In this paper, we present a novel neural architecture for 5G channel estimation (CE), the Recurrent Equivariant UERS Estimation Network (ReQuestNet). It incorporates several practical considerations in wireless communication systems, such as ability to handle variable number of resource block (RB), dynamic number of transmit layers, physical resource block groups (PRGs) bundling size (BS), demodulation reference signal (DMRS) patterns with a single unified model, thereby, drastically simplifying the CE pipeline. Besides it addresses several limitations of the legacy linear minimum mean squared error (MMSE) solutions, for example, by being independent of other reference signals and particularly by jointly processing multiple input multiple output (MIMO) layers and differently precoded channels, unknown at the receiver. ReQuestNet comprises of two sub-units, CoarseNet followed by RefinementNet. CoarseNet performs per PRG, per transmit-receive (Tx-Rx) stream channel estimation, while RefinementNet refines the CoarseNet channel estimate by incorporating correlations across differently precoded PRGs, and correlation across MIMO channel spatial dimensions (cross-MIMO). The simulation results show that ReQuestNet outperforms genie MMSE across various channel profiles as well as unseen channel profiles during training, achieving up to 10dB gain at high signal-to-noise ration (SNR).

Modelling the propagation of electromagnetic wireless signals is critical for designing modern communication systems. Wireless ray tracing simulators model signal propagation based on the 3D geometry and other scene parameters, but their accuracy is fundamentally limited by underlying modelling assumptions and correctness of parameters. In this work, we introduce Wi-GATr, a fully-learnable neural simulation surrogate designed to predict the channel observations based on scene primitives (e. g., surface mesh, antenna position and orientation). Recognizing the inherently geometric nature of these primitives, Wi-GATr leverages an equivariant Geometric Algebra Transformer that operates on a tokenizer specifically tailored for wireless simulation. We evaluate our approach on a range of tasks (i. e., signal strength and delay spread prediction, receiver localization, and geometry reconstruction) and find that Wi-GATr is accurate, fast, sample-efficient, and robust to symmetry-induced transformations. Remarkably, we find our results also translate well to the real world: Wi-GATr demonstrates more than 35% lower error than hybrid techniques, and 70% lower error than a calibrated wireless tracer.

We consider multi-draft speculative sampling, where the proposal sequences are sampled independently from different draft models. At each step, a token-level draft selection scheme takes a list of valid tokens as input and produces an output token whose distribution matches that of the target model. Previous works have demonstrated that the optimal scheme (which maximizes the probability of accepting one of the input tokens) can be cast as a solution to a linear program. In this work we show that the optimal scheme can be decomposed into a two-step solution: in the first step an importance sampling (IS) type scheme is used to select one intermediate token; in the second step (single-draft) speculative sampling is applied to generate the output token. For the case of two identical draft models we further 1) establish a necessary and sufficient condition on the distributions of the target and draft models for the acceptance probability to equal one and 2) provide an explicit expression for the optimal acceptance probability. Our theoretical analysis also motives a new class of token-level selection schemes based on weighted importance sampling. Our experimental results demonstrate consistent improvements in the achievable block efficiency and token rates over baseline schemes in a number of scenarios.

In the context of communication networks, digital twin technology provides a means to replicate the radio frequency (RF) propagation environment as well as the system behaviour, allowing for a way to optimize the performance of a deployed system based on simulations. One of the key challenges in the application of Digital Twin technology to mmWave systems is the prevalent channel simulators' stringent requirements on the accuracy of the 3D Digital Twin, reducing the feasibility of the technology in real applications. We propose a practical Digital Twin creation pipeline and a channel simulator, that relies only on a single mounted camera and position information. We demonstrate the performance benefits compared to methods that do not explicitly model the 3D environment, on downstream subtasks in beam acquisition, using the real-world dataset of the DeepSense6G challenge.

The Poisson Matching Lemma (PML) introduced by Li & Anantharam (IT-Trans 2021) is a powerful technique for one-shot analysis of a variety of multi-terminal source and channel coding problems. In this work we make use of PML to derive one-shot achievability results for unequal message protection with a fixed number of message classes. Our analysis involves revisiting the proof of the PML to account for the error associated with each codebook at the decoder. Our approach leads to compact bounds on the error probability for each message class for arbitrary input distributions and channels. For the example of binary erasure channel, we compare our bounds numerically with prior work [1] and demonstrate improvements in the achievable rate.

Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified probability. Intuitively, the size of the prediction set encodes a general notion of uncertainty, with larger sets associated with higher degrees of uncertainty. In this work, we leverage information theory to connect conformal prediction to other notions of uncertainty. More precisely, we prove three different ways to upper bound the intrinsic uncertainty, as described by the conditional entropy of the target variable given the inputs, by combining CP with information theoretical inequalities. Moreover, we demonstrate two direct and useful applications of such connection between conformal prediction and information theory: (i) more principled and effective conformal training objectives that generalize previous approaches and enable end-to-end training of machine learning models from scratch, and (ii) a natural mechanism to incorporate side information into conformal prediction. We empirically validate both applications in centralized and federated learning settings, showing our theoretical results translate to lower inefficiency (average prediction set size) for popular CP methods.

In terahertz (THz) massive multiple-input multiple-output (MIMO) systems, the combination of huge bandwidth and massive antennas results in severe beam split, thus making the conventional phase-shifter based hybrid precoding architecture ineffective. With the incorporation of true-time-delay (TTD) lines in the hardware implementation of the analog precoders, delay-phase precoding (DPP) emerges as a promising architecture to effectively overcome beam split. However, existing DPP approaches suffer from poor performance, high complexity, and weak robustness in practical THz channels. In this paper, we propose a novel DPP approach in wideband THz massive MIMO systems. First, the matrix decomposition optimization problem is converted into a compressive sensing (CS) form, which can be solved by the proposed extended spatially sparse precoding (SSP) algorithm. To compensate for beam split, frequency-dependent measurement matrices are designed, which can be approximately realized by feasible phase and delay codebooks. Furthermore, several efficient atom selection techniques are developed to further reduce the complexity of the extended SSP algorithm. In simulation, the proposed DPP approach achieves superior performance, complexity, and robustness by using it alone or in combination with existing DPP approaches under various settings.

Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wireless channel properties based on map data. In this work, we present a transformer-based neural network architecture that enables predicting link-level properties from maps of various dimensions and from sparse measurements. The map contains information about buildings and foliage. The transformer model attends to the regions that are relevant for path loss prediction and, therefore, scales efficiently to maps of different size. Further, our approach works with continuous transmitter and receiver coordinates without relying on discretization. In experiments, we show that the proposed model is able to efficiently learn dominant path losses from sparse training data and generalizes well when tested on novel maps.

Neural network pruning and quantization techniques are almost as old as neural networks themselves. However, to date, only ad-hoc comparisons between the two have been published. In this paper, we set out to answer the question of which is better: neural network quantization or pruning? By answering this question, we hope to inform design decisions made on neural network hardware going forward. We provide an extensive comparison between the two techniques for compressing deep neural networks. First, we give an analytical comparison of expected quantization and pruning error for general data distributions. Then, we provide lower and upper bounds for the per-layer pruning and quantization error in trained networks and compare these to empirical error after optimization. Finally, we provide an extensive experimental comparison for training 8 large-scale models trained on 3 tasks and provide insights into the representations learned during fine-tuning with quantization and pruning in the loop. Our results show that in most cases quantization outperforms pruning. Only in some scenarios with a very high compression ratio, compression might be beneficial from an accuracy standpoint.

Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and energy consumption. However, channel estimation in the context of HAD is challenging due to only limited radio frequency (RF) chains at transceivers. Although various compressive sensing (CS) algorithms have been developed to solve this problem by exploiting inherent channel sparsity and sparsity structures, practical effects, such as power leakage and beam squint, can still make the real channel features deviate from the assumed models and result in performance degradation. Besides, the high complexity of CS algorithms caused by a large number of iterations hinders their applications in practice. To tackle these issues, we develop a deep learning (DL)-based channel estimation approach where the sparse Bayesian learning (SBL) algorithm is unfolded into a deep neural network (DNN). In each SBL layer, Gaussian variance parameters of the sparse angular domain channel are updated by a tailored DNN, which is able to capture complicated channel sparsity structures in various domains effectively and efficiently. The measurement matrix is jointly optimized for performance improvement. Then, the proposed approach is extended to the multi-block case where channel correlation in time is further exploited to adaptively predict the measurement matrix and facilitate the update of variance parameters. Simulation results show that the proposed approaches outperform existing approaches in terms of both performance and complexity.

Analog beamforming is the predominant approach for millimeter wave (mmWave) communication given its favor-able characteristics for limited-resource devices. In this work, we aim at reducing the spectral efficiency gap between analog and digital beamforming methods. We propose a method for refined beam selection based on the estimated raw channel. The channel estimation, an underdetermined problem, is solved using compressed sensing (CS) methods leveraging angular domain sparsity of the channel. To reduce the complexity of CS methods, we propose dictionary learning iterative soft-thresholding algorithm, which jointly learns the sparsifying dictionary and signal reconstruction. We evaluate the proposed method on a realistic mm Wave setup and show considerable performance improvement with respect to code-book based analog beamforming approaches.

Tree-based demappers for multiple-input multiple-output (MIMO) detection such as the sphere decoder can achieve near-optimal performance but incur high computational cost due to their sequential nature. In this paper, we propose the perturbed linear demapper (PLM), which is a novel data-driven model for computing soft outputs in parallel. To achieve this, the PLM learns a distribution centered on an initial linear estimate and a log-likelihood ratio clipping parameter using end-to-end Bayesian optimization. Furthermore, we show that lattice-reduction can be naturally incorporated into the PLM pipeline, which allows to trade off computational cost against coded block error rate reduction. We find that the optimized PLM can achieve near maximum-likelihood (ML) performance in Rayleigh channels, making it an efficient alternative to tree-based demappers.

We present a neural network architecture for jointly learning user locations and environment mapping up to isometry, in an unsupervised way, from channel state information (CSI) values with no location information. The model is based on an encoder-decoder architecture. The encoder network maps CSI values to the user location. The decoder network models the physics of propagation by parametrizing the environment using virtual anchors. It aims at reconstructing, from the encoder output and virtual anchor location, the set of time of flights (ToFs) that are extracted from CSI using super-resolution methods. The neural network task is set prediction and is accordingly trained end-to-end. The proposed model learns an interpretable latent, i.e., user location, by just enforcing a physics-based decoder. It is shown that the proposed model achieves sub-meter accuracy on synthetic ray tracing based datasets with single anchor SISO setup while recovering the environment map up to 4cm median error in a 2D environment and 15cm in a 3D environment.

We propose generative channel modeling to learn statistical channel models from channel input-output measurements. Generative channel models can learn more complicated distributions and represent the field data more faithfully. They are tractable and easy to sample from, which can potentially speed up the simulation rounds. To achieve this, we leverage advances in generative adversarial network (GAN), which helps us learn an implicit distribution over stochastic MIMO channels from observed measurements. In particular, our approach MIMO-GAN implicitly models the wireless channel as a distribution of time-domain band-limited impulse responses. We evaluate MIMO-GAN on 3GPP TDL MIMO channels and observe high-consistency in capturing power, delay and spatial correlation statistics of the underlying channel. In particular, we observe MIMO-GAN achieve errors of under 3.57 ns average delay and -18.7 dB power.

In compressed sensing, the goal is to reconstruct the signal from an underdetermined system of linear measurements. Thus, prior knowledge about the signal of interest and its structure is required. Additionally, in many scenarios, the signal has an unknown orientation prior to measurements. To address such recovery problems, we propose using equivariant generative models as a prior, which encapsulate orientation information in their latent space. Thereby, we show that signals with unknown orientations can be recovered with iterative gradient descent on the latent space of these models and provide additional theoretical recovery guarantees. We construct an equivariant variational autoencoder and use the decoder as generative prior for compressed sensing. We discuss additional potential gains of the proposed approach in terms of convergence and latency.

Equivariant networks capture the inductive bias about the symmetry of the learning task by building those symmetries into the model. In this paper, we study how equivariance relates to generalization error utilizing PAC Bayesian analysis for equivariant networks, where the transformation laws of feature spaces are deter- mined by group representations. By using perturbation analysis of equivariant networks in Fourier domain for each layer, we derive norm-based PAC-Bayesian generalization bounds. The bound characterizes the impact of group size, and multiplicity and degree of irreducible representations on the generalization error and thereby provide a guideline for selecting them. In general, the bound indicates that using larger group size in the model improves the generalization error substantiated by extensive numerical experiments.

Cryo-Electron Microscopy (Cryo-EM) is an important imaging method which allows high-resolution reconstruction of the 3D structures of biomolecules. It produces highly noisy 2D images by projecting a molecule's 3D density from random viewing directions. Because the projection directions are unknown, estimating the images' poses is necessary to perform the reconstruction. We focus on this task and study it under the group synchronization framework: if the relative poses of pairs of images can be approximated from the data, an estimation of the images' poses is given by the assignment which is most consistent with the relative ones.In particular, by studying the symmetries of cryo-EM, we show that relative poses in the group O(2) provide sufficient constraints to identify the images' poses, up to the molecule's chirality. With this in mind, we improve the existing multi-frequency vector diffusion maps (MFVDM) method: by using O(2) relative poses, our method not only predicts the similarity between the images' viewing directions but also recovers their poses. Hence, we can leverage all input images in a 3D reconstruction algorithm by initializing the poses with our estimation rather than just clustering and averaging the input images. We validate the recovery capabilities and robustness of our method on randomly generated synchronization graphs and a synthetic cryo-EM dataset.

We propose Hypernetwork Kalman Filter (HKF) for tracking applications with multiple different dynamics. The HKF combines generalization power of Kalman filters with expressive power of neural networks. Instead of keeping a bank of Kalman filters and choosing one based on approximating the actual dynamics, HKF adapts itself to each dynamics based on the observed sequence. Through extensive experiments on CDL-B channel model, we show that the HKF can be used for tracking the channel over a wide range of Doppler values, matching Kalman filter performance with genie Doppler information. At high Doppler values, it achieves around 2dB gain over genie Kalman filter. The HKF generalizes well to unseen Doppler, SNR values and pilot patterns unlike LSTM, which suffers from severe performance degradation.

Neural networks have been shown to be vulnerable against minor adversarial perturbations of their inputs, especially for high dimensional data under $\ell_\infty$ attacks.To combat this problem, techniques like adversarial training have been employed to obtain models that are robust on the training set.However, the robustness of such models against adversarial perturbations may not generalize to unseen data.To study how robustness generalizes, recent works assume that the inputs have bounded $\ell_2$-norm in order to bound the adversarial risk for $\ell_\infty$ attacks with no explicit dimension dependence.In this work, we focus on $\ell_\infty$ attacks with $\ell_\infty$ bounded inputs and prove margin-based bounds.Specifically, we use a compression-based approach that relies on efficiently compressing the set of tunable parameters without distorting the adversarial risk. To achieve this, we apply the concept of effective sparsity and effective joint sparsity on the weight matrices of neural networks.This leads to bounds with no explicit dependence on the input dimension, neither on the number of classes.Our results show that neural networks with approximately sparse weight matrices not only enjoy enhanced robustness but also better generalization. Finally, empirical simulations show that the notion of effective joint sparsity plays a significant role in generalizing robustness to $\ell_\infty$ attacks.

We consider the reconstruction of group-sparse vectors from sign measurements of random projections. In particular, we establish conditions on the number of measurements under which such signal ensembles can be recovered up to a prescribed accuracy. The results rely on a mixed restricted isometry property as first employed by Foucart in the context of 1-bit compressed sensing, as well as certain results on random hyperplane tessellations. The paper fills a gap in the literature by establishing that group-sparse signals can be estimated from 1-bit observations with the same number of measurements as required for the reconstruction of block-sparse signals from unquantized measurements. We confirm the correct behavior of the recovery schemes in a series of numerical experiments.

We address the problem of reconstructing group-sparse vectors from compressive measurements acquired via subgaussian block diagonal measurement operators. Such results can be obtained by establishing the so-called group restricted isometry property of the underlying measurement matrix. In particular, the problem is reduced to the task of bounding certain geometric objects associated with the suprema of a particular chaos process, which involves estimating Talagrand's γ2-functional via Dudley's metric entropy integral. As part of the proof, we generalize Maurey's empirical method to provide new bounds on the covering number of sets consisting of finite convex combinations of compact sets.

Low-power wide-area network (LPWAN) multi-radio access technology (RAT) devices promise enabling Internet of Things (IoT) use-cases that simultaneously require high coverage and data rates, and low energy consumption. For such devices, active probing is usually used for discovering if communication between a mobile terminal (MT) and a base station (BS) or a handover of the MT across LPWAN technologies should be initiated. Because of continuous probing, this procedure increases signaling overhead and energy consumption of the MT. Assuming that the location information of the MT is required for enabling an IoT use-case, this information can potentially also be used for enhancing the discovery and vertical handover procedures in heterogeneous LPWANs. Hence, we propose a location-based mechanism for making discovery and handover decisions in outdoor LPWANs. We do that under the assumption that the location of the MT can be estimated with a certain level of localization errors, while the perfectly accurate location information of the BSs are known to the MT. The mechanism grounds the decisions on the expected SNR between the MT and the BS, which removes the need for continuous probing. If the location information of the MT can be estimated with GPS-like accuracy, we demonstrate that the mechanism can achieve more than 90% correct discovery decisions. We also show that the mechanism is highly accurate in determining if a handover between technologies should be initiated. For an order of magnitude less accurate location information (e.g., for SigFox-based fingerprinting), we show that the mechanism can still make reasonable discovery decisions.

The mutual coherence provides a basis for deriving recovery guarantees in compressed sensing. In this paper, the mutual coherence of spherical harmonics sensing matrices is examined for a class of sensing patterns common in practice and is used as a figure of merit for designing sensing matrices. We will show that for each sampling pattern, the coherence is lower bounded by the inner product of two Legendre polynomials with different degrees. In some practical situation, it is desirable to have sampling points on a sphere follow a regular pattern, hence, facilitating the measurement process. It will be shown that for a class of sampling patterns, the mutual coherence would be at its maximum, yielding the worst performance. Finally, the sampling strategy is proposed to achieve the derived lower bound.

With the abundance of mobile connected devices and coexisting networks, localization solutions are inevitably subject to interference. In this paper, effects of interference on the performance of Fingerprinting Localization Algorithms (FPS) are studied both theoretically and through experimentation. The previously introduced theoretical framework based on Hypothesis Testing (HT) problem is employed to characterize the performance of FPS and provide guidelines for combating negative impact of interference. In particular, it is shown that background interference interestingly can improve the performance of FPS, while the interference in the measurement phase incurs an error by pushing the reported location closer to the anchors. Moreover, the anchors provide the highest interference robustness for locations in their proximity. These results are further verified through simulations and experimentation in realistic setups.

The censored channel is one of the fundamental channels in information theory, which belongs to the class of non-linear channels. It is modeled by cascading an additive noise channel with a clipping operator. This paper is concerned with the information theoretic capacity of this channel. A necessary and sufficient condition for optimality of the input distribution is derived and it is shown that the capacity-achieving input distribution for the amplitude-limited censored channel has only a finite number of mass points. This result holds for a large class of noise distributions including additive Gaussian noise.

Despite the popularity of Fingerprinting Localization Algorithms (FPS), general theoretical frameworks for their performance studies have rarely been discussed in the literature. In this work, after setting up an abstract model for the FPS, we show that a fingerprinting-based localization problem can be cast as a Hypothesis Testing (HT) problem and therefore various results from the HT literature can be used to provide insights, guidelines, and performance bounds for the FPS. This includes the scaling limits of error probability in terms of the number of measurements and the precise characterization of localization error. The provided results hold for the general FPS. Additionally, Received Signal Strength (RSS)-based fingerprinting algorithms are particularly considered from the theoretical viewpoint due to their widespread practical usage. Simulations and experimental results characterize numerically the findings of the theoretical framework and demonstrate its consistency with realistic localization scenarios.

Device-to-Device (D2D) communication has a high potential in reducing the amount of network traffic and improving the latency and energy efficiency of communication. Currently, D2D link establishment decisions are based on active probing between devices that wish to establish a D2D link. The main drawback of such approaches is a large overhead as during active probing no data communication can take place. We leverage physical locations of the devices that wish to establish a D2D link in order to estimate the probability of success in establishing the link before making an attempt to communicate. The probability of success is given as a closed form equation that takes into account the imperfections of location information of the devices and intrinsic randomness of wireless environments. We experimentally evaluate the proposed location-based decision- making mechanism for D2D link establishment in a complex office-like indoor environment. We show that setting the Signal-to-Noise Ratio (SNR) threshold of the proposed mechanism to a value that is 5 dB higher than the nominal SNR required for communication results in reliable link establishment with false positive rate of less than 2%. Furthermore, we show a relatively small loss of link establishment potential due to an increase in the inaccuracies of location information.

The increasing number of mobile devices with high processing power and high-resolution screens had led to an enormous growth of mobile video traffic. Mobile network operators face the requirement to efficiently support large numbers of concurrent unicast streaming sessions. In the present work, the long-term quality of experience perceived by the user, the fairness, and the overall system efficiency are addressed simultaneously from the cross-layer perspective by jointly optimizing the video adaptation and the wireless resource allocation. One fundamental challenge of the cross layer design is that the time scale of video adaptation - seconds - differs by several orders of magnitude from the one of resource allocation - milliseconds. We focus on the low-delay live streaming, which is particularly sensitive to the throughput fluctuation. We consider the streaming both in the downlink and in the uplink, explicitly taking into account the imperfect synchronization in the uplink. Our proposed solution consists of two components. First, we formulate the problem of video adaptation as a quality of experience based max-min optimization problem that leverages the link rate estimate in the lower network layers. Second, we propose a dynamic resource allocation scheme that takes into account the demands of the streaming clients. These two components together aim at a fair and efficient cross-layer streaming system. An accurate estimation of link rate on the time scale of seconds, required for this problem, is particularly difficult in mobile networks. As a separate contribution, several link rate estimation approaches are evaluated. The prediction algorithms assuming the static resource allocation, although computationally less complex, may lead to inaccurate prediction results in comparison to the one when dynamic resource allocation scheme is used. In this work, the spectral efficiency gain by dynamic resource allocation can be approximated and used to improve throughput predictions. We evaluate the proposed approach against state-of-the-art baselines. The results reveal significant improvements of quality of experience in all studied use cases.

Self interference (SI) in full duplex (FD) systems is the interference caused by the transmission stream on the reception stream. Being one of the main restrictive factors for performance of practical full duplex systems, however, not too much is known about its effect on the fundamental limits of relaying systems. In this work, we consider the full duplex three-node relay channel with SI where SI is modeled as an additive Gaussian noise whose variance is dependent on instantaneous input power. The classical achievable rates and upper bounds for the single three-node relay channel no longer apply due to the structure of SI. Achievable rates for Decode-and-Forward (DF) and Compress-and-Forward (CF) and upper bounds on the capacity are derived assuming Gaussian inputs and SI. The deterministic model is also introduced and its capacity is characterized. The optimal joint source-relay distributions is discussed. Numerical results are provided comparing the achievable rates and upper bound.

Range-based IEEE 802.15.4 localization systems currently require relatively high anchor density for indoor deployments. It can therefore be beneficial to use external sources of transmission as additional anchors. We present methods for using WiFi beacons to improve localization accuracy of a range-based IEEE 802.15.4 localization system in cases where only two IEEE 802.15.4 anchor nodes are available. We do this by identifying WiFi beacons from RSSI traces that we dynamically sample online, and applying fingerprinting and range-based methods using the RSSI values of the identified beacons. However, because the data of the WiFi traffic is not decodable by the IEEE 802.15.4 devices, these RSSI measurements lack identifiers that can associate them to specific WiFi Access Points (APs). Therefore, novel methods are required for both fingerprinting and range-based approaches to allow for these additional WiFi APs to be used as anchors. We show by using real-world measurements that our beacon identification method gives a false-positive rate of only 3%, and that if the range measurements to the IEEE 802.15.4 anchors are relatively accurate, with a standard deviation of 1 and 3 m, a localization accuracy improvement of 47% and 24% can be gained, respectively.

Consider a slowly fading Gaussian relay channel where the source is not aware of channel state information (CSI) and the relay is only partially aware of CSI. As a single cooperation strategy, Decode-and-Forward (DF) or Compress-and-Forward (CF), is not the best for all channel states, the relay should be able to switch between them according to its CSI. However as the source cannot be aware of chosen cooperative strategy, it should use so called oblivious codes that perform equally well under different cooperative strategies. In this paper, we prove that doubly nested lattice codes is an oblivious code which can be used to achieve DF, CF or point-to-point rate in the relay channels. Using the oblivious lattice coding, the relay, according to its CSI, decides to use DF, CF or no cooperation at all. We show that inner bound on outage probability is significantly improved if the oblivious lattice code is used with selective coding strategy at the relay.

In this paper, we consider the problem of improving Quality of Experience (QoE) of multiple video streams in OFDMA uplink. The proposed system leverages both video adaptation and adaptation of resource allocation (RA). Three different time scales arises in this scenario, namely (1) long time scale of QoE, evaluated over the sequence of Groups of Pictures (GOPs), (2) medium time scale of video adaptation for each GOP, and (3) short time scale of RA inside each GOP. We deal with the time scale differences through a two step process. We first formulate, for each GOP, an optimization problem of video quality which takes into account the long time scale QoE. Then we convert this problem to a sequence of quality-driven RA optimization. At each RA step inside a GOP, resources are allocated to users according to their utility determined by QoE constraints and quality fairness. The performance of proposed quality-driven RA is evaluated under realistic uplink scenario. It is shown that the proposed system improves significantly users' QoE compared to other solutions.

We study the effect of interference on localization algorithms through the study of the interference effect on signal features that are used for localization. Particularly, the effect of interference on packet-based Received Signal Strength Indicator (RSSI), reported by IEEE 802.11 and IEEE 802.15.4 technologies, and on Time of Flight (ToF), reported by IEEE 802.15.4 technology, is studied using both theoretical discussions and experimental verifications. As for the RSSI values, using an information theoretic formulation, we distinguish three operational regimes and we show that the RSSI values, in dBm, remain unchanged in the noise-limited regime, increase almost linearly with interference power in dBm in the interference- limited regime and cannot be obtained due to packet loss in the collision regime. The maximum observable RSSI variation is dependent on the transmission rate and Signal to Noise Ratio (SNR). We also show that ToF is, interestingly, decreased under interference which is caused in the symbol synchronization procedure at the receiver. After providing the experimental results, we discuss how the localization algorithms are affected by interference.

The problem of communicating a single message to a destination in presence of multiple relay nodes, referred to as cooperative unicast network, is considered. First, we introduce mixed noisy network coding (MNNC) scheme which generalizes noisy network coding where relays are allowed to decode-and-forward (DF) messages while all of them (without exception) transmit noisy descriptions of their observations. These descriptions are exploited at the destination and DF relays aim to decode the transmitted messages while creating full cooperation among the nodes. Moreover, the destination and DF relays can independently select the set of descriptions to be decoded or treated as interference. This concept is further extended to multihopping scenarios, referred to as layered MNNC, where DF relays are organized into disjoint groups representing one hop in the network. For cooperative unicast additive white Gaussian noise (AWGN) networks, we show that-provided DF relays are properly chosen-MNNC improves over all previously established constant gaps to the cut-set bound. Second, we consider the composite cooperative unicast network where the channel parameters are randomly drawn before communication starts and remain fixed during the transmission. Each draw is assumed to be unknown at the source and fully known at the destination but only partly known at the relays. We introduce through MNNC scheme the concept of selective coding strategy (SCS) that enables relays to decide dynamically whether, in addition to communicate noisy descriptions, is possible to decode and forward messages. It is demonstrated through slow-fading AWGN relay networks that SCS clearly outperforms conventional coding schemes.

Many indoor positioning applications focus on determining the location of a device in a particular area, like a room of a building, rather than its metric coordinates. It is common that most buildings are partitioned in more or less a regular way and discovering the relative positions of the nodes can give us an accurate estimate of the space, e.g. a room, they are located in. In this study, we have developed a probabilistic methodology to detect the sequence of wireless sensor nodes placed in an indoor environment. We assume no preliminary training, preconfiguration or infrastructure other than the identity of one reference node at the beginning of the sequence. We trigger the nodes under consideration to transmit a series of packets, and use received signal strength information as observed by the neighbors to infer about the sequence of the nodes. In this paper, a centralized processing of the information is used. The proposed methodology is verified both by simulation and by experiments in a building with regular rooms, high shadowing and multipath distortions. Rarely observed classification errors in the experiments have motivated some extension to the basic methodology.

Despite their popularity, the current praxis of comparative experimental evaluation of fingerprinting-based localization algorithms is lacking rigor, with studies typically following an ad-hoc evaluation process and focusing on black-box comparison of complete algorithms. In this paper we present a systematic benchmarking methodology that is focused on gaining finegrained insight about the relative contributions of the individual phases of the fingerprinting-based localization algorithms to their overall performance. To this end, we decompose the localization algorithms in common phases (collection of raw measurements, creation of fingerprints, pattern matching and post-processing) and systematically asses the performance of different procedures that can be applied in each of these phases. We illustrate the application of the proposed methodology using a comprehensive experimental case-study of 3 WiFi fingerprinting algorithms with 4 raw RSSI collection procedures, 3 fingerprint creation and pattern matching procedures, 4 different post-processing procedures in 3 testbeds and 4 evaluation scenarios, resulting in 36 individual experiments. The results demonstrate that in the evaluated scenarios, a lower number of WiFi APs and rather simple fingerprint creation and pattern matching can achieve better performance in terms of location accuracy than more sophisticated alternatives. The results also show that postprocessing steps like k-Nearest Neighbours (kNN) procedure are indeed effective in reducing the localization error variability and extremes, thus increasing the stability of the location estimation.

In OFDMA uplink, Multiple Access Interference (MAI) caused by synchronization offsets in user signals can considerably degrade user throughputs. In this paper, we investigate the problem of applying Dynamic Resource Allocation (DRA) to mitigate MAI and simultaneously improve user throughputs. Firstly, we introduce a novel solvable optimization problem (OP) following DRA to maximize the minimum user throughput. Using this proposed OP, we show the optimal resource allocation schemes can increase minimum user throughput up to 300% compared to existing DRA heuristics, and to 25% compared to the estimation and correction based approach. Secondly, by investigating the frequency assignment, we show an insight about the advantage of DRA in uplink, where better exploitation of frequency and multiuser diversities leads to efficient MAI mitigation and significant throughput gain. Finally, we introduce suboptimal OPs to reduce the complexity, among that the one based on chunking can significantly reduce the complexity and still provide comparable throughput gain.

Noisy Network Coding (NNC) was recently introduced, generalizing Compress-and-Forward (CF) to multiterminal networks. In this paper, we present Mixed Noisy Network Coding scheme as the generalization of NNC where part of the nodes are allowed to select Decode-and-Forward (DF) as their cooperative strategy while all nodes without exception transmit the compressed version of their observations. The compressed version of relays is exploited at each destination to decode the intended message. It is shown that Mixed NNC scheme performs potentially better than NNC. In particular, for AWGN networks it achieves a tighter ”constant gap” with respect to the cut-set bound, provided that DF relays are chosen properly.

Consider the simultaneous relay channel (SRC) that consists of a set of relay channels where the source wishes to transmit common and private information to each of the destinations. This problem is recognized as being equivalent to that of sending common and private information to several destinations in presence of helper relays where each channel outcome becomes a branch of the broadcast relay channel (BRC). Cooperative schemes and capacity region for a set with two memoryless relay channels are investigated. The proposed coding schemes, based on decode-and-forward (DF) and compress-and-forward (CF), must be capable of transmitting information simultaneously to all destinations in such a set. Depending on the quality of source-to-relay and relay-to-destination channels, inner bounds on the capacity of the general BRC are derived. Three cases of particular interest are considered: 1) cooperation is based on DF strategy for both users, referred to as DF–DF region; 2) cooperation is based on CF strategy for both users, referred to as CF–CF region; and 3) cooperation is based on DF strategy for one destination and CF for the other, referred to as DF–CF region. These results can be seen as a generalization and hence unification of previous works. An outer bound on the capacity of the general BRC is also derived. Capacity results are obtained for the specific cases of semidegraded and degraded Gaussian SRCs. Rates are evaluated for Gaussian models where the source must guarantee a minimum amount of information to both users while additional information is sent to each of them.

Consider a composite unicast relay network where the channel statistic is randomly drawn from a set of conditional distributions indexed by θ ϵ Θ, which is assumed to be unknown at the source, fully known at the destination and only partly known at the relays. Commonly, the coding strategy at each relay is fixed regardless of its channel measurement. A novel coding for unicast composite networks with multiple relays is introduced. This enables the relays to select dynamically-based on its channel measurement - the best coding scheme between compress-and-forward (CF) and decode-and-forward (DF). As a part of the main result, a generalization of Noisy Network Coding is shown for the case of unicast general networks where the relays are divided between those using DF and CF coding. Furthermore, the relays using DF scheme can exploit the help of those based on CF scheme via offset coding. It is demonstrated via numerical results that this novel coding, referred to as Selective Coding Strategy (SCS), outperforms conventional coding schemes.

This paper investigates composite multiterminal networks which consist of a set of multiterminal channels indexed or parametrized by a vector of channel parameters θ. The channel in operation is drawn from the sample set with probability Pθ. Instead of finding the maximum achievable rate subject to a -asymptotically- small error probability (EP), we look at the behavior of the error probability for a fixed coding rate. The asymptotic spectrum of error probability (ASEP) is then introduced as a novel and more general performance measure for composite networks. Indeed, the ASEP is defined as the smallest probability that the EP exceeds a desirable error ε for a coding rate r. It is shown that the ASEP is directly related to the ε-capacity of the network and assuming memoryless channels the ASEP can be bounded by a new region referred to as the full error region. Moreover, every code with a rate belonging to this region yields asymptotic EP equal to one.

Consider the composite relay channel consisting of a set of relay channels associated to a probability measure. The current channel is a draw from its probability and in some cases arbitrary small error probability cannot be guaranteed for all channels in the set. In this paper, instead of finding the maximum achievable rate subject to a small error probability (EP) for all the channels in the set, we look at the asymptotic behavior of EP for a given rate. The notion of achievable EP is introduced as a novel performance measure for wireless relay channels. We can intuitively define it as the smallest EP that can be asymptotically achieved for a given rate. The behavior of EP is directly related to the ϵ-capacity of each channel in the set. It is shown that the behavior of EP is upper and lower bounded by the outage probability of a region which is referred to as the full error region. Then every code with a rate belonging to this region yields EP equal to one. Finally, new coding for oblivious cooperation simultaneously exploiting both Decode-and-Forward (DF) and Compress-and-Forward (CF) strategies is investigated. The Gaussian relay channel with slow fading is also discussed.

Consider the broadcast relay channel (BRC) which consists of a source sending information over a two user broadcast channel in presence of two relay nodes that help the transmission to the destinations. Clearly, this network with five nodes involves all the problems encountered in relay and broadcast channels. New inner bounds on the capacity region of this class of channels are derived. These results can be seen as a generalization and hence unification of previous work in this topic. Our bounds are based on the idea of recombination of message bits and various effective coding strategies for relay and broadcast channels. Capacity result is obtained for the semi-degraded BRC-CR, where one relay channel is degraded while the other one is reversely degraded. An inner and upper bound is also presented for the degraded BRC with common relay (BRC-CR), where both the relay and broadcast channel are degraded which is the capacity for the Gaussian case. Application of these results arise in the context of opportunistic cooperation of cellular networks.

The simultaneous relay channel is investigated where the source is unaware of the channel statistic controlling the communication but knows that this statistic is one of two possible discrete memoryless relay channels. We aim to derive coding schemes capable of transmitting information, regardless of which of these relays is present. First, this problem is recognized as being equivalent to that of sending common and private information to two destinations in presence of two helper relays. In this scenario, each possible relay links becomes a branch of the so-called broadcast relay channel. An inner bound on the capacity region of this channel is derived. Applications of these results arise when the source node is uncertain of the noise levels or the network topology (e.g. due to user mobility the positions of the relay and the destination nodes are unknown). Specific rates are computed for an AWGN relay channel, where the relay node may be absent but the source node is unaware of this.