Despite the success of Test-time adaptation (TTA), recent work has shown that adding relatively small adversarial perturbations to a limited number of samples leads to significant performance degradation. Therefore, it is crucial to rigorously evaluate existing TTA algorithms against relevant threats and implement appropriate security countermeasures. Importantly, existing threat models assume test-time samples will be labeled, which is impractical in real-world scenarios. To address this gap, we propose a new attack algorithm that does not rely on access to labeled test samples, thus providing a concrete way to assess the security vulnerabilities of TTA algorithms. Our attack design is grounded in theoretical foundations and can generate strong attacks against different state of the art TTA methods. In addition, we show that existing defense mechanisms are almost ineffective, which emphasizes the need for further research on TTA security. Through extensive experiments on CIFAR10-C, CIFAR100-C, and ImageNet-C, we demonstrate that our proposed approach closely matches the performance of state-of-the-art attack benchmarks, even without access to labeled samples. In certain cases, our approach generates stronger attacks, e.g., more than 4% higher error rate on CIFAR10-C.

Test Time Adaptation (TTA) has emerged as a practical solution to mitigate the performance degradation of Deep Neural Networks (DNNs) in the presence of corruption/ noise affecting inputs. Existing approaches in TTA continuously adapt the DNN, leading to excessive resource consumption and performance degradation due to accumulation of error stemming from lack of supervision. In this work, we propose Domain-Aware Real- Time Dynamic Adaptation (DARDA) to address such issues. Our key approach is to proactively learn latent representations of some corruption types, each one associated with a sub-network state tailored to correctly classify inputs affected by that corruption. After deployment, DARDA adapts the DNN to previously unseen corruptions in an unsupervised fashion by (i) estimating the latent representation of the ongoing corruption; (ii) selecting the sub-network whose associated corruption is the closest in the latent space to the ongoing corruption; and (iii) adapting DNN state, so that its representation matches the ongoing corruption. This way, DARDA is more resource-efficient and can swiftly adapt to new distributions caused by different corruptions without requiring a large variety of input data. Through experiments with two popular mobile edge devices - Raspberry Pi and NVIDIA Jetson Nano - we show that DARDA reduces energy consumption and average cache memory footprint respectively by 1.74 x and 2.64 x with respect to the state of the art, while increasing the performance by 10.4%, 5.7% and 4.4% on CIFAR-10, CIFAR-100 and TinyImagenet.

Channel Autoencoders (CAEs) have shown significant potential in optimizing the physical layer of a wireless communication system for a specific channel through joint end-to-end training. However, the practical implementation of CAEs faces several challenges, particularly in realistic and dynamic scenarios. Channels in communication systems are dynamic and change with time. Still, most proposed CAE designs assume stationary scenarios, meaning they are trained and tested for only one channel realization without regard for the dynamic nature of wireless communication systems. Moreover, conventional CAEs are designed based on the assumption of having access to a large number of pilot signals, which act as training samples in the context of CAEs. However, in real-world applications, it is not feasible for a CAE operating in real-time to acquire large amounts of training samples for each new channel realization. Hence, the CAE has to be deployable in few-shot learning scenarios where only limited training samples are available. Furthermore, most proposed conventional CAEs lack fast adaptability to new channel realizations, which becomes more pronounced when dealing with a limited number of pilots. To address these challenges, this paper proposes the Online Meta Learning channel AE (OML-CAE) framework for few-shot CAE scenarios with dynamic channels. The OML-CAE framework enhances adaptability to varying channel conditions in an online manner, allowing for dynamic adjustments in response to evolving communication scenarios. Moreover, it can adapt to new channel conditions using only a few pilots, drastically increasing pilot efficiency and making the CAE design feasible in realistic scenarios.

During advanced surveillance missions, Unmanned Aerial Vehicles (UAVs) usually require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints, and the possible node failures. To address these critical challenges, we propose a novel A 2 - UAV framework that optimizes the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel Application-Aware Task Planning Problem ( A 2 - TPP ) to optimize routing, data pre-processing and target assignment for each UAV. Our formulation explicitly takes into account (i) the relationship between CV task accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions , (iii) the current energy/position of the UAVs, and (iv) the possible node failures. We demonstrate A 2 - TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A 2 - UAV through simulation and real-world experiments using a testbed composed by four DJI Mavic Air 2 UAVs. Results on image classification show that A 2 - UAV attains on average around 38% more accomplished tasks w.r.t. the state of the art, with a 400% improvement in tasks-intensive scenarios. Moreover, we show that our framework is able to reconfigure the network in case of nodes failure.

Emerging mobile applications often require the execution of computer vision (CV) tasks based on compute-and memory-intensive deep neural networks (DNNs). Although offloading CV tasks to edge servers can decrease resource consumption at the mobile devices, it poses the challenge of handling multiple concurrent tasks with limited computing and memory capacity. In stark opposition with the existing state of the art, we tackle this challenge by jointly optimizing (i) the utilization of resources at the edge, among which memory - so far widely overlooked - and the radio resources used for task offloading; (ii) which and how many offloaded tasks should be executed; and (iii) the structure of the DNNs. First, we formulate the DNN for scalable Offloading of Tasks (DOT) problem, prove that it is NP-hard, and envision a weighted-tree-based heuristic solution, named OffloaDNN, that efficiently solves the DOT problem. We evaluate OffloaDNN through extensive numerical analysis using state-of-the-art image classification ResNet-18, as well as real-world experiments on the Colosseum emulator. The numerical results show that, in small-scale scenarios, OffloaDNN matches the optimum very closely, and, in larger-scale scenarios, increases the number of admitted offloaded tasks by 26.9 % with respect to the state of the art, while saving 82.5 % memory and 77.4% per-inference computing time. The numerical results are confirmed by the real-world validation on Colosseum.

The next generation of mobile networks (NextG) will require careful resource management to support edge offloading of resource-intensive deep learning (DL) tasks. Current slicing frameworks treat all DL tasks equally without adjusting to their high-level objectives, resulting in sub-optimal performance. To overcome this, we proposeSEM-O-RAN, a semantic and flexible slicing framework for computer vision task offloading in NextG Open RANs. Our framework accounts for the semantic nature of object classes as well as the level of data quality to optimally tailor data compression and minimize the usage of networking and computing resources. In fact, we show that different object classes tolerate different levels of image compression while preserving detection accuracy. To address the above issues, we first present the mathematical formulation of the Semantic Flexible Edge Slicing Problem (SF-ESP), which turns out to be NP-hard. We thus define a greedy algorithm to solve it efficiently, which is also able to always select the resource allocation that yields the best resource utilization, whenever multiple allocations satisfy the DL task requirements. We evaluateSEM-O-RAN's performance through extensive numerical analysis and real-world experiments on the Colosseum testbed, considering state-of-the-art computer-vision tasks and DL models. The obtained results demonstrate thatSEM-O-RANallocates up to 169% more tasks and obtains 52% higher revenues than the state of the art.

As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing. Network slicing has emerged as a potential solution to address the different service requirements. However, managing network slices while maintaining quality of services (QoS) in dynamic environments is a challenging task. Utilizing machine learning (ML) approaches for optimal control of dynamic networks can enhance network performance by preventing Service Level Agreement (SLA) violations. This is critical for dependable decision-making and satisfying the needs of emerging networks. Although RL-based control methods are effective for real-time monitoring and controlling network QoS, generalization is necessary to improve decision-making reliability. This paper introduces an innovative attention-based deep RL (ADRL) technique that leverages the O-RAN disaggregated modules and distributed agent cooperation to achieve better performance through effective information extraction and implementing generalization. The proposed method introduces a value-attention network between distributed agents to enable reliable and optimal decision-making. Simulation results demonstrate significant improvements in network performance compared to other DRL baseline methods.

Indoor localization has gained significant attention in recent years due to its various applications in smart homes, industrial automation, and healthcare, especially since more people rely on their wireless devices for location-based services. Deep learning-based solutions have shown promising results in accurately estimating the position of wireless devices in indoor environments using wireless parameters such as Channel State Information (CSI) and Received Signal Strength Indicator (RSSI). However, despite the success of deep learning-based approaches in achieving high localization accuracy, these models suffer from a lack of generalizability and can not be readily-deployed to new environments or operate in dynamic environments without retraining. In this paper, we propose meta-learning-based localization models to address the lack of generalizability that persists in conventionally trained DL-based localization models. Furthermore, since meta-learning algorithms require diverse datasets from several different scenarios, which can be hard to collect in the context of localization, we design and propose a new meta-learning algorithm, TB-MAML (Task Biased Model Agnostic Meta Learning), intended to further improve generalizability when the dataset is limited. Lastly, we evaluate the performance of TB-MAML-based localization against conventionally trained localization models and localization done using other meta-learnina algorithms.

To perform advanced surveillance, Unmanned Aerial Vehicles (UAVs) require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints. For this reason, we propose a novel A2-UAV framework to optimize the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel Application-Aware Task Planning Problem (A2-TPP) that takes into account (i) the relationship between deep neural network (DNN) accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions, (iii) the current energy/position of the UAVs to optimize routing, data pre-processing and target assignment for each UAV. We demonstrate A2-TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A2-UAV through real-world experiments with a testbed composed by four DJI Mavic Air 2 UAVs. We consider state-of-the-art image classification tasks with four different DNN models (i.e., DenseNet, ResNet152, ResNet50 and MobileNet-V2) and object detection tasks using YoloV4 trained on the ImageNet dataset. Results show that A2-UAV attains on average around 38% more accomplished tasks than the state of the art, with 400% more accomplished tasks when the number of targets increase significantly. To allow full reproducibility, we pledge to share datasets and code with the research community.

5G and beyond cellular networks (NextG) will support the continuous execution of resource-expensive edgeassisted deep learning (DL) tasks.To this end, Radio Access Network (RAN) resources will need to be carefully "sliced" to satisfy heterogeneous application requirements while minimizing RAN usage.Existing slicing frameworks treat each DL task as equal and inflexibly define the resources to assign to each task, which leads to sub-optimal performance.In this paper, we propose SEM-O-RAN, the first semantic and flexible slicing framework for NextG Open RANs.Our key intuition is that different DL classifiers can tolerate different levels of image compression, due to the semantic nature of the target classes.Therefore, compression can be semantically applied so that the networking load can be minimized.Moreover, flexibility allows SEM-O-RAN to consider multiple edge allocations leading to the same task-related performance, which significantly improves system-wide performance as more tasks can be allocated.First, we mathematically formulate the Semantic Flexible Edge Slicing Problem (SF-ESP), demonstrate that it is NP-hard, and provide an approximation algorithm to solve it efficiently.Then, we evaluate the performance of SEM-O-RAN through extensive numerical analysis with state-of-the-art multi-object detection (YOLOX) and image segmentation (BiSeNet V2), as well as realworld experiments on the Colosseum testbed.Our results show that SEM-O-RAN improves the number of allocated tasks by up to 169% with respect to the state of the art.

Deep learning-based (DL-based) models have shown to be powerful tools for wireless interference identification (WII). However, one of the key concerns toward using these models in practical systems is that they perform poorly when they are encountered with signals coming from new sources not previously observed during the training phase. In a real-world communication system, the interference identifier will frequently face new unknown signals due to the existence of many wireless transmitters. This renders the conventional DL-based models impractical as a WII tool unless they go through a new training phase. Retraining the model is not only inefficient, but it can also be not feasible in some cases (e.g., at end-user devices) as the training phase consumes time and resources and requires large amounts of data. We present a new approach for data-driven WII systems using meta- learning to address the lack of adaptability in conventional DL-based models to new (not previously seen) signals. We show that by using meta-learning, we are able to identify signals coming from not previously observed technologies and frequencies using just a handful of new samples, a task that is not generally possible with conventional DL models. Finally, we analyze and compare the performance of the presented meta-learning model in multiple different settings using raw I/Q samples and Fast Fourier Transform of I/Q samples. Based on our experiments, we show that the proposed meta-learning scheme outperforms the conventional deep learning models for WII when there are just a few samples available for training1.

Thanks to the ubiquitousness of Wi-Fi access points and devices, Wi-Fi sensing enables transformative applications in remote health care, home/office security, and surveillance, just to name a few. Existing work has explored the usage of machine learning on channel state information (CSI) computed from Wi-Fi packets to classify events of interest. However, most of these algorithms require a significant amount of data collection, as well as extensive computational power for additional CSI feature extraction. Moreover, the majority of these models suffer from poor accuracy when tested in a new/untrained environment. In this paper, we propose ReWiS, a novel framework for robust and environment-independent Wi-Fi sensing. The key innovation of ReWiS is to leverage few-shot learning (FSL) as the inference engine, which (i) reduces the need for extensive data collection and application-specific feature extraction; (ii) can rapidly generalize to new environments by leveraging only a few new samples. Moreover, ReWiS leverages multi-antenna, multi-receiver diversity, as well as fine-grained frequency resolution, to improve the overall robustness of the algorithms. Finally, we propose a technique based on singular value decomposition (SVD) to make the FSL input constant irrespective of the number of receive antennas. We prototype the ReWiS using off-the-shelf Wi-Fi equipment and showcase its performance by considering a compelling use case of human activity recognition. Thus, we perform an extensive data collection campaign in three different propagation environments with two human subjects. We evaluate the impact of each diversity component on the performance and compare ReWiS with an existing convolutional neural network (CNN)-based approach. Experimental results show that ReWiS improves the performance by about 40% with respect to existing single-antenna low-resolution approaches. Moreover, when compared to a CNN-based approach, ReWiS shows a 35% more accuracy and less than 10% drop in accuracy when tested in different environments, while the CNN drops by more than 45%. To allow reproducibility of our results and to address the current dearth of Wi-Fi sensing datasets, we pledge to release our 60 GB dataset and the entire code repository to the community.

Dynamic spectrum access (DSA) has the great potential to alleviate spectrum shortage and promote network capacity. However, two fundamental technical issues have to be addressed, namely, interference coordination between DSA users and interference suppression for primary users (PUs). These two issues are very challenging since generally there is no powerful infrastructures in DSA networks to support centralized control. As a result, DSA users have to perform spectrum management individually, including spectrum access and power allocation, without accurate channel state information and centralized control. In this article, a novel spectrum management framework is proposed, in which Q-learning, a type of reinforcement learning, is utilized to enable DSA users to carry out effective spectrum management individually and intelligently. For more efficient process, neural networks (NNs) are employed to implement Q-learning processes, so-called deep Q-network (DQN). Furthermore, we also investigate the optimal way to construct DQN considering both the performance of wireless communications and the difficulty of NN training. Finally, extensive simulation studies are conducted to demonstrate the effectiveness of the proposed spectrum management framework.

The 3rd Generation Partnership Project (3GPP) is actively designing New Radio Vehicle-to-Everything (NR V2X)-a 5G NR-based technology for V2X communications. NR V2X, along with its predecessor Cellular V2X (C-V2X), is set to enable low-latency and high-reliability communications in high-speed and dense vehicular environments. A key reliability-enhancing mechanism that is available in C-V2X and is likely to be re-used in NR V2X is packet re-transmissions. In this paper, using a systematic and extensive simulation study, we investigate the impact of this feature on the system performance of C-V2X. We show that statically configuring vehicles to always disable or enable packet re-transmissions either fails to extract the full potential of this feature or leads to performance degradation due to increased channel congestion. Motivated by this, we propose and evaluate Channel Congestion-based Re-transmission Control (C2RC), which, based on the observed channel congestion, allows vehicles to autonomously decide whether or not to use packet re-transmissions without any role of the cellular infrastructure. Using our proposed mechanism, C-V2X-capable vehicles can boost their performance in lightly-loaded environments, while not compromising on performance in denser conditions.

Current studies that apply reinforcement learning (RL) to dynamic spectrum access (DSA) problems in wireless communications systems mainly focus on model-free RL (MFRL). However, in practice, MFRL requires a large number of samples to achieve good performance making it impractical in real-time applications such as DSA. Combining model-free and model-based RL can potentially reduce the sample complexity while achieving a similar level of performance as MFRL as long as the learned model is accurate enough. However, in a complex environment, the learned model is never perfect. In this article, we combine model-free and model-based RL, and introduce an algorithm that can work with an imperfectly learned model to accelerate the MFRL. Results show our algorithm achieves higher sample efficiency than the standard MFRL algorithm and the Dyna algorithm (a standard algorithm integrating model-based RL and MFRL) with much lower computation complexity than the Dyna algorithm. For the extreme case where the learned model is highly inaccurate, the Dyna algorithm performs even worse than the MFRL algorithm while our algorithm can still outperform the MFRL algorithm.

Spatial spectrum sensing (SSS) enables mobile devices to sense the spatial spectrum holes and reuse the scarce spectrum opportunistically. In this paper, we model and analyze the SSS in uplink two-tier user-centric deployed heterogeneous networks (HetNets) where secondary users (SUs) sense the spectrum holes of cellular users. In the two-tier user-centric deployed HetNets, small cell base stations (SBSs) are deployed in hotspots with high user density, and macro base stations (MBSs) are deployed uniformly. Based on the semi-static power control mechanism, the average transmit power of cellular users associated with MBS and SBS are derived, respectively. Furthermore, the spatial false alarm probability and the spatial miss detection probability of a typical SU are obtained, respectively. Moreover, we characterize the coverage probability and the area spectral efficiency (ASE) of SU and cellular networks. The SUs' optimal SSS radius is obtained to maximize the ASE of the entire network while guaranteeing the ASE of cellular networks above a certain threshold. Simulation results show that when the density of SUs is small, a decrease in SUs' SSS radius reduces the coverage probability of SUs. However, it improves the ASE of SUs networks, although the inter-SU interference increases.

In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide a practical solution for situations where the UAV network may need external spectrum when dealing with congested spectrum or need to change its operational frequency due to security threats. Here we study a scenario where the UAV network performs a remote sensing mission. In this model, the UAVs are categorized to two clusters of relaying and sensing UAVs. The relay UAVs provide a relaying service for a licensed network to obtain spectrum access for the rest of UAVs that perform the sensing task. We develop a distributed mechanism in which the UAVs locally decide whether they need to participate in relaying or sensing considering the fact that communications among UAVs may not be feasible or reliable. The UAVs learn the optimal task allocation using a distributed reinforcement learning algorithm. Convergence of the algorithm is discussed and simulation results are presented for different scenarios to verify the convergence.

Power control plays an important role in improving the system throughput in communication system since co-channel interference is a major limitation to the system throughput. The power control problem of maximizing the system throughput in the multiuser and multichannel communication system is a highly complicated nonconvex problem since user are interfered with one another if operating in the same wireless channel. We reformulate the nonconvex objective function of this problem as a difference of two convex functions, which is called DC (difference of convex function) programming. To reduce the computation complexity in the high dimensional space, we introduce an alternative power allocation scheme to search in the low dimensional space, where each user updates its power sequentially. A global optimal power allocation is found by utilizing the branch-and- bound algorithm for each user while taking other users' power allocation as constant value. Furthermore, we incorporate each user's maximum power and minimum data rate constraint into the optimization framework. We found that the minimum data rate constraint of each user can be turned into multiple linear inequalities and then be added to the DC programming optimization framework. The simulation results show that our introduced method achieves the highest sum data rate compared to the state-of-the-art methods, including iterative water filling and geometric programming.

This paper develops a novel framework for the modeling and analysis of spatial spectrum sensing (SSS) for device-to-device (D2D) communications in uplink two- tier user-centric deployed heterogeneous networks (HetNets), where small cell base stations (SBSs) are deployed in the places with high user density termed hotspots introduced by 3GPP. We study the average transmit power of uplink users, the probability of spatial false alarm and the probability of spatial miss detection of a typical D2D transmitter (D2D-Tx) during SSS. Based on the results, we further characterize the coverage probability of a typical D2D user and the area spectral efficiency (ASE) of D2D networks. Simulation results verify our analysis and demonstrate the advantages of SSS-based D2D communications in future wireless networks.

In this paper, we study the problem of spectrum scarcity in a network of unmanned aerial vehicles (UAVs) during mission-critical applications such as disaster monitoring and public safety missions, where the pre-allocated spectrum is not sufficient to offer a high data transmission rate for real-time video-streaming. In such scenarios, the UAV network can lease part of the spectrum of a terrestrial licensed network in exchange for providing relaying service. In order to optimize the performance of the UAV network and prolong its lifetime, some of the UAVs will function as a relay for the primary network while the rest of the UAVs carry out their sensing tasks. Here, we propose a team reinforcement learning algorithm performed by the UAV's controller unit to determine the optimum allocation of sensing and relaying tasks among the UAVs as well as their relocation strategy at each time. We analyze the convergence of our algorithm and present simulation results to evaluate the system throughput in different scenarios.

While massive multiple-input multiple-output system (MIMO) promises to provide substantial increase in spectral efficiency-per- cell, due to high dimensionality, estimating its channel is considered as one of the major challenges towards extracting all the benefits of such large antenna systems. In this paper, based on parametric channel modeling, we present a direction of arrival (DoA) estimation method for multi-cell multi-user 3D massive-MIMO/Full Dimension (FD-MIMO) orthogonal frequency division multiplexing (OFDM) system using estimation of signal parameter via rotational invariance technique (ESPRIT). Furthermore, we analytically characterize the performance of ESPRIT-based DoA estimation in multiuser scenario, and investigate how pilot contamination, intra-cell, and inter-cell interference affect DoA estimation performance.

Reservoir Computing (RC) is a class of machine learning approaches that is suitable for prediction tasks with low computational complexity. In this paper, an RC-based symbol detection for MIMO- OFDM systems is presented where RC is realized through the echo state network (ESN). Detailed energy-efficiency analysis is conducted to characterize the energy-efficiency of the introduced symbol detector. To be specific, the transmit power, the circuit power, and the computational power at both transmitter and receiver are jointly considered for the energy-efficiency analysis. The overall system energy-efficiency as well as the receiver energy-efficiency of the introduced RC-based symbol detector are compared with those of the popular linear minimum mean squared error (LMMSE)-based approach. Simulation and numerical results show that the RC-based symbol detector is a ``green'' solution compared to the traditional LMMSE-based method with lower energy consumption per information bit.

Mobile health (m-Health) is one potential application of cyber-physical systems, where biomedical sensors and mobile devices interact tightly together to transmit medical data to an m-Health server. In this paper, we consider a three-tier hierarchical m-Health system: 1) the sensor network tier capturing vital signals; 2) the mobile computing network tier processing and routing the sensed data to a fixed remote location; and 3) the back-end network tier processing and analyzing the sensed medical data along with patient's medical history. Based on this architecture, a physical layer security scheme for the second tier is developed and network performance of the introduced scheme is analyzed under different metrics using stochastic geometry. To be specific, secure transmission range and average end-to-end delay are analyzed for two different strategies: the mobile device transmits: 1) to the nearest neighbor and 2) to the furthest neighbor. Furthermore, we consider the cases of full knowledge on eavesdroppers' locations and when such information is unavailable. Results show that transmitting to the nearest neighbor achieves the highest secure transmission distance with the lowest mean delay when full information on eavesdroppers is available.

This paper investigates methods to achieve high-rate data transmission through metallic barriers using ultrasound. Multiple-input-multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) is employed in conjunction with interference mitigation techniques to reduce throughput-limiting crosstalk. Several crosstalk mitigation strategies are investigated and their theoretical and practical bit-loaded data rates are determined for the general case of A transmitters and A receivers (A×A MIMO). A physical MIMO acoustic-electric channel array is formed using a 40 mm (1.575 in) thick steel barrier with seven pairs of 4 MHz nominal resonant frequency piezoelectric disk transducers, each with 10 mm (0.394 in) diameter. To investigate the effects of crosstalk, the transducers are closely spaced, and each transmitter-receiver pair is coaxially aligned on opposing sides of the metallic barrier. It is shown that, with the use of crosstalk mitigation techniques, the aggregate multichannel theoretical capacity performance scales linearly with the number of channels. This paper also investigates the use of novel power allocation techniques in a MIMO-OFDM acoustic-electric channel, which show significant throughput performance gains over conventional bit-loading and greedy bit-filling techniques. Finally, this paper presents a study on the effects of transducer misalignment on the multichannel theoretical capacity and achievable data transmission rates using bit-loading techniques.

Energy efficiency is one of the major challenges of 5G networks. As data rates are expected to increase by 1000x from 4G to 5G, energy efficiency will need to improve by about the same amount. Recently, energy harvesting techniques have attracted lots of attention from the scientific community, due to their ability to increase network lifetime. More specific, energy harvesting from ambient radio frequency (RF) signals is of special importance, especially with the recent RF circuit advancements. In this paper, we consider a device-to-device (D2D) communication in underlay cellular networks, where D2D users reuse the spectrum occupied by cellular users. We introduce the concept of spatial RF energy harvesting, where D2D users harvest RF power from uplink cellular transmissions, if it exceeds a predesigned threshold, in a spatial region. Using tools from stochastic geometry, we obtain a closed form expression for the probability of activating RF power conversion circuit by making full use of spatial locations of ambient RF signals. Subsequently, we study the impact of RF energy harvesting region radius to harvest sufficient power on the signal-to-interference (SIR) ratio of D2D network. Simulation results provide insights for the required advancements to design highly efficient RF harvesting circuits.

With the explosive growth of mobile traffic demand, device-to-device (D2D) communication offers a promising approach to reduce cellular congestion by offloading users' traffic to D2D network. In this paper, we consider a relay- assisted D2D communication in underlay cellular networks, where D2D users access a proportion of the spectrum occupied by cellular users. This poses two fundamental issues. The first issue is related to protecting the cellular transmissions by reducing the spectrum partition factor, which measures the fraction of spectrum available to D2D users. The second issue is that as we limit the spectrum available to D2D users, less D2D transmissions can be supported, which in turn increases the distances between them. To address these issues, we allow a relay user to assist D2D communications in order to achieve higher D2D spectral efficiency without affecting cellular spectral efficiency. In fact, results show significant improvements in spectral efficiency not only to D2D, but to the whole network, especially in a dense D2D environment.

In this paper, we consider device-to-device (D2D) communication underlaying cellular networks, where D2D users harvest radio frequency (RF) power from uplink cellular transmissions. This paper addresses two important issues for energy harvesting-based D2D cellular networks. The first is how the energy harvested from cellular ambient RF signals affects the D2D spectral efficiency. Using tools from stochastic geometry, we investigate this issue by first characterizing the transmission probability of D2D transmitter as the probability of having enough battery power, and then obtaining analytic expressions of the spectral efficiency for D2D and cellular networks. The second issue is related to the cellular spectral efficiency: the more RF energy D2D users can harvest, the higher their transmission probability becomes, which leads to generating more interference in the cellular network. We carry out simulations to understand this impact of RF energy harvesting on the spectral efficiency of both D2D and cellular networks. Results show promising improvement to the whole network, in terms of the weighted spectral efficiency, when employing RF harvesting technology and when there are enough available channels in the network.

To suppress the co-channel interference in a multi-cell multi-user multiple-input-multiple-output downlink cellular network, a novel interference alignment transceiver beam-forming design along with a low complexity iterative coordinated beam-forming scheme is introduced. While the latter combats the intra-cell interference, the former is utilized to mitigate the inter-cell interference. The proposed schemes consider the codebook-based feedback, which is adopted in the LTE/LTE-advanced systems. Optimal downlink user-specific and cell-specific beam-forming matrices are characterized to maximize the lower bound of expected signal-to-leakage-plus-noise ratio and to minimize the residual inter-cell interference, respectively. Moreover, closed-form expressions for these beam-forming matrices under limited channel state information feedback and in the presence of the quantization error are identified. Simulations are conducted to investigate the performance of the proposed strategy. The results indicate that our scheme can significantly improve the average spectral-efficiency of the underlying network when compared with existing ones where the quantization error is neglected. Furthermore, for a fixed payload size of the codebook, unlike zero-forcing beam-forming in which the sum throughput is bounded as the signal-to-noise ratio (SNR) increases, in our scheme, the performance gap between rank 2 feedback and perfect feedback remains approximately constant as SNR increases.

This paper explores the use of multiple communication channels to transmit data at high rates, without physical penetrations, through thick metallic barriers using ultrasound. Two parallel acoustic-electric channels are formed in close proximity utilizing two pairs of coaxially aligned piezoelectric transducers mounted on, and acoustically coupled to, opposing sides of a metal wall. Each channel employs orthogonal frequency division multiplexing (OFDM) which can achieve a high spectral efficiency in frequency selective channels. The two-transmitter-two-receiver MIMO configuration is studied and analytical expressions of channel capacity are determined for the raw channels as well as for several co-channel interference cancellation techniques and they are verified using a Monte-Carlo simulation. It is shown that excessive crosstalk between the channels can lead to marginal increases or decreases in capacity over the single channel when no interference suppression is used. Several interference cancellation structures, including zero forcing, eigenmode transmission, and minimum mean-square error (MMSE) are investigated to mitigate this effect. It is shown that their aggregate capacity may approximately double that of either channel used independently. It is also determined that, in a relatively static MIMO acoustic-electric channel, a minimal complexity adaptive approach such as the Least Mean Squares (LMS) algorithm may be used while achieving a similar capacity to more complex structures.