics for the random case. This characteristic will reveal power budget requirements for the target throughput values. In the next step, deterministic power selection algorithms will be introduced and discussed with unconstrained and constrained power budget scenarios and sub-optimum power-throughput characteristic will be derived based on the perfect model. Similar methods, introduced for the random case, may be used to adapt the results to SIR-based model. Finally, by comparing the presented power-throughput characteristics, it will be demonstrated that optimum random and sub-optimum deterministic power selection algorithms have very similar behaviors. Therefore, noting that deterministic algorithms do not require any random power generator block, they may be preferred to random algorithms in similar scenarios.

In this paper, we will analyze a decentralized transmission power level selection algorithm in conjunction with Binary Exponential Back-off retransmission scheme, in large slotted-Aloha networks under saturation condition. In this algorithm, the transmission power is steadily increased by an amount called power step, until the packet is successfully transmitted. We will use SIR-based capture model to study the performance of this algorithm with small and large power steps. Our analysis proves that, although small power steps may seem favorable for power saving purposes, these power steps face throughput collapse under high load conditions (large number of nodes), while properly chosen large power steps guarantee higher throughputs and require approximately the same average powers.

In this paper, we first propose a space-time-frequency (STF) coding method for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission over frequency-selective fading channels and then, based on channel mean feedback model, we develop an adaptive MIMO-OFDM transmitter by applying an adaptive energy and bit loading scheme across OFDM subcarriers. Under the constraints of fixed transmit-energy and prescribed error performance, our objective is to maximize the transmission rate. Simulation results are presented which confirm the performance of our proposed system and show that under certain circumstances, the transmission rate improves considerably as compared with competing schemes.

Adaptive modulation with the goal of minimizing the average transmit power is investigated. This is the dual problem of the well-known problem of maximizing the average spectral efficiency. This is desirable in power limited systems such as mobile and sensor networks. Two main cases are considered: adapting only transmit power and adapting only rate. Appropriate expressions for rate or power control policies are derived for different cases. For rate adaptation, we consider both continuous and discrete rate policies. Then, we will apply these methods to a special case of M-QAM modulation over Rayleigh fading channel. We will show that by using rate or power adaptation schemes, between 7 dB and 18 dB power gain can be saved over nonadaptive methods for typical values of spectral efficiency

This paper introduces adaptive consensus, a spatio-temporal adaptive method to improve convergence behavior of the current consensus fusion schemes. This is achieved by introducing a time adaptive weighting method for updating each sensor data in each iteration. Adaptive consensus method will improve node convergence rate, average convergence rate and the variance of error over the network. A mathematical formulation of the method according to the adaptive filter theory as well as derivation of the time adaptive weights and convergence conditions are presented. The analytical results are verified by simulation as well

Hidden-terminal is one of the main problems in ad hoc wireless networks. In addition, there are scenarios where the desired destination is located in the range of other transmitters, so that the efforts on setting up communication with this terminal will fail due to collisions that may occur between transmitted control frames and undesired received control and data frames. This phenomenon becomes a bottleneck when most of data transmissions experience packet fragmentation. In such scenarios, the desired destination becomes unreachable during the data transfer of neighboring nodes. Using the same PHY of IEEE 802.11 and making slight modifications in its MAC, a new medium access control scheme equipped by a novel transmission scheduling algorithm is presented to address such problems. The performance of the proposed approach is compared with earlier schemes through simulation-based evaluations, showing performance enhancement due to better handling of unreachability issues in the proposed MAC scheme

Interworking in heterogeneous wireless networks is one of the key objectives in the next generation networks (NGN). Handoff between cellular and wireless local area networks (WLANs) can help achieve this aim. In this paper, a new method for decision-making on vertical handoff initiation is proposed which is on the basis of road topology information and mobility prediction. The main distinction between the proposed algorithm and previous research is exploitation of road topology map of the destination hotspot within a cellular network. Mobile terminal speed, WLAN dynamic traffic in the destination hotspot, and topology information are used to decide on suitability of vertical handoff depending on user's current situation. Simulation results verified that this method outperforms the traditional algorithm in which topology information is not used

A sensor network with mobile agents (SENMA) is an architecture proposed for large scale sensor networks in which the access point moves above the network and gathers information from a limited number of nodes. However, the performance of such system is limited by estimation errors and packet errors due to collision. In this paper, we first derive the equations of distortion in a general scenario and use this model to obtain four best node positions in order to achieve min-max estimation error in a square. In addition, an opportunistic MAC is proposed that considers both collision effects and estimation error based on channel state information (CSI). As the simulation results show the proposed scheme achieves an optimum estimation with minimum collision and energy consumption.

In this paper a modified neighbor aware medium-access control protocol is proposed designed for wireless sensor networks. Wireless sensor networks are generally comprised of battery powered nodes. These nodes are densely deployed in an ad-hoc structure. The main objective for sensor networks is low power consumption while latency is less important. This leads different MAC layer design for wireless sensor networks compared to conventional ad hoc protocols. In this paper we propose a neighbor aware medium access control to reduce power consumption. This protocol is derived from modified distributed mediation device (MDMD) protocol and uses useful properties of this protocol while improves the performance of this protocol especially in dense environments. We add a neighbor aware dynamic scheduling scheme to MDMD protocol to reduce power consumption. We compare this modified protocol with sensor-MAC (S-MAC) and MDMD protocol.

Adaptive transmission is an effective way to improve the performance of communication links in time-varying channel environments. Former works use this technique to maximize the throughput; but we exploit it to minimize BER in a system with diversity reception. We do so by adapting power in response to the channel variations. We use analytical methods to derive our results. These expressions can be used for any type of channel fading and any modulation scheme. We then apply this method to a special case of 4-QAM over Rayleigh channel with MRC reception.

In this paper, by making slight modifications in protocol stack of wireless access points, the proposed CICADA scheme aims at isolating problems related to wireless section of a TCP connection from its wired portion, using IP spoofing and Virtual Protocol Stack scheme. As the simulation results for an 802.11 based system show, the proposed scheme improves the overall network throughput while reducing the mean and variance of end-to-end delay for voice and video flows due to smarter bandwidth allocation to elastic and real-time traffic. In addition, the proposed scheme uses a centralized NAT structure that prevents undesired disconnections during handovers and achieves a smaller handoff delay compared with other schemes. CICADA also improves the privacy of wireless sections by constructing virtual protocol stacks corresponding to mobile stations and using hierarchical two-level NAT structure in infrastructure networks in order to protect the distribution system against outside attacks

Abstract In this paper, by making slight modifications in protocol stack of wireless access points, the proposed CICADA scheme aims at isolating problems related to wireless section of a transmission control protocol (TCP) connection from its wired portion, using IP spoofing and virtual protocol stack scheme. As the simulation results for an 802.11 based system show, the proposed scheme improves the overall network throughput for both DCF and PCF scenarios, while reducing the mean and variance of end‐to‐end delay for voice and video flows due to smarter bandwidth allocation to elastic and real‐time traffic. In addition, the proposed scheme uses a centralized network address translation (NAT) structure that prevents undesired disconnections during handovers and achieves a smaller handoff delay compared with other schemes. CICADA also improves the privacy of wireless sections by constructing virtual protocol stacks corresponding to mobile stations and using hierarchical two‐level NAT structure in infrastructure networks in order to protect the distribution system against outside attacks. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper, we address the issue of power distribution and decoding order in the uplink side of a multirate code division multiple access (CDMA) system based on linear successive interference cancellation (SIC). First, the closed form expressions for the required received powers at the base station, in order to achieve the users bit rate and quality of service (QoS) requirements, is derived. Then, we investigate the problem of optimal decoding order of the users under imperfect interference cancellation condition and show that optimum decoding order of users, unlike the case of perfect SIC, is a function of their requested SINR values in addition to their path gains. Finally, we derive new criteria for decoding order of users in multirate SIC-based CDMA systems.

A single-user receiver structure for space-time coded CDMA downlink is studied in a multiuser frequency selective channel. The proposed scheme is a two dimensional decision feedback equalizer (2D-DFE), whose filters are optimized based on the MMSE criterion to cancel noise, ISI, and MUI with a reasonable complexity. By modeling the spreading codes of the interfering users as random sequences, system performance has been evaluated using the Gaussian approximation. Two models for the desired user's spreading sequence have been considered and compared. Our numerical results show that in both cases the 2D-DFE exhibits significant performance improvement and capacity enhancement over the standard space-time coded RAKE structure, especially in interference-limited conditions.

In this paper, a new scheme for adaptive subcarrier, bit and power allocation for downlink transmission of multiuser orthogonal frequency division multiplexing (OFDM) systems is presented. An adaptive algorithm is introduced to maximize the total data rate in a frequency selective channel, while satisfying the requirements on total transmit power, minimum data rate of each user, and desired BER that offer various quality of service (QoS) levels for different users. The performance and complexity of the proposed scheme is also compared with similar methods and other OFDM-based multiple access methods. Simulation results show that the proposed algorithm leads to higher performance in comparison with earlier sub-optimum methods, and yet requiring an acceptable computational complexity level.

It's well known that "hidden-and exposed-terminal" is among the main problems in ad-hoc WLAN networks. In addition, there are scenarios where the desired destination is located in the range of other transmitters, so that the efforts on setting up communication with this terminal will fail due to collisions occurred between desired control packets and unwanted received data packets at destination. In such scenarios, conventional protocols can not address the problem efficiently, resulting in throughput and channel utilization degradation. In this paper, by using the same PHY of IEEE 802.11 and making slight changes in its MAC layer, a new MAC protocol is presented to address such problems. Our simulations show that the performance of the new method is better than IEEE 802.11 and DBTMA; besides, in addition to solving the above problems, it improves channel utilization and reduces the total overhead due to its prior channel reservation feature.

In this paper, we will address the issue of power distribution and decoding order in the uplink side of a multirate code division multiple access (CDMA) system based on linear successive interference cancellation (SIC). First, the closed form expressions for the required received powers at the base station, in order to achieve the users bit rate and quality of service (QoS) requirements, will be derived. Then, we will investigate the problem of optimal decoding order of the users under imperfect interference cancellation condition and will show that optimum decoding order of users, unlike the case of perfect SIC, is a function of their requested SINR values in addition to their path gains. Finally, we will derive new criteria for decoding order of users in multirate SIC-based CDMA systems.

A new approach for data detection in fast fading channels is proposed. By using a combination of Rake receiver and sequential detection for fast fading channels, it is shown that system performance can be improved without prior knowledge of the channel. As will be shown in this paper, by combining spread spectrum signaling and sequential detection it is possible to exploit the time-frequency diversity of fast fading channels in a novel way. It is shown by simulation that combination of sequential detection and Rake receiver can effectively improve system performance, compared with equal gain combining.

In this paper, two single-antenna receiver structures for multiuser space-time coded wideband CDMA downlink are studied in frequency selective fading channels. The first structure is the standard space-time coded RAKE receiver, while in the second structure, we have proposed a two dimensional decision feedback equalizer (2D-DFE), whose filters are optimized to jointly cancel noise, ISI, and MUI based on the MMSE criterion. For this receiver, by modeling the spreading sequences of the interfering users as random sequences, system bit error probability has been calculated using the Gaussian approximation. Our numerical results show that the 2D-DFE exhibits significant performance improvement over the space-time coded RAKE structure, especially in the interference-limited conditions.

In this paper, a new scheme for adaptive subcarrier bit and power allocation for downlink transmission of multiuser orthogonal frequency division multiplexing (OFDM) systems is presented. An adaptive algorithm is introduced to achieve the desired bit rate and BER for QoS (quality of service) of different users in a frequency selective fading channel, while the total transmit power is minimized. The performance and complexity of the proposed scheme is also thoroughly compared with the other existing methods. As the simulation results show, the proposed algorithm outperforms all prior sub-optimum schemes, while offering comparable computational complexity.

In this paper, we will introduce a new constraint on linear space-time block codes that will result in a novel decoupled symbol-by- symbol detection scheme. Based on the new framework, a quasidecoupled space-time code that is applicable to any number of transmit and receive antennas will be proposed. We will also solve a more general case and show that all other known solutions to this problem such as orthogonal designs and Clifford algebra representations, represent special cases of such general solution which will provide an easier framework, to deal with such codes. It is noticeable that the new framework not only provides better understanding of earlier solutions but may also help in solving new useful cases.

Orthogonal frequency-division multiplexing (OFDM) introduces large amplitude variations in time, which can result in significant signal distortion in the presence of nonlinear amplifiers. We introduce a new bound for the peak of the continuous envelope of an OFDM signal, based on the maximum of its corresponding oversampled sequence; it is shown to be very tight as the oversampling rate increases. The bound is then used to derive a closed-form probability upper bound for the complementary cumulative distribution function of the peak-to-mean envelope power ratio of uncoded OFDM signals for sufficiently large numbers of subcarriers. As another application of the bound for oversampled sequences, we propose tight relative error bounds for computation of the peak power using two main methods: the oversampled inverse fast Fourier transform and the method introduced for coded systems based on minimum distance decoding of the code.

In this paper, we introduce a new bound for the peak of the continuous envelope of an OFDM signal based on the maximum of its corresponding oversampled sequence, that is considerably tighter than the recent results presented in Sharif and Khalaj (2001). The proposed bound is then used as the basis of our study in two parts. In the first part, we find an upper bound for the regrowth of the peak of the digitally clipped oversampled sequence after discrete to continuous conversion. We also show that for oversampling rates greater than /spl pi///spl radic/2, the peak regrowth percentage depends only on oversampling rate, regardless of the number of subcarriers. In the second part, we propose new and tight relative error bounds for computation of the peak power using two main methods: the oversampled inverse fast Fourier transform (IFFT) and the method introduced for coded systems by Tarokh and Jafarkhani (2000).

Our goal is to propose an optimal handoff algorithm for hybrid networks (HNs), which are constructed by interworking between wireless LANs (WLANs) and mobile networks. The proposed algorithm is an adaptive fuzzy logic based algorithm that can adapt itself with the dynamic conditions in HNs. It uses mobile terminal speed estimation and traffic in the WLAN as additional input parameters. This algorithm is designed to the meet special requirements of HNs. Simulation results show that it has much better performance than conventional algorithms.

Orthogonal frequency division multiplexing (OFDM) has a large peak to mean envelope power ratio (PMEPR), which can result in significant signal distortion in the presence of nonlinear amplifiers. We show that the peak of the continuous envelope of the OFDM signal can be bounded by the maximum of the corresponding oversampled sequence. This fact is then used as the cornerstone of our two-part study. In the first part, statistical analysis is used to find the probability upper bound for the complementary cumulative distribution function of PMEPR. It is also proven that for a large number of subcarriers, only one redundant bit is necessary to limit the PMEPR within 10 % of its maximum value. In the second part, PMEPR estimation using oversampling and the fast Fourier transform is revisited. Here, we introduce a novel measure on the estimation error using an oversampled OFDM signal.

A technique is proposed for estimating the parameters of two-dimensional (2-D) uniform motion of multiple moving objects in a scene, based on long-sequence image processing and the application of a multiline fitting algorithm. Plots of the vertical and horizontal projections versus frame number give new images in which uniformly moving objects are represented by skewed band regions, with the angles of the skew from the vertical being a measure of the velocities of the moving objects. For example, vertical bands will correspond to objects with zero velocity. An algorithm called subspace-based line detection (SLIDE) can be used to efficiently determine the skew angles. SLIDE exploits the temporal coherence between the contributions of each of the moving patterns in the frame projections to enhance and distinguish a signal subspace that is defined by the desired motion parameters. A similar procedure can be used to determine the vertical velocities. Some further steps must then be taken to properly associate the horizontal and vertical velocities.

Mobile radio channels can be generally characterized as a fading multipath channel with multipath spread delay that ranges from a few microseconds (/spl mu/s) up to as much as 15 /spl mu/s. Such large delays result in intersymbol interference which raises the need for equalization. A semi-blind maximum likelihood sequence estimation (MLSE) with an antenna array is studied as an adaptive equalization method for time division multiple access (TDMA) digital cellular systems. The MLSE is implemented using a Viterbi algorithm (VA). An antenna array is used to utilize both temporal and spatial structure in the received signal to provide the VA with the estimate of the channel impulse response necessary for it to perform MLSE.>

A self-reference technique is developed for detecting the location of defects in repeated pattern wafers and masks. The application area of the proposed method includes inspection of memory chips, shift registers, switch capacitors, and CCD arrays. Using high resolution spectral estimation algorithms, the proposed technique first extracts the period and structure of repeated patterns from the image to sub-pixel resolution, and then produces a defect-free reference image for making comparison with the actual image. Since the technique acquires all its needed information from a single image, there is no need for a database image, a scaling procedure, or any a-priori knowledge about the repetition period of the patterns.>