We address comprehensive optimization of uplink spectral efficiency (SE) and resource allocation in multiple reconfigurable intelligent surfaces (RIS)-aided cell-free massive MIMO (CF-mMIMO) systems. While integrating CF-mMIMO with RISs enhances SE, existing solutions assume ideal conditions or separately optimize access point (AP) clustering, large-scale fading decoding (LSFD), RIS phase-shifts, and power allocation. To bridge these gaps, we propose a unified statistical channel state information (CSI)-based optimization (SCOP) framework that jointly optimizes AP clustering, LSFD, RIS phase-shift control, and uplink power allocation to maximize the minimum uplink SE. A closed-form SE expression is derived for maximum ratio (MR) combining, accounting for both direct and cascaded channels with spatially correlated Ricean fading. Leveraging statistical CSI significantly reduces real-time acquisition overhead while enabling robust and efficient uplink transmission design. SCOP is solved via a multi-step strategy: (i) for fixed power, an iterative algorithm computes joint optimization parameters (JOP) vector representing a combination of AP clustering, LSFD, and RIS phase-shift parameters; (ii) a closed-form solution updates power allocation; and (iii) an alternating optimization jointly refines both. We also introduce a novel method to extract the optimal system parameters from the JOP. Simulation results show that, in a representative 60 APs, 30 users, and 4 RISs scenario, the proposed SCOP framework lifts the median uplink SE from 2.4 bit/s/Hz to 3.9 bit/s/Hz (+65%) and more than doubles the bottom 5% rate, with similar 60–120% gains in other setups.

This paper proposes a secure wireless system that integrates rate-splitting multiple access (RSMA), intelligent reflecting surfaces (IRS), and full-duplex relaying (FDR) to enhance secrecy performance against multiple colluding eavesdroppers. Closed-form expressions for the secrecy outage probabilities (SOPs) and average secrecy capacities (ASCs) of both common and private messages are derived. Comparative analysis with a baseline RSMA-FDR system (without IRS) demonstrates the benefits of IRS in improving secrecy. Numerical results reveal that RSMA-IRS-FDR achieves superior secrecy performance, with SOPs and ASCs strongly influenced by transmission power, particularly differing between message types. Moreover, the adverse effect of residual self-interference (RSI) is substantially mitigated in the IRS-aided system. The secrecy performance is further enhanced by increasing the number of IRS elements. The study also investigates the impact of key parameters such as power allocation, target secrecy rate, fading order, Wi-Fi frequency, and number of eavesdroppers, offering practical insights for secure RSMA-IRS-FDR system design.

The ever-increasing demand for high-speed wireless communication has generated significant interest in utilizing frequency bands that are adjacent to those occupied by legacy wireless systems. Since the legacy wireless systems were designed based on often decades-old assumptions about wireless interference, utilizing these new bands will result in interference with the existing legacy users. Many of these legacy wireless devices are used by critical infrastructure networks upon which society depends. There is an urgent need to develop schemes that can protect legacy users from such interference. For many applications, legacy users are located within geographically-constrained regions. Several studies have proposed mitigating interference through the implementation of exclusion zones near these geographically-constrained regions. In contrast to solutions based on geographic exclusion zones, this paper presents a communication theory-based solution. By leveraging knowledge of these geographically-constrained regions, we aim to reduce the interference impact on legacy users. We achieve this by incorporating received power constraints, termed as region constraints, in our massive multiple-input multiple-output (MIMO) system design. We perform a capacity analysis for single-user massive MIMO and a sum-rate analysis for the multi-user massive MIMO system with transmit power and region constraints. We present a precoding design method that allows for the utilization of new frequency bands while protecting legacy users.

A novel multibeam time-division (TD) multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) approach is proposed to achieve a balanced tradeoff between sensing and communication functionalities and accurate sensing parameter estimation with a wide field-of-view. Firstly, the TD strategy is introduced to address the simultaneous high demands for sensing performance and communication rate. By allocating time resources between sensing and communications, this approach can reach a desired balance between them while avoiding spectrum and spatial interference, as well as competition in power allocation. Next, a new multibeam method is developed to achieve wide-area sensing for TD MIMO ISAC. Conventional multibeam methods typically rely on beam scanning for direction estimation, suffering from limited accuracy. Inspired by Doppler division multiple access (DDMA) approach, the proposed method divides the Doppler spectrum into more subbands, generating more beams than the number of transmit antenna elements using only phase modulation. Beyond enabling flexible control over the sensing coverage location through beam selection, the proposed method also improves parameter estimation accuracy by fully leveraging the inter-beam relationships, particularly for targets located at null directions. Specifically, for such targets, the proposed method achieves a significantly higher maximum unambiguous velocity, mitigating the velocity ambiguity inherent in conventional DDMA. Simulation results validate the effectiveness of the proposed approach in enhancing both the performance tradeoff between sensing and communication, and the accuracy of sensing parameter estimation.

This paper investigates an OFDM-based over-the-air federated learning (OTA-FL) system, where multiple mobile devices, e.g., unmanned aerial vehicles (UAVs), transmit local machine learning (ML) models to a central parameter server (PS) for global model aggregation. The high mobility of local devices results in imperfect channel estimation, leading to a misalignment problem, i.e., the model parameters transmitted from different local devices do not arrive at the central PS simultaneously. Moreover, the mobility introduces time-varying uploading channels, which further complicates the aggregation process. All these factors collectively cause distortions in the OTA-FL training process which are underexplored. To quantify these effects, we first derive a closed-form expression for a single-round global model update in terms of these channel imperfections. We then extend our analysis to capture multiple rounds of global updates, yielding a bound on the accumulated error in OTA-FL. We validate our theoretical results via extensive numerical simulations, which corroborate our derived analysis.

The reconstruction of sparse signals from a limited set of measurements poses a significant challenge as it necessitates a solution to an underdetermined system of linear equations. Compressed sensing (CS) deals with sparse signal reconstruction using techniques such as linear programming (LP) and iterative message passing schemes. The interval passing algorithm (IPA) is an attractive CS approach due to its low complexity when compared to LP. In this paper, we propose a sequential IPA that is inspired by sequential belief propagation decoding of low-density-parity-check (LDPC) codes used for forward error correction in channel coding. In the sequential setting, each check node (CN) in the Tanner graph of an LDPC measurement matrix is scheduled one at a time in every iteration, as opposed to the standard “flooding” interval passing approach in which all CNs are scheduled at once per iteration. The sequential scheme offers a significantly lower message passing complexity compared to flooding IPA on average, and for some measurement matrix and signal sparsity, a complexity reduction of 36% is achieved. We show both analytically and numerically that the reconstruction accuracy of the IPA is not compromised by adopting our sequential scheduling approach.

We present a novel approach for real-time photorealistic style transfer of digital humans in virtual reality environments using a lightweight U-Net-based neural network architecture. Our method transforms rendered VR images into photorealistic images while maintaining temporal consistency. Unlike previous approaches that attempt to support arbitrary style transfer, we focus on predefined target styles, enabling significantly higher performance and visual fidelity in real-time applications. Our technique achieves high frame rates (104 FPS at$2 ~\mathrm{K} \times 2 ~\mathrm{K}$resolution) through optimization and 8-bit integer quantization with NVIDIA's TensorRT. By incorporating foveated rendering techniques that prioritize processing in the center of vision, we further achieve$72+$FPS at$2 \times 2064 \times 2208$(stereo resolution) when integrated into a full PC-powered VR pipeline. The temporal artifacts such as flickering are eliminated through our direct image-to-image regression training, without additional temporal constraints. We evaluate two training methodologies: application-specific using sampled VR renderings, and generalized using diverse photorealistic datasets. Our experimental results demonstrate that our approach outperforms previous techniques in both quality and performance metrics for VR applications, enabling new possibilities for immersive photorealistic experiences.

Reconfigurable intelligent surfaces (RISs) are emerging as a transformative technology for sixth-generation (6G) wireless networks. They enable dynamic manipulation of the propagation environment to enhance signal coverage, mitigate interference, and improve spectral and energy efficiencies. However, this flexibility introduces significant security vulnerabilities when RISs are maliciously controlled. This study explores the threats posed by such RISs, focusing on their potential to compromise the security and integrity of 6G networks. From an adversarial perspective, we analyze key attack vectors, including sophisticated jamming attacks that disrupt communication, eavesdropping attacks that intercept communications, and pilot contamination attacks that impair channel estimation accuracy, all contributing to severe performance degradation. For each attack, we detail the underlying mechanisms and adversarial optimization strategies designed to maximize impact. A case study quantifies the practical effects of these malicious RIS-based attacks in a simulated 6G network scenario. This research emphasizes the critical need for robust defense mechanisms and proposes essential research directions to address the evolving threats from malicious RISs, ensuring the security of 6G networks.

Consider the Gaussian wiretap channel, where a transmitter wishes to send a confidential message to a legitimate receiver in the presence of an eavesdropper. It is well known that if the eavesdropper experiences less channel noise than the legitimate receiver, then it is impossible for the transmitter to achieve positive secrecy rates. A known solution to this issue consists in involving a second transmitter, referred to as a helper, to help the first transmitter to achieve security. While such a solution has been studied for the asymptotic blocklength regime and via non-constructive coding schemes, in this paper, for the first time, we design explicit and short blocklength codes using deep learning and cryptographic tools to demonstrate the benefit and practicality of cooperation between two transmitters over the wiretap channel. Specifically, our proposed codes show strict improvement in terms of information leakage compared to existing codes that do not consider a helper. Our code design approach relies on a reliability layer, implemented with an autoencoder architecture based on the successive interference cancellation method, and a security layer implemented with universal hash functions. We also propose an alternative autoencoder architecture that significantly reduces training time by allowing the decoders to independently estimate messages without successively canceling interference by the receiver during training. Additionally, we show that our code design is also applicable to the multiple access wiretap channel with helpers, where two transmitters send confidential messages to the legitimate receiver.

The reconstruction of sparse signals from a limited set of measurements poses a significant challenge as it necessitates a solution to an underdetermined system of linear equations. Compressed sensing (CS) deals with sparse signal reconstruction using techniques such as linear programming (LP) and iterative message passing schemes. The interval passing algorithm (IPA) is an attractive CS approach due to its low complexity when compared to LP. In this paper, we propose a sequential IPA that is inspired by sequential belief propagation decoding of low-density-parity-check (LDPC) codes used for forward error correction in channel coding. In the sequential setting, each check node (CN) in the Tanner graph of an LDPC measurement matrix is scheduled one at a time in every iteration, as opposed to the standard “flooding” interval passing approach in which all CNs are scheduled at once per iteration. The sequential scheme offers a significantly lower message passing complexity compared to flooding IPA on average, and for some measurement matrix and signal sparsity, a complexity reduction of approximately 36% is achieved. We show both analytically and numerically that the reconstruction accuracy of the IPA is not compromised by adopting our sequential scheduling approach.

Although user cooperation cannot improve the capacity of Gaussian two-way channels (GTWCs) with independent noises, it can improve communication reliability. In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users. We first formulate general encoding/decoding functions, where the user cooperation is captured by the coupling of user encoding processes. The coupling effect renders the encoder/decoder design non-trivial, requiring effective decoding to capture this effect, as well as efficient power management at the encoders within power constraints. To address these challenges, we propose two different twoway coding strategies: linear coding and learning-based coding. For linear coding, we propose optimal linear decoding and discuss new insights on encoding regarding user cooperation to balance reliability. We then propose an efficient algorithm for joint encoder/decoder design. For learning-based coding, we introduce a novel recurrent neural network (RNN)-based coding architecture, where we propose interactive RNNs and a power control layer for encoding, and we incorporate bi-directional RNNs with an attention mechanism for decoding. Through simulations, we show that our two-way coding methodologies outperform conventional channel coding schemes (that do not utilize user cooperation) significantly in sum-error performance. We also demonstrate that our linear coding excels at high signal-to-noise ratios (SNRs), while our RNN-based coding performs best at low SNRs. We further investigate our two-way coding strategies in terms of power distribution, two-way coding benefit, different coding rates, and block-length gain.

We address the design and optimization of real-world-suitable hybrid precoders for multi-user wideband sub-terahertz (sub- THz) communications. We note that the conventional fully connected true-time delay (TTD)-based architecture is impractical because there is no room for the required large num-ber of analog signal combiners in the circuit board. Additionally, analog signal combiners incur significant signal power loss. These limitations are often overlooked in sub- THz research. To overcome these issues, we study a non-overlapping subarray architecture that eliminates the need for analog combiners. We extend the conventional single-user assumption by formulating an optimization problem to maximize the minimum data rate for simultaneously served users. This complex optimization problem is divided into two sub-problems. The first sub-problem aims to ensure a fair subarray allocation for all users and is solved via a continuous domain relaxation technique. The second sub-problem deals with practical TTD device constraints on range and resolution to maximize the sub array gain and is resolved by shifting to the phase domain. Our simulation results highlight significant performance gain for our real-world-ready TTD-based hybrid precoders.

A recent line of research has been investigating deep learning approaches to wireless positioning (WP). Although these WP algorithms have demonstrated high accuracy and robust performance against diverse channel conditions, they also have a major drawback: they require processing high-dimensional features, which can be prohibitive for mobile applications. In this work, we design a positioning neural network (P-NN) that substantially reduces the complexity of deep learning-based WP through carefully crafted minimum description features. Our feature selection is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We also develop a novel methodology for adaptively selecting the size of feature space, which optimizes over balancing the expected amount of useful information and classification capability, quantified using information-theoretic measures on the signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP).

Consider the Gaussian wiretap channel, where a legitimate transmitter wishes to send a confidential message to a legitimate receiver in the presence of an eavesdropper. Unfortunately, in this setting, it is well known that if the eavesdropper experiences less channel noise than the legitimate receiver, then it is impossible for the transmitter to achieve positive secrecy rates. A known solution to this issue consists in involving a second transmitter, referred to as a helper, to help the first transmitter to achieve security. While such a solution has been studied for the asymptotic blocklength regime and via non-constructive coding schemes, in this paper, for the first time, we design explicit and short blocklength codes using deep learning and cryptographic tools to demonstrate the benefit and practicality of cooperation between two transmitters over the wiretap channel. Specifically, our proposed codes show strict improvement in terms of information leakage compared to existing point-to-point codes that do not consider a helper, even when the transmitter has adverse channel conditions, in the sense that the eavesdropper experiences less channel noise than the legitimate receiver. Our code design approach relies on a reliability layer, implemented with an autoencoder architecture inspired by the successive interference cancellation method developed for broadcast channels, and a security layer implemented with universal hash functions.

In this article, a multiunmanned aerial vehicle-mobile-edge computing (UAV-MEC) network is proposed for mobile devices (MDs) located far from a terrestrial base station (BS)-MEC. In the UAV-MEC network, the MDs offload tasks to the UAV-MECs, followed by task offloading from the UAV-MECs to the BS-MEC. Each UAV-MEC aims to jointly optimize its energy consumption, queue stability, and the energy consumption of the MDs by controlling its trajectory and offloading ratio. Specifically, the trajectories of the UAV-MECs are controlled to locate themselves at the optimal positions where the transmission energy of the MDs is minimized. Additionally, the UAV-MECs consider the constraints of task processing time and queue stability in determining the amount of task to be offloaded. Thus, an independent proximal policy optimization (IPPO)-based offloading and trajectory control learning model (IM) is proposed to solve these problems, and a convex optimization model (CM) is also presented to show the optimality of the proposed IM. Specifically, in IM, a near-optimal trajectory control for the randomly located MDs is enabled by exploiting channel gain information only without resorting to accurate location information of the MDs. Furthermore, a dynamic offloading ratio control of each UAV-MEC is achieved by considering its queue dynamics. The simulation results show that the proposed IM jointly optimizes energy consumption and queue stability. Consequently, it outperforms other deep reinforcement learning (DRL)-based algorithms by achieving low energy consumption and long service duration. Moreover, it achieves similar performance to CM while requiring remarkably less time on action decision.

This article introduces an innovative cell-free massive MIMO (CF-mMIMO) architecture integrating a low-Earth orbit satellite with unmanned aerial vehicles (UAVs) as flying access points (FAPs), referred to as a CF-mMIMO satellite-UAV network, aimed at enhancing uplink spectrum efficiency (SE) for ground users (GUs). Expressions are derived to estimate both direct and cascaded channels involving satellites, UAVs, and GUs, while proposing local combining strategies for the network. A straightforward dynamic clustering framework is developed to mitigate interference among GUs effectively, leveraging the inherent characteristics of UAV-based networks to optimize user service quality. An optimization model for FAP trajectory and energy consumption is proposed to effectively manage energy in limited-resource scenarios and enhance network performance. Additionally, max-min fairness power control issue is explored, with a closed-form solution being presented that ensures the equitable SE distribution among GUs. We also tackle a nonconvex optimization challenge to maximize the network’s sum-rate by refining GU power levels, incorporating large-scale fading decoding (LSFD) to further elevate SE. Extensive simulations validate the effectiveness of our proposed methods, illustrating significant improvements over traditional configurations. These approaches not only advance CF-mMIMO satellite-UAV network performance but also lay foundational strategies for future aerial communication systems.

Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios. Recently, open-RAN (O-RAN) techniques have begun adding enormous flexibility to RAN implementations. O-RAN is a natural architectural fit for cell-free massive multiple-input multiple-output (CFmMIMO) systems, where many geographically-distributed access points (APs) are employed to achieve ubiquitous coverage and enhanced user performance. In this paper, we address the decentralized pilot assignment (PA) problem for scalable O-RAN-based CFmMIMO systems. We propose a low-complexity PA scheme using a multi-agent deep reinforcement learning (MA-DRL) framework in which multiple learning agents perform distributed learning over the O-RAN communication architecture to suppress pilot contamination. Our approach does not require prior channel knowledge but instead relies on real-time interactions made with the environment during the learning procedure. In addition, we design a codebook search (CS) scheme that exploits the decentralization of our O-RAN CFmMIMO architecture, where different codebook sets can be utilized to further improve PA performance without any significant additional complexities. Numerical evaluations verify that our proposed scheme provides substantial computational scalability advantages and improvements in channel estimation performance compared to the state-of-the-art.

Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational complexity coming from processing high-dimensional features can be prohibitive for mobile applications. In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP. P-NN’s feature selection strategy is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We improve P-NN’s learning ability by intelligently processing two different types of inputs: sparse image and measurement matrices. Specifically, we implement a self-attention layer to reinforce the training ability of our network. We also develop a technique to adapt feature space size, optimizing over the expected information gain and the classification capability quantified with information-theoretic measures on signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP). In particular, we find that P-NN achieves a large improvement in performance for low SNR, as unnecessary measurements are discarded in our minimum description features.

In wideband sub-Terahertz (sub-THz) massive multiple-input multiple-output (MIMO) communication systems, the beam squint effect manifests as a substantial degradation in array gain. To mitigate the aforementioned beam squint effect, a hybrid precoding approach leveraging both true-time delay (TTD) and phase shifters (PS) has been proposed. However, existing methods operate under the assumption that the TTD device can generate any desired time delay value. These methods subsequently design the TTD precoder while fixing the PS precoder. This work presents a novel optimization framework for the joint TTD and PS precoder design, incorporating realistic time delay constraints for each TTD device. Unlike previous methods, our framework does not rely on the unbounded time delay assumption and optimizes the TTD and PS values jointly to cope with the practical limitations. Furthermore, within the context of our proposed framework, we mathematically determine the minimum number of TTD devices necessitated to achieve a predetermined target array gain. Simulations confirm the proposed approach exhibits performance improvement, guarantees array gain, and achieves computational efficiency.

The design of codes for feedback-enabled communications has been a long-standing open problem. Recent research on non-linear, deep learning-based coding schemes have demonstrated significant improvements in communication reliability over linear codes, but are still vulnerable to the presence of forward and feedback noise over the channel. In this paper, we develop a new family of non-linear feedback codes that greatly enhance robustness to channel noise. Our autoencoder-based architecture is designed to learn codes based on consecutive blocks of bits, which obtains de-noising advantages over bit-by-bit processing to help overcome the physical separation between the encoder and decoder over a noisy channel. Moreover, we develop a power control layer at the encoder to explicitly incorporate hardware constraints into the learning optimization, and prove that the resulting average power constraint is satisfied asymptotically. Numerical experiments demonstrate that our scheme outperforms state-of-the-art feedback codes by wide margins over practical forward and feedback noise regimes, and provide information-theoretic insights on the behavior of our non-linear codes. Moreover, we observe that, in a long blocklength regime, canonical error correction codes are still preferable to feedback codes when the feedback noise becomes high. Our code is available at https://anonymous.4open.science/r/RCode1.

Approximate message passing (AMP) is a scalable, iterative approach to signal recovery. For structured random measurement ensembles, including independent and identically distributed (i.i.d.) Gaussian and rotationally-invariant matrices, the performance of AMP can be characterized by a scalar recursion called state evolution (SE). The pseudo-Lipschitz (polynomial) smoothness is conventionally assumed. In this work, we extend the SE for AMP to a new class of measurement matrices with independent (not necessarily identically distributed) entries. We also extend it to a general class of functions, called controlled functions which are not constrained by the polynomial smoothness; unlike the pseudo-Lipschitz function that has polynomial smoothness, the controlled function grows exponentially. The lack of structure in the assumed measurement ensembles is addressed by leveraging Lindeberg-Feller. The lack of smoothness of the assumed controlled function is addressed by a proposed conditioning technique leveraging the empirical statistics of the AMP instances. The resultants grant the use of the SE to a broader class of measurement ensembles and a new class of functions.

Federated machine learning (FL) is a powerful technology which can be implemented to exploit the sheer amount of geographically distributed data for enhanced computation. Exploiting the impending proliferation of wireless devices, in this paper, we incorporate distributed quantized transmissions for reliable connectivity to a remote FL server. We develop a novel theoretical framework for the convergence analysis of the proposed network under joint impact of communication bit error rate (BER), and model quantization, and participation control. We show that the convergence rate of the network is affected by the BER and it can be improved via participation control. Through simulation, we demonstrate that our proposed model can provide the same performance as the conventional FL networks based on point-to-point communication while the energy consumption is divided across the distributed nodes.

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients. We consider adaptive IRS control in the practical setting where (i) the IRS reflection coefficients are attained by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station (BS) to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not practical in this setting due to the difficulty of channel estimation and the low data rate of the feedback channel. To address these challenges, we develop a novel adaptive codebook-based limited feedback protocol to control the IRS. We propose two solutions for adaptive IRS codebook design: (i) random adjacency (RA), which utilizes correlations across the channel realizations, and (ii) deep neural network policy-based IRS control (DPIC), which is based on a deep reinforcement learning. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by the proposed schemes.

A new approach is presented to the problem of compensating the beam squint effect arising in wideband terahertz (THz) hybrid massive multiple-input multiple-output (MIMO) systems, based on the joint optimization of the phase shifter (PS) and true-time delay (TTD) values under per-TTD device time delay constraints. Unlike the prior approaches, the new approach does not require the unbounded time delay assumption; the range of time delay values that a TTD device can produce is strictly limited in our approach. Instead of focusing on the design of TTD values, we jointly optimize both the TTD and PS values to effectively cope with the practical time delay constraints. Simulation results that illustrate the performance benefits of the new method for the beam squint compensation are presented. Through simulations and analysis, we show that our approach is a generalization of the prior TTD-based precoding approaches.

The minimum mean-squared error (MMSE) estimator is recognized as the best estimator for measuring transmission channel distortion in orthogonal frequency division multiplexing (OFDM) using pilot-symbol assisted modulation (PSAM) in the presence of noise. In practice, however, the estimator suffers from high complexity and relies on the estimation of second-order statistics which may change rapidly within small-scale fading environments in a high-mobility wireless transmission system. We propose using machine learning (ML) with Gaussian Process Regression (GPR) to adaptively learn the hyperparameters of a channel model, which then can be used to calculate the MMSE estimates. Moreover, GPR can be used to more accurately interpolate the channel estimates in between pilot symbols compared to linear interpolation techniques. After describing the learning process and its equivalency to MMSE, we derive the BER for a receiver using GPR for time-domain interpolation, then use BER to find a practical bound on the number of training points needed to achieve best performance. We show that the performance of GPR-based ML is comparable to that of more complex neural network-based ML.

The fact that the millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channel has sparse support in the spatial domain has motivated recent compressed sensing (CS)-based mmWave channel estimation methods, where the angles of arrivals (AoAs) and angles of departures (AoDs) are quantized using angle dictionary matrices. However, the existing CS-based methods usually obtain the estimation result through one-stage channel sounding that have two limitations: (i) the requirement of large-dimensional dictionary and (ii) unresolvable quantization error. These two drawbacks are irreconcilable; improvement of the one implies deterioration of the other. To address these challenges, we propose, in this paper, a two-stage method to estimate the AoAs and AoDs of mmWave channels. In the proposed method, the channel estimation task is divided into two stages, Stage I and Stage II. Specifically, in Stage I, the AoAs are estimated by solving a multiple measurement vectors (MMV) problem. In Stage II, based on the estimated AoAs, the receive sounders are designed to estimate AoDs. The dimension of the angle dictionary in each stage can be reduced, which in turn reduces the computational complexity substantially. We then analyze the successful recovery probability (SRP) of the proposed method, revealing the superiority of the proposed framework over the existing one-stage CS-based methods. We further enhance the reconstruction performance by performing resource allocation between the two stages. We also overcome the unresolvable quantization error issue present in the prior techniques by applying the atomic norm minimization method to each stage of the proposed two-stage approach. The simulation results illustrate the substantially improved performance with low complexity of the proposed two-stage method.

Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. A key challenge in providing this capability is the requirement for judicious management of the heterogeneous communication and computation resources that exist at the edge to meet processing needs. In this paper, we develop an optimization methodology that considers the network topology jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing. Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple-input multiple-output (MIMO) wireless devices. We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a “best-effort” at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient. As a component of these two methods, we develop a novel coordinated beamforming algorithm which we show obtains the optimal beamformer for a common receiver characteristic. Through numerical experiments, we find that our methodology yields substantial improvements in network overhead compared with local computation and partially optimized methods, which validates our joint optimization approach. Further, we find that the efficient alternate optimization scales well with the number of nodes, and thus can be a practical solution for D2D computing in large networks.

Today’s growth in the volume of wireless devices coupled with the demand for data-intensive use cases has motivated the deployment of millimeter-wave (mmWave) small-cell networks. Although it is true that mmWave networks can carry a large volume of traffic, highly intermittent connectivity and the challenges related to installing many small-cell base stations (BSs) in urban geometry have impeded its progression into practical networks. To cope with these challenges, we present, in this paper, an approach to the mmWave BS deployment (site planning) problem, based on the minimum-deployment-cost criterion that is subject to user equipment (UE) outage constraints. Unlike the prior works, the proposed model captures the randomness of link blockage and signal-to-interference-plus-noise-ratio (SINR) statistics in mmWave networks. We formulate the minimum-cost deployment problem as large-scale integer nonlinear programming (INP). To deal with the coupled and combinatorial of the problem, the large-scale INP has approached to devise a suboptimal but efficient algorithm by decomposing it into two subproblems: (i) cell coverage optimization and (ii) minimum subset selection. We provide the solutions to each subproblem as well as theoretical justifications of them. Simulation results that illustrate UE outage guarantees of the proposed BS deployment method are presented. The results reveal that the proposed method uniquely distributes the macro-diversity orders that are distinct from other benchmarks.

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We consider a practical setting where (i) the IRS reflection coefficients are configured by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not applicable in this setting due to the difficulty of channel estimation and the low feedback rate. Therefore, we develop a novel adaptive codebook-based limited feedback protocol where only a codeword index is transferred to the IRS. We propose two solutions for adaptive codebook design, random adjacency (RA) and deep neural network policy-based IRS control (DPIC), both of which only require the end-to-end compound channels. We further develop several augmented schemes based on RA and DPIC. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by our schemes.

In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver. The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large prelabeled datasets for training. To address these limitations, we present a frequency-domain denoising method based on a reinforcement learning framework that does not need a priori channel knowledge and pre-labeled data. Our methodology includes a new successive channel denoising process based on channel curvature computation, for which we obtain a channel curvature magnitude threshold to identify unreliable channel estimates. Based on this process, we formulate the denoising mechanism as a Markov decision process, where we define the actions through a geometry-based channel estimation update, and the reward function based on a policy that reduces mean squared error (MSE). We then resort to Q-learning to update the channel estimates. Numerical results verify that our denoising algorithm can successfully mitigate noise in channel estimates. In particular, our algorithm provides a significant improvement over the practical least squares (LS) estimation method and provides performance that approaches that of the ideal linear minimum mean square error (LMMSE) estimation with perfect knowledge of channel statistics.

The paper proposes a new text recognition network for scene-text images. Many state-of-the-art methods employ the attention mechanism either in the text encoder or decoder for the text alignment. Although the encoder-based attention yields promising results, these schemes inherit noticeable limitations. They perform the feature extraction (FE) and visual attention (VA) sequentially, which bounds the attention mechanism to rely only on the FE final single-scale output. Moreover, the utilization of the attention process is limited by only applying it directly to the single scale feature-maps. To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel. The multi-scale channels also undergo regular fusion with each other to develop the coordinated knowledge together. Quantitative evaluation and robustness analysis on the standard benchmarks demonstrate that the proposed network outperforms the state-of-the-art in most cases.

Average-based consensus time synchronization protocols have been widely used in wireless sensor networks owing to their robustness against a single point of failures. In these types of distributed protocols, each node is designed to have the same role as other nodes in the network. However, because of this design, these protocols are limited in that any unsynchronized node can negatively affect other nodes in the system. Since the presence of unsynchronized nodes, such as newly joined nodes or malicious nodes is unavoidable, average-based consensus time synchronization protocols are vulnerable in dynamic or hostile environments. In this article, we propose a new protocol named neighbor-aware time synchronization protocol (NTSP) that incorporates the neighbor-aware concept to overcome the aforementioned limitation and improve the robustness of the average-based consensus time synchronization protocol in both dynamic and hostile environments. In particular, by being aware of each neighbor’s status (e.g., synchronized, new, or unsynchronized neighbor), each node can decide whether to include a neighbor in the calculation of the consensus process. As a result, NTSP can prevent adverse effects from unsynchronized nodes. The simulation results demonstrate that NTSP has a relatively faster recovery time than gradient time synchronization protocol and Average TimeSynch when new nodes join and can protect the synchronization of the network from attacks by a malicious node.

In this paper, we investigate the performance of a full-duplex (FD) relay system where multi-antennas are exploited at source and destination. Unlike previous works, the impacts of imperfect channel state information (I-CSI), transceiver hardware noise (THN), and residual self-interference (RSI) are taken into account. We mathematically derive the exact closed-form expressions of the outage probability (OP), symbol error rate (SER), and ergodic capacity (EC) of the FD relay system with I-CSI, THN, and RSI over Nakagami-$m$fading channels. From the derived expressions, the performance of the considered system under the effects of three negative factors (I-CSI, THN, and RSI) is compared with that system in the case of all ideal factors (perfect channel state information (P-CSI), perfect transceiver hardware (P-TH) and perfect self-interference cancellation (P-SIC)), two ideal factors (P-CSI and P-TH, P-CSI and P-SIC, P-TH and P-SIC), or one ideal factor (P-CSI or P-TH or P-SIC). Numerical results show a strong impact of three negative factors on the OP, SER, and EC of the considered FD relay system, especially when the data transmission rate of the system and signal-to-noise ratio (SNR) are high. In particular, OP, SER, and EC go to the floors in the high SNR regime due to the three negative factors. Therefore, when I-CSI, THNs, and RSI exist in the FD relay system, we should use suitable source and relay transmission power to obtain excellent performance while saving energy consumption. Moreover, when two of the three negative factors are large enough, the remaining factor’s impact becomes weaker and may be neglected in certain circumstances.

We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based approach, called Kolmogorov model (KM). Unlike the previous KM, whose computational complexity is not scalable with the size of the problem, a new approach, centered on discrete monotonic optimization (DMO), is proposed, leading to significantly reduced complexity. We also present a Kolmogorov-Smirnov (KS) criterion for the advanced hypothesis testing, which does not require any subjective threshold setting compared to the frequency estimation (FE) method developed for the conventional KM. Simulation results that demonstrate the efficacy of the proposed KM learning for mmWave MIMO beam alignment are presented.

We investigate the problem of delay-efficient and reliable data delivery in ultra-dense networks (UDNs) that constitute macro base stations (MBSs), small base stations (SBSs), and mobile users. Considering a two-hop data delivery system, we propose a partial decode-and-forward (PDF) relaying strategy together with a simple and intuitive amicable encoding scheme for rateless codes to significantly improve user experience in terms of end-to-end delay. Simulation results verify that our amicable encoding scheme is efficient in improving the intermediate performance of rateless codes. It also verifies that our proposed PDF significantly improves the performance of the decode-and-forward (DF) strategy, and that PDF is much more robust against channel degradation. Overall, the proposed strategy and encoding scheme are efficient towards delay-sensitive data delivery in the UDN scenarios.

We propose a versatile cross-layer framework to analyze performance metrics for mobile traffic in fifth-generation (5G) millimeter wave (mmWave) networks. Our proposed framework is based on stochastic geometry, teletraffic models, and the classical Erlang Fixed Point Approximation method, with the objective of evaluating blocking probability, mean service time of user requests, and utilization rate of base stations by taking into account practical concerns in mmWave networks including blockages encountered by mmWaves in the physical layer, capacity constraints in the network layer, and the stochastic nature of mobile traffic. We demonstrate by numerical results that our analytical method is accurate and computationally-efficient.

In this paper, we study the distributed computational capabilities of device-to-device (D2D) networks. A key characteristic of D2D networks is that their topologies are reconfigurable to cope with network demands. For distributed computing, resource management is challenging due to limited network and communication resources, leading to inter-channel interference. To overcome this, recent research has addressed the problems of wireless scheduling, subchannel allocation, power allocation, and multiple-input multiple-output (MIMO) signal design, but has not considered them jointly. In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency. Given that the resulting problem is a non-convex mixed integer program (MIP) which is prohibitive to solve at scale, we decompose its solution into two parts: (i) a resource allocation subproblem, which optimizes the link selection and subchannel allocations, and (ii) MIMO signal design subproblem, which optimizes the transmit beamformer, transmit power, and receive combiner. Simulation results using wireless edge topologies show that our method yields substantial improvements in energy efficiency compared with cases of no offloading and partially optimized methods and that the efficiency scales well with the size of the network.

To make the system available at low-cost, millimeter-wave (mmWave) multiple-input multiple-output (MIMO) architectures employ analog arrays, which are driven by a limited number of radio frequency (RF) chains. One primary challenge of using large hybrid analog-digital arrays is that the digital baseband cannot directly access the signal to/from each antenna. To address this limitation, recent research has focused on retransmissions, iterative precoding, and subspace decomposition methods. Unlike these approaches that exploited the channel's low-rank, in this work we exploit the sparsity of the received signal at both the transmit/receive antennas. While the signal itself is de facto dense, it is well-known that most signals are sparse under an appropriate choice of basis. By delving into the structured compressive sensing (CS) framework and adapting them to variants of the mmWave hybrid architectures, we provide methodologies to recover the analog signal at each antenna from the (low-dimensional) digital signal. Moreover, we characterizes the minimal numbers of measurement and RF chains to provide this recovery, with high probability. We discuss their applications to common variants of the hybrid architecture. By leveraging the inherent sparsity of the received signal, our analysis reveals that a hybrid MIMO system can be "turned into" a fully digital one: the number of needed RF chains increases logarithmically with the number of antennas.

Gradient Time Synchronization Protocol (GTSP) is a consensus-based time synchronization protocol in which each node adjusts its logical clock by averaging the relative clock rate of neighbor nodes in every synchronization beacon interval. This process is repeated until the network achieves convergence (synchronization). Therefore, a short beacon interval can reduce the convergence time. However, it causes high energy consumption because messages are sent frequently. On the other hand, a longer beacon interval can reduce energy consumption, but the convergence time can be longer. In this paper, we propose Adaptive Gradient Time Synchronization Protocol (AGTSP), which allows to adjust the beacon interval of each node dynamically in a fully distributed manner to reduce convergence time as well as save energy. A performance evaluation is conducted to prove the effectiveness of AGTSP compared with GTSP.

TDMA protocols can provide a reliable, collision-free data-transferring mechanism for wireless sensor networks. However, it requires an effective scheduling (time slot assignment)algorithm, which is a challenging issue, especially in wireless multi-hop networks due to random-based competition. In this paper, we propose DSTO, a distributed TDMA scheduling algorithm using Topological Ordering (TO). DSTO aims to reduce conflict of scheduling time among neighbor nodes by creating a Topological Order with local neighborhood size as the main priority factor. We implemented DSTO on OPNET Network Simulator and proved its effectiveness compared to DRAND in terms of running time and message overheads.

A structured random matrix ensemble that maintains constant modulus entries and unit-norm columns, often called a random phase-rotated (RPR) matrix, is considered in this letter. We analyze the coherence statistics of RPR measurement matrices and apply them to acquire probabilistic performance guarantees of orthogonal matching pursuit (OMP) for support detection (SD). It is revealed via numerical simulations that the SD performance guarantee provides a tight characterization, especially when the signal is sparse.

An important challenge in the cyber-space is the effective identification of network anomalies, often caused by malicious activities. With the remarkable advances, machine learning algorithms have widely been studied for network intrusion and anomaly detection. In particular, deep learning based on neural network structures has recently been given a greater attention to deal with the growing complexity of data with higher dimensions and non-linearity. Convolutional Neural Networks (CNNs) is one of the widely employed deep learning methods. In this work, we introduce a new encoding technique that enhances the performance for the identification of anomalous events using a CNN structure. To evaluate, we utilize three different datasets for the extensive analysis. The experimental results show that our method consistently outperforms the gray-scale encoding technique previously proposed over the datasets employed in the evaluation.

In this paper, we present some contributions from our recent investigation. We address the open issue of interference coordination for sub-28 GHz millimeter-wave communication, by proposing fast- converging coordination algorithms, for dense multi-user multi-cell networks. We propose to optimize a lower bound on the network sum-rate, after investigating its tightness. The bound in question results in distributed optimization, requiring local information at each base station and user. We derive the optimal solution to the transmit and receive filter updates, that we dub non-homogeneous waterfilling, and show its convergence to a stationary point of the bound. We also underline a built-in mechanism to turn-off data streams with low SINR, and allocate power to high-SNR streams. This `stream control' is a at the root of the fast-converging nature of the algorithm. Our numerical result conclude that low- overhead coordination offers large gains, for dense sub-28 GHz systems. These findings bear direct relevance to the ongoing discussions around 5G New Radio.

Website fingerprinting attacks attempt to identify the website visited in anonymized and encrypted network traffic, that is, even if a user is using Tor and HTTPS. These attacks have been shown to be effective. Mitigations have been proposed which decreased the accuracy of the attacks from about 90% to about 20%. We propose a new mitigation technique based on traffic morphing and clustering. The intuition is that a lot of websites, by nature, are similar and can be clustered together. It is then easier and more efficient to make that whole cluster look exactly the same by using traffic morphing, rather than adding noise to make all websites look similar. All the websites in a cluster, thus, would become indistinguishable. There are many ways to perform traffic morphing. As a proof of concept, we used biggest, which means that all websites in a cluster will look exactly like the biggest website (in terms of network packet size) of that cluster. In simulating our proposed approach, the fingerprinting accuracy dropped from 70% to less than 1%.

Relay stations (RSs), as key components of next‐generation heterogenous networks, can be readily deployed between base station (BS) and users to extend cell coverage and improve throughput. Since RSs are connected with a donor BS through wireless backhaul‐links, i.e. BS–RS links, additional radio resources should be allocated to these links. For in‐band relaying, access‐links and backhaul‐links should be jointly optimised, which make the resource allocation problem for a relay network more difficult and challenging. In this study, the authors develop an analytical framework for resource allocation in relay networks with a time‐division duplexing mode for access‐link and backhaul‐link transmissions. The solution of the proposed analytical framework maximises the total utility of the relay network, and interference coordination among the BS and RSs is also considered. The developed framework is thoroughly evaluated by comparison with exhaustive search methods in different case studies. The comparison results show that the proposed framework provides optimal solutions with low computational complexity.

Communication at millimeter wave frequencies will be one of the essential new technologies in 5G. Acquiring an accurate channel estimate is the key to facilitate advanced millimeter wave hybrid multiple-input multiple-output (MIMO) precoding techniques. Millimeter wave MIMO channel estimation, however, suffers from a considerably increased channel use overhead. This happens due to the limited number of radio frequency (RF) chains that prevent the digital baseband from directly accessing the signal at each antenna. To address this issue, recent research has focused on adaptive closed-loop and two-way channel estimation techniques. In this paper, unlike the prior approaches, we study a non-adaptive, hence rather simple, open-loop millimeter wave MIMO channel estimation technique. We present a random phase rotation design of channel subspace sampling signals and show that they obey the restricted isometry property (RIP) with high probability. We then formulate the channel estimation as a low-rank subspace decomposition problem and, based on the RIP, show that the proposed framework reveals resilience to a low signal-to-noise ratio. It is revealed that the required number of channel uses ensuring a bounded estimation error is linearly proportional to the degrees of freedom of the channel, whereas it converges to a constant value if the number of RF chains can grow proportionally to the channel dimension while keeping the channel rank fixed. In particular, we show that the tighter the RIP characterization the lower the channel estimation error is. We also devise an iterative technique that effectively finds a suboptimal, but stationary, solution to the formulated problem. The proposed technique is shown to have improved channel estimation accuracy with a substantially low channel use overhead as compared to that of previous closed-loop and two-way adaptation techniques.

For physical layer security with multiple antennas over wireless channels, we consider an artificial noise-aided secure beamforming system. A transmitter can send the confidential information to the legitimate user more securely without eavesdropping when an artificial jamming signal interfering eavesdroppers is transmitted with the confidential information signal. The transmitter splits its transmit power for both the information and jamming signals with a power splitting factor. Under such a system model, we investigate the impacts on secrecy performance of a power splitting factor, the numbers of antennas and eavesdroppers, and noise variance at the legitimate receivers and eavesdroppers by analyzing the expected secrecy rate. The optimal power splitting factor and limiting secrecy rate with large number of antennas are also derived. Moreover, we examine the expected secrecy rate loss caused by channel variation over time. It is shown that the secrecy rate loss is independent of system parameters like a power splitting factor, the numbers of antennas and eavesdroppers in a high signal to interference plus noise ratio (SINR) region. In a low SINR region, on the other hand, the secrecy rate loss changes with system parameters. Simulation results verify these observations on ergodic secrecy rate and its loss due to time-varying channels.

A primary challenge for deploying dense small-cell networks comes from the lack of practical techniques that efficiently handle the increased network interference at a low cost. This has aroused considerable interest in the design of distributed precoder/combiner coordination techniques that leverage channel reciprocity, while relying on the local channel state information (CSI) available at each communication end. We present, in this paper, a power-efficient distributed coordination technique for dense small-cell multiple-input multiple-output (MIMO) networks. We optimize the linear filters by minimizing the transmit power subject to target signal-to- interference-plus-noise ratios (SINRs). Although this strategy enhances the power efficiency, the considered optimization problem is non-convex and not directly solvable even under centralized coordination. To address this difficulty, we propose distributed filter adaptation and power allocation techniques that are based on the primal and its dual problem formulations. Under this construction, the two sub-problems, i.e., the linear filter design and power allocation problems, are separable. To solve them, we devise the distributed Jacobi-type power allocation and maximum SINR filter design techniques. Improved power efficiency of the proposed technique as compared to other existing distributed techniques is evidenced.

For a mobile multihop relay (MMR) system, a centralised packet scheduling method is compared with a distributed packet scheduling method. The performance of the MMR system considering the joint effect of a finite‐length queue and an adaptive modulation and coding scheme for base station and relay station is analysed. A finite state Markov chain (FSMC) describing the queue and channel state pairs’ transitions for the MMR system is built. Moreover, a method of reducing the state space of the FSMC, which enables a hop‐by‐hop performance analysis of the MMR system without explosive growth in the number of states, is presented. Numerical and simulation results show that the proposed FSMC modelling is accurate and the performance of centralised scheduling becomes degraded because of increased mismatches between a scheduled modulation and coding scheme (MCS) level and the actual MCS level.

MIMO systems in the lower part of the millimetre-wave (mmWave) spectrum band (i.e., below 28 GHz) do not exhibit enough directivity and selectively, as compared to their counterparts in higher bands of the spectrum (i.e., above 60 GHz), and thus still suffer from the detrimental effect of interference, on the system sum rate. As such systems exhibit large numbers of antennas and short coherence times for the channel, traditional methods of distributed coordination are ill-suited, and the resulting communication overhead would offset the gains of coordination. In this paper, we propose algorithms for tackling the sum-rate maximization problem that are designed to address the above-mentioned limitations. We derive a lower bound on the sum rate, a so-called difference of log and trace (DLT) bound, shed light on its tightness, and highlight its decoupled nature at both the transmitters and receivers. Moreover, we derive the solution to each of the subproblems that we dub non-homogeneous waterfilling (a variation on the MIMO waterfilling solution), and underline an inherent desirable feature: its ability to turn-OFF streams exhibiting low SINR, and contribute to greatly speeding up the convergence of the proposed algorithm. We then show the convergence of the resulting algorithm, max-DLT, to a stationary point of the DLT bound. Finally, we rely on extensive simulations of various network configurations, to establish the fast-converging nature of our proposed schemes, and thus their suitability for addressing the short coherence interval, as well as the increased system dimensions, arising when managing interference in lower bands of the mmWave spectrum. Moreover, our results suggest that interference management still brings about significant performance gains, especially in dense deployments.

Directional narrow beams are used in the millimeter wave systems to provide sufficient array gains to overcome the severe pathloss as well as boost the system throughput. However, the directional link can be easily blocked by obstacles and incurs outage when the system is surrounded by many random obstacles. The blockage impediment of the millimeter wave system could be very critical and must be clearly analyzed before the system can be deployed and used to its full potential. In this work, we analyze and quantify the outage probability of a point-to-point millimeter wave link in the presence of first- order reflection paths. In our model, the reflection paths can be formed by the random obstacles. These obstacles can either reflect or block beams from the transmitter to the receiver. When the line-of-sight (LoS) path is blocked, the transmitter and receiver can form directional beams along with one of the reflection paths to recover the communication. We characterize the distribution of the number of obstacles that can potentially create the first-order reflection paths and derive the outage probability. Simulation results demonstrate the accuracy of our analysis. The results also show that the presence of the first-order reflections can mitigate the outage by a great amount.

Millimeter wave multiple-input multiple-output (MIMO) communication systems must operate over sparse wireless links and will require large antenna arrays to provide high throughput. To achieve sufficient array gains, these systems must learn and adapt to the channel state conditions. However, conventional MIMO channel estimation can not be directly extended to millimeter wave due to the constraints on cost-effective millimeter wave operation imposed on the number of available RF chains. Sparse subspace scanning techniques that search for the best subspace sample from the sounded subspace samples have been investigated for channel estimation. However, the performance of these techniques starts to deteriorate as the array size grows, especially for the hybrid precoding architecture. The millimeter wave channel estimation challenge still remains and should be properly addressed before the system can be deployed and used to its full potential. In this work, we propose a sparse subspace decomposition (SSD) technique for sparse millimeter wave MIMO channel estimation. We formulate the channel estimation as an optimization problem that minimizes the subspace distance from the received subspace samples. Alternating optimization techniques are devised to tractably handle the non-convex problem. Numerical simulations demonstrate that the proposed method outperforms other existing techniques with remarkably low overhead.

Spatial modulation (SM) is a new modulation technique where the information to be sent is encoded using one or more symbols and the subspace over which the symbols are transmitted. Unfortunately, a general capacity analysis that encompasses different forms of SM systems has not been developed. In this paper, we consider a general form of SM, where the number of transmitted data streams is allowed to vary. We refer to this form of SM as generalized spatial modulation with multiplexing (GSMM). A Gaussian mixture model (GMM) is shown to accurately model the transmitted spatially modulated signal using a precoding framework. Using this transmit model, a closed-form expression for the achievable rate when operating over Rayleigh fading channels is evaluated, and tight upper and lower bounds for the achievable rate are proposed. The expressions of the achievable rate obtained are flexible enough to accommodate any form of SM—where any subspace can be used for the transmission—by adjusting the precoding set. Simulations are presented to show the tightness of the proposed bounds. The effect of the system dimensions and a comparison with other prominent capacity results are also demonstrated in simulations.

Urban outdoor millimeter wave communication systems will comprise of dense cell deployment and large-sized array antennas. In this paper, a millimeter wave downlink system-level simulator operating at 73 GHz, which emulates multiple base stations (BSs) and user equipments (UEs) in an outdoor street geometry, is developed. Each BS and UE are surrounded by moving obstacles (e.g., moving cars and walking pedestrians). Two-dimensional (2D) array antennas are employed at both UE and BS to form the 3-dimensional (3D) beams to combat the severe pathloss found in the spectrum. The 3D ray tracer customized to the 3D beam patterns is implemented in the simulator using the OpenGL platform. The 3D ray tracer, in our simulator, carries out the beam alignment task and produces physical channels between UE and BS. The physical channel representation characterizes useful link statistics, e.g., link blockage and co-channel interference statistics. It is shown through the simulation study that high density BS deployment with the directional 3D beamforming enhances the link quality and decreases outage probability compared to the lower density BS deployment. However, the performance degradation caused by the co-channel interference compared to the zero-interference case is still significant. Furthermore, it is observed that high density moving obstacles deteriorate both the throughput and outage performances compared to the low density case because of the increased link blockage.

Millimeter wave multiple-input multiple-output (MIMO) systems equipped with large-sized array antennas have a great potential to boost the achievable data rates by leveraging large bandwidth available at the spectrum. In this paper, we focus on the angle of departure (AoD) and angle of arrival (AoA) tracking problem for temporally correlated sparse millimeter wave MIMO channels. With the beam space MIMO channel modeling, a low complexity two-sided channel sounding scheme for the AoD and AoA tracking is investigated. The adoption of the sparse beam space MIMO channel representation eventually converts the AoD and AoA estimation problem to the support recovery problem. For the support recovery, we employ an iterative soft thresholding algorithm which is broadly referred to as the approximate message passing (AMP) algorithm. The support recovery performance of the AMP, however, suffers from serious deterioration especially at the low SNR regime. To remedy, several directional sounding beam design schemes are proposed to ameliorate the performance of the channel tracking at the low SNR. Simulation results demonstrate that the proposed AoD and AoA tracking outperforms other benchmark schemes when compared in a low SNR setting.

The demand for smart environment technologies necessitates technical innovations in sensor networks. It is now well expected that smart sensor technologies will soon be deployed for a wide variety of applications. Interest in applications of sensor networks will keep growing and will create a new class of industrial standardizations in various different application domains. In this context, the papers in this special section cover a number of novel contributions related to energy efficient PHY-aided MAC techniques and sensor applications. Targeted are recent trends, research outcomes, practical developments of sensor infrastructure, reliable MAC solutions leveraging advanced PHY-layer aspects, and internet-of-things (IoT) solutions as well as compact and low-power sensor device technologies, which are applied to challenge measurement problems and applications.

It is now well expected that low-power sensor networks will soon be deployed for a wide variety of applications. These networks could potentially have millions of nodes spread in complex indoor/outdoor environments. One of the major deployment challenges under such diverse communication environments is providing reliable communication links to those low cost and/or battery-powered sensor nodes. Over the past few years, research in physical (PHY)-layer has demonstrated promising progresses on link reliability and energy efficiency. In modern medium access control (MAC) design, energy efficiency has become one of the key requirements and is still a hot research topic. In this overview, we provide a broad view encompassing both PHY- and MAC-layer techniques in the field of sensor networks with a focus on link reliability and energy efficiency. We review work in systems employing various PHY techniques in spatial diversity, energy efficient modulation, packet recovery, and data fusion, as well as MAC protocols in contention-based duty cycling, contention-free duty cycling, and hybrid duty cycling. The latest developments in cross-layer MAC designs that leverage PHY-layer techniques are presented. We also provide a synopsis of recent development and evolution of sensor network applications in industrial communications.

Low power sensor networks with communication enabled by WiFi are expected to be widely deployed. A major challenge is collecting event-driven uplink data from a large number of low-power sensors with low latency. In WiFi, the access point (AP) typically polls nodes individually to schedule uplink transmission times, resulting in a large latency. In this paper, we present a physical (PHY) layer-aided medium access control (MAC) framework to enhance the uplink throughput of sensor data traffic. In the approach, the acknowledgements from the sensor nodes to the poll message are parallelized. By detecting the parallel acknowledgement, the AP knows which nodes have data to send and allocates channel resources by sending a pull message. This approach is referred to as the probe and pull MAC (PPMAC) mechanism. Our scheme is based on maximizing the achievable throughput of PPMAC by optimizing the PHY layer components. More precisely, we investigate the parallel acknowledgement detector design problem and develop a non-convex optimization framework that maximizes the PPMAC throughput by optimizing the parallel acknowledgement detection statistics. Numerical examples illustrate that PPMAC outperforms the point coordination function (PCF) and distributed coordination function (DCF) mechanisms, standardized in IEEE 802.11, in terms of the achievable throughput and the overhead.

In this work, we study the so-called leakage minimization problem, within the context of interference alignment (IA). For that purpose, we propose a novel approach based on controlled perturbations of the leakage function, and show how the latter can be used as a mechanism to control the algorithm's convergence (and thus tradeoff convergence speed for reliability). Although the proposed scheme falls under the broad category of stochastic optimization, we show through simulations that it has a quasi-deterministic convergence that we exploit to improve on the worst case performance of its predecessor, resulting in significantly better sum-rate capacity and average cost function value.

Collecting sensory data at access point (AP) from large number of sensor nodes with low latency is a critical issue. In Wi-Fi, prior to uplink data delivery, AP typically needs to poll large number of sensor nodes sequentially and allocate channel resources to individual node resulting in large latency. An efficient method to reduce the latency and power consumption in wireless sensor networks is to parallelize the polling operation so that multiple nodes can concurrently respond to the poll request of an AP by sending orthogonal sequences with uplink power control. In this paper, we present a conceptually simple uplink power control scheme for the parallel polling operation between AP and low power sensor nodes. We formulate the uplink power control problem as a sequence design problem and show that uplink channel state information (CSI) required to achieve a given target receive SNR can be significantly reduced by carefully designing sequences. We further develop a low complexity instantaneous (fast) power control scheme in order to reduce the number of computations required by the low power sensor node. We also analyze and compare the detection performance of the instantaneous (fast) and average (slow) power control schemes in terms of diversity gain.

In recent years, the scheduling scheme of exploiting multiuser diversity gain has attracted much attention for the purpose of efficient spectrum utilisation. The authors consider the problem of analysing the quality of service in the scheduler extracting multiuser diversity gain. The authors use not only the channel state information but also the queue information in determining the optimal user to be scheduled. Through queuing analysis, the guaranteed rate, the average delay and the delay‐bound violation probability can be calculated.

Recently, there has been considerable interest in new tiered network cellular architectures, which would likely use many more cell sites than found today. Two major challenges will be i) providing backhaul to all of these cells and ii) finding efficient techniques to leverage higher frequency bands for mobile access and backhaul. This paper proposes the use of outdoor millimeter wave communications for backhaul networking between cells and mobile access within a cell. To overcome the outdoor impairments found in millimeter wave propagation, this paper studies beamforming using large arrays. However, such systems will require narrow beams, increasing sensitivity to movement caused by pole sway and other environmental concerns. To overcome this, we propose an efficient beam alignment technique using adaptive subspace sampling and hierarchical beam codebooks. A wind sway analysis is presented to establish a notion of beam coherence time. This highlights a previously unexplored tradeoff between array size and wind-induced movement. Generally, it is not possible to use larger arrays without risking a corresponding performance loss from wind-induced beam misalignment. The performance of the proposed alignment technique is analyzed and compared with other search and alignment methods. The results show significant performance improvement with reduced search time.

A major challenge in low power sensor networks is collecting data from large number of nodes with low latency. Access point (AP) typically needs to poll large number of nodes individually and schedule transmission times for each of the nodes resulting in large latency. In this paper, we propose a physical layer technique that parallelizes the polling response from multiple nodes (so called parallel acknowledgment) in order to decrease the latency. Each node responds to the poll request of an AP by using the well-known Zadoff-Chu (ZC) sequences. By using the orthogonal properties of the ZC sequences, the AP is able to resolve the multiple signatures of different nodes simultaneously. We further develop low complexity transmission scheme for the ZC sequences in order to reduce the number of computations and complexity required by the low power sensor nodes. We also compare the performance of both time and frequency domain receivers.

In downlink multiuser multiple-input multiple-output (MIMO) systems, system performance highly depends on the reliability of downlink channel state information (CSI) at the base station (BS). In frequency division duplexing, the most practical solution is to have downlink CSI from the users fed back to the BS. Most work on this feedback design has assumed independent block fading channels. However, this paper proposes a new differential feedback scheme using the observation that the channel realizations are usually temporally correlated. The sum-rate loss assuming differential feedback is analyzed for a system with the number of users K equal to the number of transmit antennas M. When K >; M, a user selection algorithm based on an approximated signal-to-interference plus noise ratio (SINR) estimation is proposed. In simulation results, the proposed differential feedback scheme increases the sum-rate compared to previous differential feedback schemes. Moreover, the proposed user selection algorithm outperforms semi-orthogonal user selection in the moderate signal-to-noise ratio (SNR) region, despite requiring less feedback information. In low mobility channels, utilizing the channels' time correlation during quantization is shown to play a bigger role in determining sum-rate performance than multiuser diversity for most SNR regimes when a practical number of users is considered.

We consider a homogeneous multiple cellular scenario with multiple users per cell, i.e., K ≥ 1 where K denotes the number of users in a cell. In this scenario, a degrees of freedom outer bound as well as an achievable scheme that attains the degrees of freedom outer bound of the multicell multiple access channel (MAC) with constant channel coefficients are investigated. The users have M antennas, and the base stations are equipped with N antennas. The found outer bound is general in that it characterizes a degrees of freedom upper bound for K ≥ 1 and L >; 1 where L denotes the number of cells. The achievability of the degrees of freedom outer bound is studied for two cell case (i.e., L = 2). The achievable schemes that attains the degrees of freedom outer bound for L = 2 are based on two approaches. The first scheme is a simple zero forcing with M = Kβ+β and N = Kβ, and the second approach is null space interference alignment with M = Kβ and N = Kβ + β where β >; 0 is a positive integer.

The network capacity of wireless systems can be increased drastically by extracting multiuser diversity gain. The multiuser diversity gain is obtained by granting scheduling priority to the mobile station (MS) with the best current channel condition. There are a couple of drawbacks in multiuser diversity schedulers such as the linearly increasing feedback load with the number of MSs and the degrading fairness. To balance the capacity request against the fairness request, the authors need a scheduler achieving capacity and fairness tradeoff. In this study, the authors propose a new scheduling scheme of selecting a single MS, which can achieve the trade-off between capacity and fairness with reduced feedback. The simulation result shows that the proposed scheduling scheme attains better fairness compared with the conventional method for achieving capacity and fairness trade-off.

In this paper, an adaptive limited feedback linear precoding technique for temporally correlated multiple-input multiple-output (MIMO) channels is proposed, where the receiver has perfect channel knowledge but the transmitter only receives a quantized channel direction. To perform adaptation to the time correlation structure, we employ a differential feedback, where the "amount" of the perturbation added to the previous precoder is determined by the statistics of the directional variation. Based on random matrix quantization analysis, we develop a spherical cap codebook approach, where the cap is centered at the previous precoder and the radius of the cap is determined proportional to the identified directional variation. If the channel is highly correlated in time, it is shown that the proposed differential feedback scheme achieves significant throughput improvement in the large codebook size regime. The rest of the paper is devoted to developing a systematic spherical cap codebook generation method. The developed approach employs a feedback scheme that uses a differential rotation of the previously used precoder. Our codebook adaptation is based on generating a perturbation in Euclidean space and projecting the perturbation onto the unitary space. Simulation results show that the proposed adaptation scheme accurately tracks the channel using only a small rate of feedback.

Allowing the receiver in a multiple antenna wireless system to send a limited amount of channel state information (CSI) feedback is an effective way to enable channel adaptive signaling. This paper addresses the problem of controlling the feedback update period and feedback rate of limited feedback multiple antennas systems in temporally correlated channels. The challenge in our problem is how to assign the feedback update period and feedback rate subject to a constraint on feedback overhead. The presented approach analyzes the required CSI feedback rate and feedback update period by maximizing a lower bound on the average normalized effective signal-to-noise ratio. By imposing the channel evolution structure and employing a random quantization argument, we are able to determine a closed-form solution. This result leads to a bound on the feedback rate that characterizes when the proposed feedback update control scheme outperforms the conventional feedback scheme. Both analytical and numerical results demonstrate that the proposed feedback control strategy improves the average effective SNR with a relatively small amount of feedback overhead.

This paper concerns a simple limited feedback scheme taking temporal correlation into account during the feedback design in slow fading environment. In this method, the transmitter and the receiver reuse the past channel state information (CSI) as side information. Feedback, that is designed to leverage the side information, is sent from the receiver to the transmitter using a predetermined update period. The feedback update period is determined by characterizing the temporal correlation statistic, so that the proposed feedback reuse scheme outperforms a feedback scheme that does not adapt to the temporal correlation. To measure the performance, average effective SNR loss is used. Bounds on the feedback update period and the amount of feedback needed are derived. Simulation results show a reduction in the required average feedback overhead and a performance improvement when comparing the proposed scheme with prior feedback approaches.

Dual-polarized multiple-input multiple-output (MI-MO) antenna systems, where the antennas are grouped in pairs of orthogonally polarized antennas, are a spatially-efficient alternative to single polarized MIMO antenna systems. A limited feedback beamforming technique is proposed for dual-polarized MIMO channels where the receiver has perfect channel knowledge but the transmitter only receives partial information regarding the channel instantiation. The system employs an effective signal-to-noise ratio (SNR) distortion minimizing codebook to convey channel state information (CSI) in the form of beamforming direction. By investigating the average SNR performance of this system, an upper bound on the average SNR distortion is found as a weighted sum of two beamforming distortion metrics. The distortion minimization problem is solved by designing a concatenated codebook. Finally, we propose a codebook switching scheme exploiting the cross-polar discrimination (XPD) statistics. Simulations show that the proposed codebook switching scheme with an XPD dependent concatenated codebook has the ability to adapt to dual-polarized channels.

Collocated dual-polarized multiple-input multiple-output (MIMO) antenna systems provide a cost-space efficient alternative to current MIMO antenna systems. A limited feedback beamforming technique is proposed for dual-polarized MIMO channels where the receiver has perfect channel knowledge but the transmitter only receives quantized information regarding the channel instantiation. By decomposing the dual-polarized channel into a single polarized and a decoupled dual-polarized channel, the performance distortion metric can be expressed as a weighted sum of two beamforming distortion metrics. The distortion minimization problem is solved in a suboptimal way by locally minimizing each beamforming distortion metric. A concatenated codebook design approach is proposed. The capacity performance is compared with an interpolated codebook adapted to the cross- polar discrimination (XPD) through linear interpolation. From the simulation results, a concatenated codebook outperforms an interpolated codebook given the same number of feedbacks bits.

In fading channels, multiple-input multiple-output (MIMO) wireless systems make use of the spatial dimension of the channel to provide considerable capacity gain even when only partial channel state information (CSI) is available to the transmitter. A limited feedback linear preceding technique is proposed for temporally correlated MIMO channels where the receiver has perfect channel knowledge but the transmitter only receives quantized information regarding the channel instantiation. For this set-up, we first analyze capacity performance of a general rotation based limited feedback MIMO system in a Rayleigh flat fading channel. It can be shown that the minimum distance of the rotation codebook is related to the capacity performance of the system. Then, we present a framework for rotation based differential feedback by constructing the rotation codebook to adapt to the temporal correlation structure. Monte Carlo simulation results are presented to show the capacity performance of the proposed codebook design.

This paper proposes a reliable and efficient blind channel estimation method and a linearly constrained minimum variance (LCMV) receiver with circular precoding for single transmitter and receiver antenna orthogonal frequency division multiplexing (OFDM) system. The proposed blind channel estimation method is based on the interesting cross-correlation property of the cyclic prefix (CP) in an OFDM symbol. Toeplitz structure induced in the cross-correlation matrix allows us to employ a simple and reliable method for extracting channel impulse response effectively. The LCMV optimization criterion is applied for symbol detection of the OFDM symbols. At the same time, a circular precoding scheme for minimizing the maximal power of the dominant interference plus noise in LCMV receiver is considered. In the simulation study, proposed method is compared with other circularly precoded scheme and it demonstrate the superior performance of the proposed method.

We investigate closed-form bit error rate (BER) expressions for the double space-time transmit diversity (DSTTD) system with a zero-forcing decision-feedback (ZF-DF) detector. We show that the lower Alamouti's STTD unit can obtain the second order diversity gain. However, the upper one cannot guarantee the fourth order diversity due to the effect of error propagation. For example, it simply gives 3.5 dB signal-to-noise ratio (SNR) advantage over the lower one at the bit error rate (BER) of 10-3. Under the same environment, overall BER performance also suffers from 5.6 dB performance degradation over a maximum likelihood (ML) detector.

We demonstrate an efficient inverse scattering algorithm for reconstructing the coupling profiles or reflection coefficients of a discrete layered medium. The method, called the Schur algorithm combined with an extrapolation method, is based on solving the coupled-mode differential equation in a layer-peeling procedure with a simple extrapolation scheme. In order to reduce the estimation errors caused by discretization of an inhomogeneous medium, we analyze the error propagation of inverse scattering with the Schur algorithm (layer-peeling method) and propose the extrapolation scheme. Numerical examples are provided that compare the Schur algorithm combined with an extrapolation method to the general Schur algorithm in a coarse discretization environment. The comparison shows that the proposed method produces a more accurate reconstruction with a lower order of complexity.