Error-bounded lossy compressors have been developed for years to reduce the vast volumes of scientific data generated by high-performance computing (HPC) applications and advanced scientific instruments. While these compressors have been effective in mitigating the challenges posed by massive datasets, a significant gap remains in our understanding of the fundamental compressibility limits of scientific data–an issue that critically impacts the sustainable adoption and development of efficient lossy compression techniques in practice. Classical rate-distortion theory, established by Shannon, assumes stationary 1D sources with unconstrained coding–assumptions that do not hold for scientific datasets compressed under the tiling constraints imposed by modern parallel lossy compressors. This paper addresses this gap by developing a novel framework that characterizes compressibility limits for scientific datasets under realistic tiling constraints. The contribution is two-fold. First, we establish a tile-aware, finite-blocklength extension of rate–distortion theory that advances classical 1D asymptotic formulations into a rigorous framework for piecewise 2D Gaussian random fields. To our knowledge, this is the first framework to rigorously characterize lossy compressibility limits for scientific datasets and compressor, moving beyond classical asymptotic 1D source models. Second, we conduct a comprehensive validation of the proposed modeling framework using state-of-the-art error-bounded lossy compressors and diverse real-world HPC datasets, demonstrating that our theory accurately predicts rate-distortion trends and provides actionable insights for compressor design.

Motivated by International Mobile Telecommunications (IMT)-2030, sixth generation (6G) mobile networks in 3GPP (third-generation partner project) commenced with a workshop in March 2025, attracting more than 200 contributions and 700 in-person attendances. While the details of 6G study and the eventual 6G specifications in 3GPP are yet to be developed, there is strong motivation in 3GPP to focus on the fundamental values that 6G may bring, most notably in terms of improving user experience and the overall network operation, particularly with respect to reducing the total cost of ownership (TCO). It is evident that there is a strong need to streamline and simplify the 6G specifications for efficient standardization, implementation, and commercial deployments. For the services also accommodated by the fifth generation (5G) networks, 6G is expected to provide meaningful enhancements. That is, instead of simply pushing for even higher envelopes such as peak data rates, more emphasis will be on improved coverage (especially at the cell edge and for certain data rates or services), and energy efficiency (for both end devices and networks) using the existing and new spectrum. Moreover, 6G is expected to support new services, with artificial intelligence (AI) and sensing as two primary examples. In this article, we provide a tutorial on the key technologies driving fundamental enhancements for the standardization of 6G and beyond, covering the necessary co-existence between 5G and 6G for smooth migration, AI-native radio access network (RAN), multiple-input-multiple-out (MIMO), sustainable operation with increased energy efficiency, coverage enhancements involving both terrestrial and non-terrestrial networks (NTN), integrated sensing and communication (ISAC), diverse device types including the low-end back-scattering based ambient internet of things (IoT) devices, and native support of NTN. We conclude this article by pointing out several key challenges and opportunities towards standardization of 6G and beyond.

Recurrent Neural Networks (RNNs) are powerful models for sequential tasks, but their training can be computationally intensive. On the other hand, Echo State Networks (ESNs), a specific RNN architecture, simplify this by configuring a fixed, random reservoir of recurrent weights. Instead of setting random weights as in the ESN case, in this work, we investigate the recurrent weight configuration problem of the fully-fledged RNN for the task of Linear Time-Invariant (LTI) system approximation. Our investigation focuses on a specific RNN architecture with a network of recurrent neurons limited to self-loops and linear activation. We demonstrate that as the recurrent weights of this RNN are trained on large datasets, their distribution converges to a near-identical match of an optimal distribution that can be analytically derived using the available domain knowledge of the LTI system. This insight establishes that domain-informed weight configuration is a highly efficient alternative to data-driven training. Building upon this, we propose a novel deterministic algorithm to set the recurrent weights, which significantly improves approximation accuracy. Numerical results show our domain-informed RNN weight configuration achieves up to a four-order-of-magnitude performance gain over conventional ESNs.

Spectrum sharing is a key enabler for 5G/6G. We model decentralized spectrum access as a noncooperative, stochastic, imperfect-information extensive-form game and show that running standard Counterfactual Regret Minimization (CFR) independently at each user can exhibitpersistent cyclingrather than converging to a stable equilibrium. We provide a constructive multi-player example and a mapping analysis explaining why the induced regret-matching update need not be contractive in general multi-player, non-zero-sum settings. To address this, we propose CFR-M2, a lightweightregret-mixingscheme that periodically aggregates a small amount ofcumulative regret(not policy parameters) across users at a configurable communication interval. Our analysis shows that inserting an infrequent regret-consensus step over any connected communication graph preserves no-(counterfactual)-regret whilecontracting inter-agent disagreement in regrets. Consequently, the C´esaro-averaged play converges to an ε–extensive-form coarse correlated equilibrium (EFCCE) with ε=Õ(1/√T)+O(RMAXTCOMM/(1-ρ)T) where ρ is the second-largest eigenvalue modulus of the mixing matrix. Under additional structure (e.g., a strongly monotone pseudo-gradient or a strongly convex potential), the EFCCE collapses to a unique Nash equilibrium. Experiments on synthetic networks and a 5G Dynamic Spectrum Sharing (DSS) scenario (WINNER II channel model and practical parameter settings) show that CFR-M2improves convergence stability and system reward over CFR and several learning/game-theoretic baselines, while requiring onlyinfrequentcommunications.

This work presents an energy-efficient reinforcement learning (RL) solution based on Neuromorphic Computing (NC) to enable opportunistic spectrum access in partially observable wireless environments. To improve the energy efficiency of the underlying spectrum access strategy, we explore Neuromorphic Computing and adopt spiking neural networks. Additionally, the time-dependent aspect of the problem and the necessity for sample efficiency drive us to liquid state machines, a variant of reservoir computing. Nevertheless, a priori hyperparameter optimization of the spiking reservoir is essential for handling state- and time-varying inputs in RL agents; yet, this can undermine model robustness and impede deployment. In response, we examine homeostatic regulation for self-modulating the small-world reservoir’s dynamics, thereby maintaining desired near-chaotic behavior throughout operation. The RL model for opportunistic spectrum access is evaluated under both dynamic spectrum access (DSA), where agents identify temporal spectrum holes for transmission, and spatiotemporal spectrum access (SSA), where agents also aim to minimize coverage overspill without coordination or sharing location data. Numerical analysis demonstrates that the proposed model outperforms existing learning models in the literature for both DSA and SSA, while significantly reducing power consumption.

In the physical layer (PHY) of modern cellular systems, information is transmitted as a sequence of resource blocks (RBs) across various domains with each resource block limited to a certain time and frequency duration. In the PHY of 4G/5G systems, data is transmitted in the unit of transport block (TB) across a fixed number of physical RBs based on resource allocation decisions. Using sharp band-limiting in the frequency domain can provide good separation between different resource allocations without wasting resources in guard bands. However, using sharp filters comes at the cost of elongating the overall system impulse response which can accentuate inter-symbol interference (ISI). In a multi-user setup, such as in Machine Type Communication (MTC), different users are allocated resources across time and frequency, and operate at different power levels. If strict band-limiting separation is used, high power user signals can leak in time into low power user allocations. The ISI extent, i.e., the number of neighboring symbols that contribute to the interference, depends both on the channel delay spread and the spectral concentration properties of the signaling waveforms. We hypothesize that using a precoder that effectively transforms an OFDM waveform basis into a basis comprised of discrete prolate spheroidal sequences (DPSS) can minimize the ISI extent when strictly confined frequency allocations are used. Analytical expressions for upper bounds on ISI are derived. In addition, simulation results support our hypothesis.

Millimeter-wave (mmWave) technology represents a promising avenue in integrated sensing and communication (ISAC), leveraging wide bandwidth to accommodate growing demands for high data-rate communication and high-resolution radar sensing. However, in non-line-of-sight (NLOS) scenarios, mmWave signals suffer from severe attenuation, and integrating precise radar sensing into bandwidth-limited communication systems remains an open problem. To address this, we propose an NLOS-ISAC system that jointly provides accurate target positioning and robust communication. Our approach synthesizes many narrowband signals into a virtual wideband radar via stepped-frequency techniques, eliminating the need for additional hardware. By fusing time-of-flight (ToF) measurements of multipath reflections with environmental maps, the system achieves submeter positioning accuracy for NLOS targets. Building on these position estimates, we then employ position-aware beamforming to significantly enhance the NLOS communication throughput. Extensive experimental results demonstrate that the proposed NLOS-ISAC system reliably achieves high-accuracy localization while improving data rates in challenging NLOS environments.

Open Radio Access Network (O-RAN) architectures enhance flexibility for 6G and NextG networks. However, it also brings significant challenges in O-RAN testing with evaluating abundant, high-dimensional key performance indicators (KPIs). In this paper, we introduce a novel two-stage framework to learn temporally-aware low-dimensional representations of O-RAN testing KPIs. To be specific, stage one employs an information-theoretic H-score to train a hybrid self-attentive transformer and echo state network (ESN) reservoir, called Transformer-ESN, capturing temporal dynamics and producing task-aligned 8-dimensional embeddings. Stage two evaluates these embeddings by training a lightweight multilayer perceptron (MLP) predictor exclusively on them for key target KPIs such as reference signal received quality (RSRQ) and spectral efficiency. Using real-world O-RAN testbed data (video streaming with interference), our approach demonstrates a significant advantage specifically when training samples are very limited. In this scenario, the low-dimensional representations learned from the Transformer-ESN yield mean square error (MSE) reductions of up to 41.9% for RSRQ and 29.9% for spectral efficiency compared to predictions from the original high-dimensional data. The framework exhibits high efficiency for O-RAN testing, significantly reducing testing complexities for O-RAN systems.

Mobile distributed MIMO (MD-MIMO) is an innovative extension of distributed MIMO, where mobile radio nodes with antenna arrays connect wirelessly to a base station. To explore the potential of these systems, we developed a software-defined radio (SDR) testbed and created a prototype implementation. This testbed serves as a platform for research and prototyping of MD-MIMO systems.

The exponential growth of data traffic beyond the 5G era necessitates improved resource utilization for the integrated terrestrial and non-terrestrial networks (ITNTN). In this work, we consider a multi-user multiple input multiple output (MU-MIMO)-empowered 5G ITNTN network consisting of terrestrial 5G and multi-beam geostationary earth orbit (GEO) satellite-based gNBs and develop an interference management framework that allows multiple users to receive downlink data over the same resource blocks (RB) simultaneously. Our developed framework first employs a traffic offloading algorithm by leveraging the reference signal received power (RSRP) and celledge width criteria to offload traffic from terrestrial to NTN networks. Subsequently, we formulate the resultant interference management as a joint power allocation and user-RB scheduling optimization problem to maximize the network’s spectral efficiency. Since the joint optimization problem is NP-hard and computationally intractable, a fractional programming-based solution is developed to obtain sub-optimal yet efficient transmit power allocation and user scheduling at terrestrial and satellite gNBs. A realistic ITNTN simulator is developed for performance evaluation by considering 3GPP channel models, antenna gains, and 5G RB numerology in rural terrestrial-GEO coexistence scenarios. Extensive simulation results confirm the efficacy of the proposed framework in managing interference and improving resource utilization at 5G ITNTN networks.

Network slicing plays a critical role in enabling multiple virtualized and independent network services to be created on top of a common physical network infrastructure. In this paper, we introduce a deep reinforcement learning (DRL)-based radio resource management (RRM) solution for radio access network (RAN) slicing under service-level agreement (SLA) guarantees. The objective of this solution is to minimize the SLA violation. Our method is designed with a two-level scheduling structure that works seamlessly under Open Radio Access Network (O-RAN) architecture. Specifically, at an upper level, a DRL-based inter-slice scheduler is working on a coarse time granularity to allocate resources to network slices. And at a lower level, an existing intra-slice scheduler such as proportional fair (PF) is working on a fine time granularity to allocate slice dedicated resources to slice users. This setting makes our solution O-RAN compliant and ready to be deployed as an ‘xApp’ on the RAN Intelligent Controller (RIC). For performance evaluation and proof of concept purposes, we develop two platforms, one industry-level simulator and one O-RAN compliant testbed; evaluation on both platforms demonstrates our solution’s superior performance over conventional methods.

This paper focuses on advancing reinforcement learning for challenging environments characterized by partial observability and non-stationarity, such as dynamic spectrum access (DSA). In the literature, the Deep Recurrent Q-Network was introduced to capitalize on the inherent temporal correlations present in DSA. Nevertheless, its practicality is still questionable due to sample inefficiency and slow convergence. We introduce Dyna-ESN, leveraging both model-based and model-free methods by employing Reservoir Computing for generative modeling. Specifically, we utilize Echo State Networks (ESNs) to synthesize samples for enhancing the sample efficiency of a model-free Deep Echo State Q-network, enabling effective operation of agent given limited genuine relevant samples obtained through interaction with environment. To mitigate potential adverse effects of synthetic samples, an evaluation algorithm guides the sample selection process, ensuring reliability. A sample augmentation technique is also introduced to allow agents to collect adequate samples despite controlling the sensing rate and duration of secondary transmissions. Our analysis explores trade-offs between data evaluation and sample efficiency, as well as the bias-variance trade-off of the model, identifying optimal design parameters. Evaluating the performance of Dyna-ESN in DSA scenarios demonstrates its performance benefits over existing methods, paving the way for more efficient and effective techniques in complex dynamic environments.

Deep learning is making a profound impact in the physical layer of wireless communications. Despite exhibiting outstanding empirical performance in tasks such as MIMO receive processing, the reasons behind the demonstrated superior performance improvement remain largely unclear. In this work, we advance the field of Explainable AI (xAI) in the physical layer of wireless communications utilizing signal processing principles. Specifically, we focus on the task of MIMO-OFDM receive processing (e.g., symbol detection) using reservoir computing (RC), a framework within recurrent neural networks (RNNs), which outperforms both conventional and other learning-based MIMO detectors. Our analysis provides a signal processing-based, first-principles understanding of the corresponding operation of the RC. Building on this fundamental understanding, we are able to systematically incorporate the domain knowledge of wireless systems (e.g., channel statistics) into the design of the underlying RNN by directly configuring the untrained RNN weights for MIMO-OFDM symbol detection. The introduced RNN weight configuration has been validated through extensive simulations demonstrating significant performance improvements. This establishes a foundation for explainable RC-based architectures in MIMO-OFDM receive processing and provides a roadmap for incorporating domain knowledge into the design of neural networks for NextG systems.

Physical layer key generation techniques aim to extract secret keys from the information contained in wireless channels. However, existing key generation schemes often rely on time-frequency domain waveforms for channel estimation, which not only makes secret extraction less reliable but may also compromise the confidentiality of the extracted secret information. This paper presents physical layer key generation methods relying on the Orthogonal Time Frequency and Space (OTFS) waveform. We present analysis showing that the delay-Doppler domain channel estimates obtained using OTFS are conducive to more secure and reliable secret extraction than time-frequency domain channel estimates obtained using the prevalent Orthogonal Frequency Division Multiplexing (OFDM). This analysis provides theoretical guarantees under certain simple assumptions. We then relax those assumptions in extensive time-division duplex (TDD) simulations and show that under realistic settings, OTFS offers the expected benefits to reliability and security. Our simulations show that the introduced OTFS schemes can reliably extract secret keys from channel estimates in scenarios where time-frequency domain methods deteriorate.

In this paper, we introduce StructNet-CE, a novel real-time online learning framework for MIMO-OFDM channel estimation, which only utilizes over-the-air (OTA) reference signals (RS) for online channel estimation on a slot basis without assuming the availability of any channel knowledge. To achieve real-time and efficient channel learning, the design of StructNet-CE leverages the structural information inherent in the MIMO-OFDM system: the repetitive structure of modulation constellation and the invariant property of symbol classification to inter-stream interference. The embedded structural information enables StructNet-CE to conduct channel estimation through the underlying symbol detection task and accurately learn MIMO channels through the limited RS with the scattered RS configuration adopted in 5G/5G-Advanced slots. Numerical experiments demonstrate that the channel estimation performance is significantly improved by incorporating the structural knowledge, achieving a mean square error (MSE) reduction ranging from around 44.41% to 95.54% compared to existing methods. Furthermore, StructNet-CE is compatible and readily applicable to current and future wireless networks, demonstrating the effectiveness, importance, and relevance of combining machine learning techniques with domain knowledge for wireless systems.

Multi-user interference and adversarial jamming are critical issues affecting the ultra-high capacity and reliability of next-generation (NextG) wireless networks. Conventional interference mitigation techniques usually rely on model-based analysis, which cannot fully address new challenges in NextG systems. Reservoir computing (RC), a new brain-inspired machine learning paradigm, can outperform conventional methods for various tasks in wireless systems due to its efficient training algorithm and minimum requirements for training data. However, its resilience in interference scenarios has yet to be thoroughly investigated. This paper explores the performance improvement for MIMO-OFDM systems under smart jamming attacks and introduces a new resilient RC architecture (ResRC) for iterative interference detection and mitigation. The performance results from Monte Carlo simulations show that ResRC can effectively mitigate interference and improve system reliability and capacity. A software-defined radio prototype receiver further verifies the ResRC architecture design in various real-world scenarios.

Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS system, where only the limited over-the-air (OTA) pilot symbols are utilized for training. However, the previous RC-based approach does not design the RC architecture based on the properties of the OTFS system to fully unlock the potential of RC. This paper introduces a novel two-dimensional RC (2D-RC) approach for online symbol detection on a subframe basis in the OTFS system. The 2D-RC is designed to have a two-dimensional (2D) filtering structure to equalize the 2D circular channel effect in the delay-Doppler (DD) domain of the OTFS system. With the introduced architecture, the 2D-RC can operate in the DD domain with only a single neural network, unlike our previous work which requires multiple RCs to track channel variations in the time domain. Experimental results demonstrate the advantages of the 2D-RC approach over the previous RC-based approach and the compared model-based methods across different modulation orders.

As the development of 6G technologies progresses, there is a focused effort by international bodies and regulatory agencies to enhance worldwide connectivity, paying special attention to the needs of high-mobility users and networks, such as Mobile Ad-Hoc Networks (MANETs) and Vehicular Ad-Hoc Networks(VANETS) . These advanced systems face significant challenges, particularly the increased demand for rapid channel state information (CSI) feedback due to the fast-changing nature of channel conditions. In environments where traditional time-frequency domain approaches struggle, the adoption of delay-Doppler domain representations, like those used in OTFS (Orthogonal Time Frequency Space) modulation, shows promise for improved stability in mobile scenarios. This paper introduces a novel expression for predicting OTFS channel variations over time and proposes an efficient technique for dynamically updating MIMO (Multiple Input Multiple Output) precoders, enhancing the utility of outdated CSI while minimized signaling overhead and computational complexity. Subsequently, we conduct Monte Carlo simulations to evaluate the performance of OFDM and OTFS MIMO systems within high-mobility environments. These simulations aim to rigorously assess the robustness and efficiency of both modulation techniques under scenarios characterized by rapid user movement and fluctuating channel conditions.

The paper proposes a new architecture for Distributed MIMO (D-MIMO) in which the base station (BS) jointly transmits with wireless mobile nodes to serve users (UEs) within a cell for 6G communication systems. The novelty of the architecture lies in the wireless mobile nodes participating in joint D-MIMO transmission with the BS (referred to as D-MIMO nodes), which are themselves users on the network. The D-MIMO nodes establish wireless connections with the BS, are generally near the BS, and ideally benefit from higher SNR links and better connections with edge-located UEs. These D-MIMO nodes can be existing handset UEs, Unmanned Aerial Vehicles (UAVs), or Vehicular UEs. Since the D-MIMO nodes are users sharing the access channel, the proposed architecture operates in two phases. First, the BS communicates with the D-MIMO nodes to forward data for the joint transmission, and then the BS and D-MIMO nodes jointly serve the UEs through coherent D-MIMO operation. Capacity analysis of this architecture is studied based on realistic 3GPP channel models, and the paper demonstrates that despite the two-phase operation, the proposed architecture enhances the system’s capacity compared to the baseline where the BS communicates directly with the UEs.

The increased number of connected devices and diverse quality of service demands in cellular networks present formidable challenges for efficient traffic management. In this paper, we present a handover control approach to tackle these challenges in 5G networks, integrating the Proximal Policy Optimization (PPO) framework with Reservoir Computing (RC). Our approach, aligned with the O-RAN architecture, offers a near-real-time solution for intelligent handover management, improving cell performance, and enhancing user experience. This renders our solution O-RAN compliant and deployable as an ’xApp’ on the RAN Intelligent Controller (RIC). The introduced algorithm features a sequential state design including user-specific and cell-specific metrics. Harnessing the power of reservoir computing, our solution captures dynamic changes in state sequences while reducing training overhead and enhancing online training efficiency. The efficacy of our algorithm is demonstrated through evaluations on the ns3 platform, proving its superiority in system-level simulations. The experimental results affirm the effectiveness of the introduced algorithm in addressing the dynamic challenges of handover in cellular networks.

Mobile and Vehicular Ad-hoc Networks (MANETs and VANETs) represent critical types of ad-hoc networks with diverse applications. Establishing a resilient wireless network for mobile devices in areas lacking traditional infrastructure, such as 5G base stations, presents significant potential for research fields like tactical military networks or commercial vehicular networks. In this study, leveraging the capabilities of the Common Open Research Emulator (CORE) and the Extendable Mobile Ad-hoc Network Emulator (EMANE), we present the development and evaluation of an emulated 5G sidelink (SL) network model. By customizing EMANE’s IEEE 802.11 model to adjust modulation and coding schemes based on signal quality and Packet Completion Rate (PCR) curves derived from link-level simulations, we have created a representative model of the 5G SL physical layer. Furthermore, our comparative analysis of routing protocols, specifically Optimized Link State Routing (OLSR) and Open Shortest Path First with MANET Designated Router (OSPF-MDR), underscores the critical role of protocol selection in diverse network environments. While OSPF-MDR exhibits advantages in stable networks, OLSR demonstrates greater efficiency in mode dynamic mobile scenarios. These insights not only contribute to optimizing vehicular network efficiency but also inform future advancements across various research domains.

For future intelligent communication systems, radiomap estimation (RME) is essential for acquiring panoramic awareness of spectrum spatial distribution in wireless environments. Recently, deep learning-based RME methods have been developed to reconstruct radiomaps from spectrum measurements collected at distributed sensors. However, these methods rely on gathering all input data at a central fusion center, resulting in large communication overheads, high computation costs, and privacy leakage concerns. To address these challenges, this work proposes a FedRME approach that makes federated learning applicable for distributed RME over a large-scale network, accommodating geographically heterogeneous transmitter locations and propagation environments. Specifically, we partition the large area into smaller regions to reduce the model complexity required for learning the radiomap in each region. Meanwhile, we incorporate the landscape map as an auxiliary input to induce a common learning model that adheres to the same propagation physics across all these heterogeneous regions. In doing so, fusion centers in all regions can collaborate through federated learning to enhance the overall RME performance. Simulation results indicate that our proposed method outperforms existing benchmarks, particularly under limited data, achieving higher learning accuracy with reduced model complexity and lower computational cost.

It is well known that index (discrete-time)-limited sampled sequences leak outside the support set when a band-limiting operation is applied. Similarly, a fractional shift causes an index-limited sequence to be infinite in extent due to the inherent band-limiting. Index-limited versions of discrete prolate spheroidal sequences (DPSS) are known to experience minimum leakage after band-limiting. In this work, we consider the effect of a half-sample shift and provide upper bounds on the resulting leakage energy for arbitrary sequences. Furthermore, we find an orthonormal basis, derived from DPSS, whose members are ordered according to energy concentrationafter half sample shifts; the primary (first) member being the global optimum.

The limited over-the-air (OTA) pilot symbols in multiple-input-multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for detecting transmitted data symbols at the receiver, especially for machine learning-based approaches. While it is crucial to explore effective ways to exploit pilots, one can also take advantage of the data symbols to improve detection performance. Thus, this paper introduces an online attention-based approach, namely RC-AttStructNet-DF, that can efficiently utilize pilot symbols and be dynamically updated with the detected payload data using the decision feedback (DF) mechanism. Reservoir computing (RC) is employed in the time domain network to facilitate efficient online training. The frequency domain network adopts the novel 2D multi-head attention (MHA) module to capture the time and frequency correlations, and the structural-based StructNet to facilitate the DF mechanism. The attention loss is designed to learn the frequency domain network. The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols. The effectiveness of the RC-AttStructNet-DF approach is demonstrated through extensive experiments in MIMO-OFDM and massive MIMO-OFDM systems with different modulation orders and under various scenarios.

The non-linear distortion caused by non-ideal radio frequency (RF) components especially the power amplifier (PA) limits the applications of higher order modulation and degrades power utilization efficiency. To improve the achievable rate in modern systems, it becomes critical to overcome the non-linear distortion so that we can maximize the opportunity of using higher order modulation such as 256QAM, 1024QAM and even 4096QAM at high transmission power. In this paper, we introduce an artificial intelligence (AI)-enabled non-linearity compensation scheme (AI-NC) to avoid the "model deficit" problem. The introduced AI-NC adapts the Echo State Network (ESN) to enable fast online training without additional training overhead. Furthermore, it is general enough to be used for any types of power amplifiers (PAs) with different non-linearity characteristics and different channel environments. It can also be used for the communication system using multiple antennas and supporting multiple users simultaneously. Simulation results and hardware-based tests show that the proposed AI-NC can drastically improve the link performance and/or coverage of higher order modulations in practice.

Despite the promises of current and future cellular networks to increase security, privacy, and robustness, 5G networks are designed to streamline discovery and initiate connections with limited computation and communication costs, leading to the predictability of control channels. This predictability enables signal-level attacks, particularly on unprotected initial access signals. To assess vulnerability in access control and enhance robustness in cellular networks, we present a strategic approach leveraging O-RAN architecture in this paper that detects and classifies signal-level attacks for actionable countermeasure defense. We evaluate attack scenarios of various power levels on both 4G/LTE-Advanced and 5G communication systems. We categorize the types of attack models based on the attack cost: Overshadowing and Jamming. Overshadowing represents low attack power categories with time and frequency synchronization, while Jamming represents un-targeted attacks that cause similar quality-of-service degradation as overshadowing attacks but require high power levels. Our detection strategy relies on supervised machine-learning models, specifically a Reservoir Computing (RC) based supervised learning approach that leverages physical and MAC-layer information for attack detection and classification. We demonstrate the efficacy of our detection strategy through extensive experimental evaluations using the O-RAN platform with software-defined radios (SDRs) and commercial off-the-shelf (COTS) user equipment (UEs). Empirical results show that our method can classify the change in statistics caused by most overshadowing and jamming attacks with more than 95% classification accuracy.

The increased flexibility and density of spectrum access in 5G New Radio (NR) has made jamming detection and classification a critical research area. To detect coexisting jamming and subtle interference, we introduce a Bayesian Inference-assisted machine learning (ML) methodology. Our methodology uses cross-layer Key Performance Indicator data collected on a Non-Standalone (NSA) 5G NR testbed to leverage supervised learning models, further assessed, calibrated, and revealed using Bayesian Network Model (BNM)-based inference. The models can operate on both instantaneous and sequential time-series data samples, achieving an Area under Curve above 0.954 for instantaneous models and above 0.988 for sequential models including the echo state network (ESN) from the Reservoir Computing (RC) family, across various jamming scenarios. The 180 ms instantaneous detection time allows for continuous tracking of the dynamic jamming condition due to UE mobility. Our approach serves as a validation method and a resilience enhancement tool for ML-based jamming detection while also enabling root cause identification for observed performance degradation. The introduced BNM-based inference proof-of-concept is successful in addressing 72.2% of the erroneous predictions of the RC-based sequential detection model caused by insufficient training data samples, thereby demonstrating its near real-time applicability in 5G NR and Beyond-5G networks.

In dynamic spectrum access (DSA) networks, secondary users (SUs) need to opportunistically access primary users’ (PUs) radio spectrum without causing significant interference. Since the SU-PU interaction is limited, deep reinforcement learning has been introduced to help SUs conduct spectrum access. Specifically, deep recurrent Q network (DRQN) has been utilized in DSA networks for SUs to aggregate information from recent experiences to make spectrum access decisions. DRQN is notorious for its sample efficiency since it needs a rather large number of training samples to tune its parameters which is a computationally demanding task. Deep echo state network (DEQN) has been introduced for DSA networks to address the sample efficiency issue of DRQN. In this work, we compare the convergence of DRQN and DEQN by comparing the upper bounds we obtain on their covering number, a notion of richness. Furthermore, we introduce a method to determine the right hyper-parameters for DEQN, providing system design guidance for DEQN-based DSA networks. Extensive performance evaluation confirms that DEQN-based DSA strategy is the superior choice with regard to computational power while outperforming DRQN-based ones.

Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS system, where only a limited number of over-the-air (OTA) pilot symbols are utilized for training. However, this approach does not leverage the domain knowledge specific to the OTFS system to fully unlock the potential of RC. This paper introduces a novel two-dimensional RC (2D-RC) method that incorporates the domain knowledge of the OTFS system into the design for symbol detection in an online subframe-based manner. Specifically, as the channel interaction in the delay-Doppler (DD) domain is a two-dimensional (2D) circular operation, the 2D-RC is designed to have the 2D circular padding procedure and the 2D filtering structure to embed this knowledge. With the introduced architecture, 2D-RC can operate in the DD domain with only a single neural network, instead of necessitating multiple RCs to track channel variations in the time domain as in previous work. Numerical experiments demonstrate the advantages of the 2D-RC approach over the previous RC-based approach and compared model-based methods across different OTFS system variants and modulation orders.

This paper provides use cases to adapt 5G sidelink technology to enable multi-hop User Equipment (UE)-to-UE (U2U) and UE-to-Network relaying in 3GPP standards. Such a capability could enable groups of users to communicate with each other when operating at the periphery or outside a network's coverage area, with commercial and public safety benefits. This paper compares routing protocols to enable sidelink with U2U relay to support a Mobile Ad hoc Network (MANET). A gap analysis of current 3rd Generation Partnership Project (3GPP) Release 18 (R-18) specifications is performed to determine the missing procedures to enable multi-hop U2U relaying, along with a proposed candidate protocol to fill the gap. The candidate protocol can be submitted as a contribution to 3GPP TSG Service and System Aspects (SA) Working Group 2 (WG2) as proposed changes to the 5G architecture in 3GPP Release 19 (R-19).

Since the start of 5G New Radio (NR) work in the 3rd Generation Partnership Project (3GPP) in early 2016, tremendous progress has been made in both standardization and commercial deployments. The first 5G NR release (Release 15) laid out a solid foundation in accommodating a diverse set of services, a wide range of spectra, and a variety of deployment scenarios, while being forward compatible. Expansion to vertical domain services [e.g., vehicle to everything (V2X), non-terrestrial networks (NTN)] was introduced in Release 16. Such an expansion was further accelerated in Release 17, with the standardization work being completed despite the extreme challenges due to COVID-19.

Since the start of 5G work in 3GPP in early 2016, tremendous progress has been made in both standardization and commercial deployments. 3GPP is now entering the second phase of 5G standardization, known as5G-Advanced, built on the 5G baseline in 3GPP Releases 15, 16, and 17. 3GPP Release 18, the start of 5G-Advanced, includes a diverse set of features that cover both device and network evolutions, providing balanced mobile broadband evolution and further vertical domain expansion and accommodating both immediate and long-term commercial needs. 5G-Advanced will significantly expand 5G capabilities, address many new use cases, transform connectivity experiences, and serve as an essential step in developing mobile communications towards 6G. This paper provides a comprehensive overview of the 3GPP 5G-Advanced development, introducing the prominent state-of-the-art technologies investigated in 3GPP and identifying key evolution directions for future research and standardization.

In this paper, we derive a theoretical upper bound on the generalization error of reservoir computing (RC), a special category of recurrent neural networks (RNNs). The specific RC implementation considered in this paper is the echo state network (ESN), and an upper bound on its generalization error is derived via the empirical Rademacher complexity (ERC) approach. While recent work in deriving risk bounds for RC frameworks makes use of a non-standard ERC measure and a direct application of its definition, our work uses the standard ERC measure and tools allowing fair comparison with conventional RNNs. The derived result shows that the generalization error bound obtained for ESNs is tighter than the existing bound for vanilla RNNs, suggesting easier generalization for ESNs. With the ESN applied to symbol detection in MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) systems, we show how the derived generalization error bound can guide underlying system design. Specifically, the derived bound together with the empirically characterized training loss is utilized to identify the optimum reservoir size in neurons for the ESN-based symbol detector. Finally, we corroborate our theoretical findings with results from simulations that employ 3GPP standards-compliant wireless channels, signifying the practical relevance of our work.

Reconfigurable intelligent surface (RIS) offers tremendous spectrum-and-energy efficiency in wireless networks. With the agility and mobility of an unmanned aerial vehicle (UAV), RIS can be mounted on a UAV to enable three-dimensional (3D) signal reflections, reliable air-ground connections, and higher configuration flexibility. However, the scalability of the aperture gain and the spatial multiplexing could not be guaranteed in a single UAV-enabled aerial RIS due to UAV’s limited payload and line-of-sight-dominated air-ground connection. In this paper, we study a UAV swarm-enabled aerial RIS (SARIS)-assisted downlink communication system. The objective of the considered SARIS system is to maximize the weighted sum-rate of ground users by designing the transmit beamforming at the base station (BS), the phase shifts of SARIS reflecting elements, and SARIS 3D placement. For joint BS and SARIS beamforming design, we introduce two beamforming schemes with low computational complexity. For SARIS placement design, the optimal SARIS 3D position is obtained by leveraging the tools from stochastic geometry and considering the distributions of ground users. Simulation results confirm the validity of the analytical derivations. In particular, the SARIS placement plays a vital role in the system performance when the distances between users and the BS increase.

Mobility load balancing (MLB) aims to solve the problem of uneven resource utilization in cellular networks. Since network dynamics are usually complicated and non-stationary, conventional model-based MLB methods fail to cover all scenarios of cellular networks. On the other hand, deep reinforcement learning (DRL) can provide a flexible framework to learn to distribute cell load evenly without explicit modeling of the underlying network dynamics. In this paper, we introduce a novel decentralized DRL-based MLB method where each cell has a DRL agent to learn its handover parameters and antenna tilt angle. As the number of cells increases, the decentralized framework is more computationally efficient than its centralized counterpart by dividing the action space. Furthermore, our designed decentralized DRL architecture only requires readily known information defined in existing cellular standards, and it can achieve a more balanced cell load distribution than the centralized DRL one by using individual reward functions. To provide realistic performance evaluation, a network simulator is introduced strictly following the Third Generation Partnership Project (3GPP) specifications. Furthermore, field data is used to construct the underlying cellular environment. Extensive evaluations have been conducted to demonstrate the fact that the introduced decentralized DRL-based MLB method can achieve a more balanced cell load distribution and a better performance of edge users than the state-of-the-art MLB methods.

Scalable video caching is a promising technique to alleviate backbone traffic in sixth generation (6G) networks, and to serve users with video quality that adapts to varying channel conditions. In this paper, we develop a layer-based scalable video caching technique with non-orthogonal transmission by taking advantage of the layer feature in the scalable video. In addition, the impact of different serving base station selection algorithms is investigated. Our results indicate that both the caching placement design and transmission scheme design dominate the caching performance. To evaluate the interplay of these two policies, a tractable metric of Caching Aided Data Rate (CADR) is characterized and maximized by jointly optimizing the aforementioned two policies. Together with extensive Monte Carlo simulations, numerical results are also evaluated in this paper, demonstrating that the proposed Layer-based video Caching scheme with Non-Orthogonal Transmission (LCNOT) can achieve higher CADR performance than other baseline schemes.

Dynamic spectrum access (DSA) has been introduced as a promising technology that allows a secondary system to access the licensed spectrum of the primary system to improve spectrum utilization. In this paper, we introduce Fed-MADRL by incorporating federated learning (FL) and multi-agent deep reinforcement learning (MADRL) to design a collaborative DSA strategy. Our Fed-MADRL scheme employs FL to enable multiple users to collaboratively optimize the system goal without sharing their training data. By keeping all the training data at the user end, FL improves the communication efficiency and strengthens user data privacy. To further reduce the communication overheads, each user only shares quantized information. We provide the convergence analysis to characterize the trade-off between the communication efficiency and the system performance. In particular, we show that the introduced method converges at a rate$\mathcal {O}(1/K^{1/4})$, where$K$is the number of FL iterations. To the best of our knowledge, Fed-MADRL is the first work that utilizes FL in DSA networks under quantized communication. Performance evaluation results show that the introduced Fed-MADRL method outperforms the independent learning method and achieves comparable performance with the centralized MADRL method, which requires much higher communication overheads.

In this paper, we develop a learning-based symbol detection algorithm for massive MIMO-OFDM systems. To exploit the structure information inherited in the received signals from massive antenna array, multi-mode reservoir computing is adopted as the building block to facilitate over-the-air training in time domain. In addition, alternating recursive least square optimization method, and decision feedback mechanism are utilized in our algorithm to achieve the real-time learning capability. That is, the neural network is trained purely online with its weights updated on an OFDM symbol basis to promptly and adaptively track the dynamic environment. Furthermore, an online learning-based module is devised to compensate the nonlinear distortion caused by RF circuit components. On top of that, a learning-efficient classifier named StructNet is introduced in frequency domain to further improve the symbol detection performance by utilizing the QAM constellation structural pattern. Evaluation results demonstrate that our algorithm achieves substantial gain over traditional model-based approach and state-of-the-art learning-based techniques under dynamic channel environment and RF circuit nonlinear distortion. Moreover, empirical result reveals our NN model is robust to training label error, which benefits the decision feedback mechanism.

Dynamic spectrum access (DSA) has emerged as a promising solution for spectrum usage enhancement by allowing opportunistic access of secondary users to the licensed spectrum. In this paper, we introduce Fed-MADRL, a collaborative DSA technique that exploits both federated learning (FL) and multiagent deep reinforcement learning (MADRL). FL allows numerous users to collaborate on the system goal optimization without sharing their training data. By keeping all training data at the user's end, FL simultaneously enhances communication efficiency and protects data privacy. To further reduce communication costs, each user in Fed-MADRL only shares quantized data. To the best of our knowledge, Fed-MADRL is the first effort that employs FL in DSA networks with quantized communication. Simulation results show that the introduced Fed-MADRL approach beats the independent learning method and provides comparable results to the synchronous FL method, which involves significantly greater communication overheads.

Jamming and intrusion detection are some of the most important research domains in 5G that aim to maintain use-case reliability, prevent degradation of user experience, and avoid severe infrastructure failure or denial of service in mission-critical applications. This paper introduces an anonymous jamming detection model for 5G and beyond based on critical signal parameters collected from the radio access and core network’s protocol stacks on a 5G testbed. The introduced system leverages both supervised and unsupervised learning to detect jamming with high-accuracy in real time, and allows for robust detection of unknown jamming types. Based on the given types of jamming, supervised instantaneous detection models reach an Area Under the Curve (AUC) within a range of 0.964 to 1 as compared to temporal-based long short-term memory (LSTM) models that reach AUC within a range of 0.923 to 1. The need for data annotation effort and the required knowledge of a vocabulary of known jamming limits the usage of the introduced supervised learning-based approach. To mitigate this issue, an unsupervised auto-encoder-based anomaly detection is also presented. The introduced unsupervised approach has an AUC of 0.987 with training samples collected without any jamming or interference and shows resistance to adversarial training samples within certain percentage. To retain transparency and allow domain knowledge injection, a Bayesian network model based causation analysis is further introduced.

In this paper, we derive an upper bound on the generalization error of reservoir computing (RC), a special category of recurrent neural networks (RNNs). The particular RC implementation considered in this paper is the echo state network (ESN), and the generalization bound is derived via the empirical Rademacher complexity (ERC) approach. While recent work in deriving risk bounds for RC frameworks makes use of a non-standard empirical Rademacher Complexity (ERC) measure and a direct application of its definition, our work uses the standard ERC measure and tools which allow easier extension to deep RC structures and a fair comparison with other RNN structures including the vanilla RNNs. Finally, we train an ESN for the task of symbol detection, a key component in the receive processing of 5G systems and show with an example how the derived generalization bound can guide the underlying system design. To be specific, we utilize the derived generalization error together with the characterized training loss to analytically identify the optimum number of neurons in the reservoir for the symbol detection task. Simulation results using 3GPP specified channels corroborate our theoretical findings, illustrating the significance and practical relevance of our work.

In this paper, we develop a learning-based symbol detection algorithm for massive MIMO systems. To exploit the structural information inherited in the received signals from massive antenna array, multi-mode reservoir computing is adopted as the building block to facilitate over-the-air training. In addition, alternating recursive least square optimization method, and decision feedback mechanism are utilized in our algorithm to achieve the real-time learning capability. That is, the neural network is trained purely online with its weights updated on an OFDM symbol basis to promptly and adaptively track the dynamic environment. Evaluation results demonstrate that our algorithm achieves substantial gain over traditional model-based approach and state-of-the-art learning-based techniques in dynamic channel environment. Moreover, empirical result reveals our NN model is robust to training label error, which benefits the decision feedback mechanism.

In the midst of the machine learning revolution, there is hope to thrive the ever-growing demand for limited spectrum resources imposed by the growth of wireless devices with a paradigm shift to more intelligent ways to manage and share the radio spectrum. This requirement mandates very energy-efficient solutions that can tackle the rapid changes of the wireless environment. This work considers spiking neural networks, which have been shown to drastically reduce the energy consumption compared to conventional neural networks in a reinforcement learning setup designed for the dynamic spectrum sharing scenario. Moreover, the temporal aspect of the problem and the necessity of sample efficiency motivates incorporating liquid state machines into this design. However, the agents’ state- and time-variant inputs impose a burden of a posteriori hyperparameter optimization for liquid state machines, rendering the deployment of reliable models whose reservoirs operate in favorable regimes very challenging in such a setting. Therefore, we employ a homeostatic learning rule for adaptively tuning small-world reservoir connections to maintain near-chaotic behavior during operation. Simulation results prove the performance of the introduced solution compared with several existing techniques.

Recently, there have been renewed interests in applying machine learning (ML) techniques to wireless systems. Nevertheless, ML-based approaches often require a large amount of data in training, and prior ML-based MIMO symbol detectors usually adopt offline learning approaches, which are not applicable to real-time signal processing. This paper adopts echo state network (ESN), a prominent type of reservoir computing (RC), to the real-time symbol detection task in MIMO-OFDM systems. Two novel ESN training methods, namely recursive-least-square and generalized adaptive weighted recursive-least-square, are introduced to enhance the performance of ESN training. Furthermore, a decision feedback mechanism is adopted to improve training efficiency and BER performance. Simulation studies show that the proposed methods perform better than previous conventional and ML-based MIMO symbol detectors. Finally, the effectiveness of our RC-based approach is validated with a software-defined radio (SDR) transceiver and extensive field tests in various real-world scenarios. To the best of our knowledge, this is the first real-time SDR implementation for ML-based MIMO-OFDM symbol detectors. Our work strongly indicates that ML-based signal processing could be a promising and critical approach for future wireless networks.

Fog nodes in the vicinity of IoT devices are promising to provision low-latency services by offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices, such as vehicles, wearable devices, and smartphones. Owing to the time-varying channel conditions, traffic loads, and computing loads, it is challenging to improve the Quality of Service (QoS) of mobile IoT devices. As task delay consists of both the transmission delay and computing delay, we investigate the resource allocation (i.e., including both radio resource and computation resource) in both the wireless channel and fog node to minimize the delay of all tasks while their QoS constraints are satisfied. We formulate the resource allocation problem into an integer nonlinear problem, where both the radio resource and computation resource are taken into account. As IoT tasks are dynamic, the resource allocation for different tasks are coupled with each other and the future information is impractical to be obtained. Therefore, we design an online reinforcement learning algorithm to make the suboptimal decision in real time based on the system’s experience replay data. The performance of the designed algorithm has been demonstrated by extensive simulation results.

The performance of federated learning systems is bottlenecked by communication costs and training variance. The communication overhead problem is usually addressed by three communication-reduction techniques, namely, model compression, partial device participation, and periodic aggregation, at the cost of increased training variance. Different from traditional distributed learning systems, federated learning suffers from data heterogeneity (since the devices sample their data from possibly different distributions), which induces additional variance among devices during training. Various variance-reduced training algorithms have been introduced to combat the effects of data heterogeneity, while they usually cost additional communication resources to deliver necessary control information. Additionally, data privacy remains a critical issue in FL and, thus, there have been attempts at bringing Differential Privacy to this framework as a mediator between utility and privacy requirements. This article investigates the tradeoffs between communication costs and training variance under a resource-constrained federated system theoretically and experimentally, and studies how communication reduction techniques interplay in a differentially private setting. The results provide important insights into designing practical privacy-aware federated learning systems.

Multi unmanned aerial vehicle (UAV) network is a promising solution to providing wireless coverage to ground users in challenging rural areas (such as Internet of Things (IoT) devices in farmlands), where the traditional cellular networks are sparse or unavailable. A key challenge in such networks is the 3-D placement of all UAV base stations (BSs) such that the formed multi-UAV network: 1) utilizes a minimum number of UAVs while ensuring—2) backhaul connectivity directly (or via other UAVs) to the nearby terrestrial BS; and 3) wireless coverage to all ground users in the area of operation. This joint backhaul-and-coverage-aware drone deployment (BoaRD) problem is largely unaddressed in the literature and, thus, is the focus of this article. We first formulate the BoaRD problem as integer linear programming (ILP). However, the problem is NP-hard and, therefore, we propose a low complexity algorithm with a provable performance guarantee to solve the problem efficiently. Our simulation study shows that the Proposed algorithm performs very close to that of the Optimal algorithm (solved using ILP solver) for smaller scenarios, where the area size and the number of users are relatively small. For larger scenarios, where the area size and the number of users are relatively large, the proposed algorithm greatly outperforms the baseline approaches—Backhaul-aware Greedy and random algorithm, respectively, by up to 17% and 95% in utilizing fewer UAVs while ensuring 100% ground-user coverage and backhaul connectivity for all deployed UAVs across all considered simulation setting.

Three-dimensional integrated circuits (3D-ICs) is a cutting-edge design methodology of placing the circuitry vertically aiming for a high-speed and energy-efficient system with the smallest design area. In this article, a novel 3-D neuromorphic system is proposed and analyzed, which utilizes the fabricated two-layer memristor as the electronic synapses in a spiking neural network (SNN). The two-layer structure of the memristors leads to a significant improvement in the design area ($2\times $), power consumption ($1.48 \times $), and latency ($2.58 \times $), compared to the traditional one-layer configuration. Meanwhile, the heat dissipation layers are added to our memristors reducing 30% cycle-to-cycle switching variation. Our memristive synapses are utilized for storing the exported weights of the SNNs that have threshold function as the activation function. The proposed neuromorphic system is evaluated using a hardware–software co-design approach importing the weights of SNNs into NeuroSIM. The simulation results demonstrate the significant improvement of memristive synapses on design area, power consumption, and latency, compared with the static random-access memory (SRAM) and other state-of-the-art memristive synapses (10%–66%).

In this paper, we consider a real-time deep learning-based symbol detection approach for MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the time-frequency structure of OFDM signals, a recurrent neural network (RNN) with deep feedforward output layers is introduced, where the recurrent layers and feedforward output layers are designed to process time-domain and frequency-domain information respectively. Reservoir computing (RC), a special type of RNN, and extreme learning machine (ELM), a special type of feedforward neural network, are chosen as the corresponding building blocks to facilitate over-the-air training. An online training loss objective is introduced to recursively update the neural weights in real-time. We believe this is the first work in the literature to realize real-time machine learning for MIMO-OFDM symbol detection, i.e., conducting NN-based symbol detection on an OFDM symbol basis. We demonstrate that (1) theIEEEstandardized WiFi training sequence can be directly applied as the real-time training sequence (2) the symbol detection performance can be further improved by using our theoretically derived pilot pattern. Evaluation results show that our RC-ELM-based symbol detection method outperforms traditional model-based techniques as well as state-of-the-art learning-based approaches in highly dynamic channel environments for real-time symbol detection.

Deep reinforcement learning (DRL) has been shown to be successful in many application domains. Combining recurrent neural networks (RNNs) and DRL further enables DRL to be applicable in non-Markovian environments by capturing temporal information. However, training of both DRL and RNNs is known to be challenging requiring a large amount of training data to achieve convergence. In many targeted applications, such as those used in the fifth-generation (5G) cellular communication, the environment is highly dynamic, while the available training data is very limited. Therefore, it is extremely important to develop DRL strategies that are capable of capturing the temporal correlation of the dynamic environment requiring limited training overhead. In this article, we introduce the deep echo state Q-network (DEQN) that can adapt to the highly dynamic environment in a short period of time with limited training data. We evaluate the performance of the introduced DEQN method under the dynamic spectrum sharing (DSS) scenario, which is a promising technology in 5G and future 6G networks to increase the spectrum utilization. Compared with conventional spectrum management policy that grants a fixed spectrum band to a single system for exclusive access, DSS allows the secondary system to share the spectrum with the primary system. Our work sheds light on the application of an efficient DRL framework in highly dynamic environments with limited available training data.

The next-generation cellular system operating in millimeter-wave (mm-Wave) frequencies requires different signal processing schemes from the legacy LTE system. Among these, beam management aligns the transmitter-receiver pairs with narrow beams and serves as an essential initial acquisition procedure. This paper introduces a novel angle-based downlink precoding strategy, including beam selection and user scheduling for a full dimension (FD) MIMO-OFDM system. The highly directional mm-Wave channel allows the system to perform user/beam scheduling rather than pure user scheduling during the precoding process. A beam-based receive matrix eliminates the remaining intra-cell interference caused by overlapped beams. The performance analysis proves the superiority of the proposed precoding and scheduling strategies over the conventional technique.

In this paper, we introduce a structure-based neural network architecture, namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO-OFDM signals through reservoir computing (RC). A binary classifier leverages the repetitive constellation structure in the system to perform multi-class detection. The incorporation of RC allows the RC-Struct to be learned in a purely online fashion with extremely limited pilot symbols in each OFDM subframe. The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity. Experiments show that the introduced RC-Struct outperforms both the conventional model-based symbol detection approaches and the state-of-the-art learning-based strategies in terms of bit error rate (BER). The advantages of RC-Struct over existing methods become more significant when rank and link adaptation are adopted. The introduced RC-Struct sheds light on combining communication domain knowledge and learning-based receive processing for 5G/5G-Advanced and Beyond.

In this paper, we introduce a new neural network (NN) structure, multi-mode reservoir computing (Multi-Mode RC). It inherits the dynamic mechanism of RC and processes the forward path and loss optimization of the NN using tensor as the underlying data format. Multi-Mode RC exhibits less complexity compared to conventional RC structures (e.g. single-mode RC), and offers comparable generalization performance to its single-mode counterpart. Furthermore, we introduce an alternating least square-based learning algorithm as well as the associated theoretical analysis for Multi-Mode RC. The result can be utilized to guide the configuration of NN parameters to sufficiently circumvent over-fitting issues. As a key application, we consider the symbol detection task in multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems with massive MIMO employed at the base stations (BSs). Thanks to the tensor structure of massive MIMO-OFDM signals, our online learning-based symbol detection method generalizes well in terms of bit error rate even using a limited online training set. Evaluation results suggest that the Multi-Mode RC-based learning framework can efficiently and effectively combat practical constraints of wireless systems (i.e. channel state information (CSI) errors and hardware non-linearity) to enable robust and adaptive communications over the air.

Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain. The conventional waveform, orthogonal frequency division multiplexing (OFDM), requires tracking frequency selective fading channels over the time, whereas OTFS benefits from full time-frequency diversity by leveraging appropriate equalization techniques. In this paper, we consider a neural network-based supervised learning framework for OTFS equalization. Learning of the introduced neural network is conducted in each OTFS frame fulfilling an online learning framework: the training and testing datasets are within the same OTFS-frame over the air. Utilizing reservoir computing, a special recurrent neural network, the resulting one-shot online learning is sufficiently flexible to cope with channel variations among different OTFS frames (e.g., due to the link/rank adaptation and user scheduling in cellular networks). The proposed method does not require explicit channel state information (CSI) and simulation results demonstrate a lower bit error rate (BER) than conventional equalization methods in the low signal-to-noise (SNR) regime under large Doppler spreads. When compared with its neural network-based counterparts for OFDM, the introduced approach for OTFS will lead to a better tradeoff between the processing complexity and the equalization performance.

The delayed feedback reservoir (DFR) network is a delay-dynamic architecture that incorporates time in its training and inference. This quality enables DFR networks to proficiently model time series in a scalable architecture with only one nonlinear neuron. Previous studies have highlighted the accuracy and energy efficiency of DFR networks in ASIC implementations; however, these approaches are limited by hardcoded weights and static reservoir architectures. In this work, we introduce a hybrid FPGA-ASIC DFR system that combines the flexibility of a FPGA platform with the energy efficiency of an ASIC. To be specific, the FPGA allows for dynamic reconfiguration and training of the readout weights during runtime, while the ASIC provides an analog activation function for the single neuron. The accuracy and energy consumption of the introduced system is demonstrated for the applications of NARMA10 as well as MIMO spectrum sensing which is a critical component of dynamic spectrum sharing/access for 5G/beyond-5G systems. Results showcase the potential to enable on-board intelligence for future wireless systems, especially for Internet of Things (IoT) devices in low-power environments.

Existing routing protocols may not be applicable in UAV networks because of their dynamic network topology and lack of accurate position information. In this paper, an enhanced flooding-based routing protocol is designed based on random network coding (RNC) and clustering for swarm UAV networks, enabling the efficient routing process without any routing path discovery or network topology information. RNC can naturally accelerate the routing process, with which in some hops fewer generations need to be transmitted. To address the issue of numerous hops and further expedite routing process, a clustering method is leveraged, where UAV networks are partitioned into multiple clusters and generations are only flooded from representatives of each cluster rather than flooded from each UAV. By this way, the amount of hops can be significantly reduced. The technical details of the introduced routing protocol are designed. Moreover, to capture the dynamic network topology, the Poisson cluster process is employed to model UAV networks. Afterwards, stochastic geometry tools are utilized to derive the distance distribution between two random selected UAVs and analytically evaluate performance. Extensive simulation studies are conducted to prove the validation of performance analysis, demonstrate the effectiveness of our designed routing protocol, and reveal its design insight.

Internet of Things (IoT) has attracted considerable attention in recent years due to its potential of interconnecting a large number of heterogeneous wireless devices. However, it is usually challenging to provide reliable and efficient random access control when massive IoT devices are trying to access the network simultaneously. In this article, we investigate methods to introduce intelligent random access management for a massive cellular IoT network to reduce access latency and access failures. Toward this end, we introduce two novel frameworks, namely, local device selection (LDS) and intelligent preamble selection (IPS). LDS enables local communication between neighboring devices to provide cluster-wide cooperative congestion control, which leads to a better distribution of the access intensity under bursty traffics. Taking advantage of the capability of reinforcement learning in developing cooperative multiagent policies, IPS is introduced to enable the optimization of the preamble selection policy in each IoT clusters. To handle the exponentially growing action space in IPS, we design a novel reinforcement learning structure, named branching actor–critic, to ensure that the output size of the underlying neural networks only grows linearly with the number of action dimensions. Simulation results indicate that the introduced mechanism achieves much lower access delays with fewer access failures in various realistic scenarios of interests.

Freshness and quality are two important metrics in real-time video applications in surveillance networks. However, these two metrics conflict with each other in many cases. To enhance the performance of real-time video services, it is very important, but also challenging, to find a good tradeoff between freshness and quality. In this article, we focus on studying the tradeoff between freshness and quality, where video packets are encoded with scalable video coding (SVC) and adaptive random network coding (ARNC). Moreover, the Age of Information (AoI) is applied to model video freshness, while the video quality is modeled by the number of layers received by users. A utility function is defined to capture the tradeoff between video freshness and video quality. By maximizing the defined utilities, an excellent tradeoff between freshness and quality could be obtained. To solve the formulated optimization problem, the maximization of the utility function is characterized as a Markov decision process (MDP) problem, which is effectively solved by a heuristic algorithm and a deep$Q$network (DQN)-based algorithm. Simulation results indicate that the applied ARNC technique can achieve higher utility performance than other benchmark transmission techniques, and the DQN-based algorithm outperforms the heuristic algorithm in most cases.

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 echo state network (ESN) is a recently developed machine-learning paradigm whose processing capabilities rely on the dynamical behavior of recurrent neural networks. Its performance outperforms traditional recurrent neural networks in nonlinear system identification and temporal information processing applications. We design and implement a cost-efficient ESN architecture on field-programmable gate array (FPGA) that explores the full capacity of digital signal processor blocks on low-cost and low-power FPGA hardware. Specifically, our scalable ESN architecture on FPGA exploits Xilinx DSP48E1 units to cut down the need of configurable logic blocks. The proposed architecture includes a linear combination processor with negligible deployment of configurable logic blocks and a high-accuracy nonlinear function approximator. Our work is verified with the prediction task on the classical NARMA dataset and a symbol detection task for orthogonal frequency division multiplexing systems using a wireless communication testbed built on a software-defined radio platform. Experiments and performance measurement show that the new ESN architecture is capable of processing real-world data efficiently for low-cost and low-power applications.

Deep neural networks (DNNs), a brain-inspired learning methodology, requires tremendous data for training before performing inference tasks. The recent studies demonstrate a strong positive correlation between the inference accuracy and the size of the DNNs and datasets, which leads to an inevitable demand for large DNNs. However, conventional memory techniques are not adequate to deal with the drastic growth of dataset and neural network size. Recently, a resistive memristor has been widely considered as the next generation memory device owing to its high density and low power consumption. Nevertheless, its high switching resistance variations (cycle-to-cycle) restrict its feasibility in deep learning. In this work, a novel memristor configuration with the enhanced heat dissipation feature is fabricated and evaluated to address this challenge. Our experimental results demonstrate our memristor reduces the resistance variation by ~ 30% and the inference accuracy increases correspondingly in a similar range. The accuracy increment is evaluated by our deep delay-feed-back reservoir computing (Deep-DFR) model. The design area, power consumption, and latency are reduced by ~48%, ~42%, and ~67%, respectively, compared to the conventional static random-access memory technique (6T). The performance of our memristor is improved at various degrees (~13%-73%) compared to the state-of-the-art memristors.

Deep learning (DL) has gained unprecedented success in many real-world applications. However, DL poses difficulties for efficient hardware implementation due to the needs of a complex gradient-based learning algorithm and the required high memory bandwidth for synaptic weight storage, especially in today's data-intensive environment. Computing-in-memory (CIM) strategies have emerged as an alternative for realizing energy-efficient neuromorphic applications in silicon, reducing resources and energy required for neural computations. In this work, we exploit a CIM-based spatial-temporal hybrid neural network (STHNN) with a unique learning algorithm. To be specific, we integrate both multilayer perceptron and recurrent-based delay-dynamical system, making the network becomes linear separable while processing information in both spatial and temporal domains, better yet, reducing the memory bandwidth and hardware overhead through the CIM architecture. The prototype fabricated in 180 nm CMOS process is built of fully-analog components, yielding an average on-chip classification accuracy up to 86.9% on handprinted alphabet characters with a power consumption of 33 mW. Beyond that, through the handwritten digit database and the radio frequency fingerprinting dataset, software-based numerical evaluations offer 1.6 -to- 9.8 × and 1.9 -to- 4.4 × speedup, respectively, without significantly degrading its classification accuracy compared to the cutting-edge DL approaches.

In this paper, a downlink (DL) precoding scheme is introduced for time-division-duplex (TDD) multi-cell multi-user 3D massive MIMO/full-dimension MIMO (FD-MIMO) systems. A pre-beamformer based on uplink direction-of-arrival (DoA) is incorporated with interference alignment (IA) scheme to provide more degrees of freedom for each cell. We analyze the feasible conditions of applying IA schemes and provide the corresponding precoding schemes for different inter-cell interference scenarios. Simulation results show that the introduced IA-DoA-based multi-cell multi-user precoding and power allocation scheme significantly outperform the existing IA-based precoding strategies for FD-MIMO systems.

In this paper, we investigate online learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) - reservoir computing (RC). We first introduce the Time-Frequency RC to take advantage of the structural information inherent in OFDM signals. Using the time domain RC and the time-frequency RC as building blocks, we provide two extensions of the shallow RC to RCNet: 1) Stacking multiple time domain RCs; 2) Stacking multiple time-frequency RCs into a deep structure. The combination of RNN dynamics, the time-frequency structure of MIMO-OFDM signals, and the deep network enables RCNet to handle the interference and nonlinear distortion of MIMO-OFDM signals to outperform existing methods. Unlike most existing NN-based detection strategies, RCNet is also shown to provide a good generalization performance even with a limited online training set (i.e, similar amount of reference signals/training as standard model-based approaches). Numerical experiments demonstrate that the introduced RCNet can offer a faster learning convergence and as much as 20% gain in bit error rate over a shallow RC structure by compensating for the nonlinear distortion of the MIMO-OFDM signal, such as due to power amplifier compression in the transmitter or due to finite quantization resolution in the receiver.

In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR with spiking neuros: DFRs are a new type of recurrent neural networks (RNNs) that are able to capture the temporal correlations in time series while spiking neurons are energy-efficient and biologically plausible neurons models. The introduced deep spiking DFR model is energy-efficient and has the capability of analyzing time series signals. The corresponding field programmable gate arrays (FPGA)-based hardware implementation of such deep spiking DFR model is introduced and the underlying energy-efficiency and recourse utilization are evaluated. Various spike encoding schemes are explored and the optimal spike encoding scheme to analyze the time series has been identified. To be specific, we evaluate the performance of the introduced model using the spectrum occupancy time series data in MIMO-OFDM based cognitive radio (CR) in dynamic spectrum sharing (DSS) networks. In a MIMO-OFDM DSS system, available spectrum is very scarce and efficient utilization of spectrum is very essential. To improve the spectrum efficiency, the first step is to identify the frequency bands that are not utilized by the existing users so that a secondary user (SU) can use them for transmission. Due to the channel correlation as well as users' activities, there is a significant temporal correlation in the spectrum occupancy behavior of the frequency bands in different time slots. The introduced deep spiking DFR model is used to capture the temporal correlation of the spectrum occupancy time series and predict the idle/busy subcarriers in future time slots for potential spectrum access. Evaluation results suggest that our introduced model achieves higher area under curve (AUC) in the receiver operating characteristic (ROC) curve compared with the traditional energy detection-based strategies and the learning-based support vector machines (SVMs).

Conventional reservoir computing (RC) is a shallow recurrent neural network (RNN) with fixed high dimensional hidden dynamics and one trainable output layer. It has the nice feature of requiring limited training which is critical for certain applications where training data is extremely limited and costly to obtain. In this paper, we consider two ways to extend the shallow architecture to deep RC to improve the performance without sacrificing the underlying benefit: (1) Extend the output layer to a three layer structure which promotes a joint time-frequency processing to neuron states; (2) Sequentially stack RCs to form a deep neural network. Using the new structure of the deep RC we redesign the physical layer receiver for multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) signals since MIMO-OFDM is a key enabling technology in the 5th generation (5G) cellular network. The combination of RNN dynamics and the time-frequency structure of MIMO-OFDM signals allows deep RC to handle miscellaneous interference in nonlinear MIMO-OFDM channels to achieve improved performance compared to existing techniques. Meanwhile, rather than deep feedforward neural networks which rely on a massive amount of training, our introduced deep RC framework can provide a decent generalization performance using the same amount of pilots as conventional model-based methods in 5G systems. Numerical experiments show that the deep RC based receiver can offer a faster learning convergence and effectively mitigate unknown non-linear radio frequency (RF) distortion yielding twenty percent gain in terms of bit error rate (BER) over the shallow RC structure.

The Reservoir Computing, a neural computing framework suited for temporal information processing, utilizes a dynamic reservoir layer for high-dimensional encoding, enhancing the separability of the network. In this paper, we exploit a Deep Learning (DL)-based detection strategy for Multiple-input, Multiple-output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) symbol detection. To be specific, we introduce a Deep Echo State Network (DESN), a unique hierarchical processing structure with multiple time intervals, to enhance the memory capacity and accelerate the detection efficiency. The resulting hardware prototype with the hybrid memristor-CMOS co-design provides the in-memory computing and parallel processing capabilities, significantly reducing the hardware and power overhead. With the standard 180nm CMOS process and memristive synapses, the introduced DESN consumes merely 105mW of power consumption, exhibiting 16.7% power reduction compared to shallow ESN designs even with more dynamic layers and associated neurons. Furthermore, numerical evaluations demonstrate advantages of the DESN over state-of-the-art detection techniques in the literate for MIMO-OFDM systems even with a very limited training set, yielding a 47.8% improvement against conventional symbol detection techniques.

With the advance of edge computing, a fast and efficient machine learning model running on edge devices is needed. In this paper, we propose a novel quantization approach that reduces the memory and compute demands on edge devices without losing much accuracy. Also, we explore its application in communication such as symbol detection in 5G systems, attack detection of smart grid, and dynamic spectrum access. Conventional neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) could be exploited on these applications and achieve state-of-the-art performance. However, conventional neural networks consume a large amount of computation and storage resources, and thus do not fit well to edge devices. Reservoir computing (RC), which is a framework for computation derived from RNN, consists of a fixed reservoir layer and a trained readout layer. The advantages of RC compared to traditional RNNs are faster learning and lower training costs. Besides, RC has faster inference speed with fewer parameters and resistance to overfitting issues. These merits make the RC system more suitable for applications running on edge devices. We apply the proposed quantization approach to RC systems and demonstrate the proposed quantized RC system on Xilinx Zynq®-7000 FPGA board. On the sequential MNIST dataset, the quantized RC system utilizes 62%, 65%, and 64% less of DSP, FF, and LUT, respectively compared to the floating-point RNN. The inference speed is improved by 17 times with an 8% accuracy drop.

In Internet-of-Thing (IoT) networks, enormous low-power IoT devices execute latency-sensitive yet computation intensive machine learning tasks. However, the energy is usually scarce for IoT devices, especially for some without battery and relying on solar power or other renewables forms. In this paper, we introduce a cross-layer optimization framework for distributed computing among low-power IoT devices. Specifically, a programming layer design for distributed IoT networks is presented by addressing the problems of application partition, task scheduling, and communication overhead mitigation. Furthermore, the associated federated learning and local differential privacy schemes are developed in the communication layer to enable distributed machine learning with privacy preservation. In addition, we illustrate a three-dimensional network architecture with various network components to facilitate efficient and reliable information exchange among IoT devices. Moreover, a model quantization design for IoT devices is illustrated to reduce the cost of information exchange. Finally, a parallel and scalable neuromorphic computing system for IoT devices is established to achieve energy-efficient distributed computing platforms in the hardware layer. Based on the introduced cross-layer optimization framework, IoT devices can execute their machine learning tasks in an energy-efficient way while guaranteeing data privacy and reducing communication costs.

We consider the scenario where a sender streams a flow at a fixed rate to a receiver across a multi-hop network, possibly using multiple paths. Data transmission over a link incurs a cost and a delay, both of which are traffic-dependent. We study the problem of minimizing network transmission cost subject to a maximum delay constraint and a throughput requirement. The problem is important for leveraging edge-cloud computing platforms to support computationally intensive IoT applications, which are sensitive to three critical performance metrics, i.e., cost, maximum delay, and throughput. Our problem jointly considers the three metrics, while existing ones only account for one or two of them. We first show that our problem is uniquely challenging, as (i) it is NP-complete even to find a feasible solution satisfying all constraints, and (ii) directly extending existing solutions to our problem results in problem-dependent maximum delay violations that can be unbounded. We then design both an approximation algorithm and an efficient heuristic. For any feasible instance, our approximation algorithm will achieve a cost no worse than the optimal, while violating the maximum delay constraint and the throughput requirement only by constant ratios. Meanwhile, our heuristic will construct feasible solutions for a large portion (over 60% empirically) of feasible instances, strictly satisfying the maximum delay constraint and the throughput requirement. We further characterize a condition under which the cost of our heuristic must be within a problem-dependent-ratio gap to the optimal. We simulate representative edge computing platforms, and observe that (i) when sacrificing 3% throughput, our approximation algorithm reduces 32% cost as compared to a greedy baseline, and satisfies the maximum delay constraint for 56% simulated instances; (ii) our heuristic solves 62% of feasible instances, and reduces 24% cost as compared to the baseline while strictly satisfying all constraints.

Unmanned aerial systems/vehicles (UAS/UAVs) are emerging in commercial spaces and will support many applications and services, such as smart agriculture, dynamic network deployment, and network coverage extension, surveillance and security. Emerging 5G terrestrial cellular communications networks will support UAS communications. This paper describes the communications security implications of integrating UAVs into cellular networks. We consider two roles for UAVs in a terrestrial cellular system—guardians and attackers—and analyze solutions against eavesdropping. Our approach leverages the mobility of UAV guardians that act as relays or jammers. The numerical analysis using common air-to-ground and air-to-air channel models demonstrates how the use of ground and aerial relay nodes can improve the secrecy rate in light of ground and UAV-based attacks. Specifically, the dependency on height and elevation angle between the ground and aerial communicating nodes is analyzed. The results show that the strategic use of single and multi-hop aerial relays can significantly increase the secrecy rate of ground cellular network users.

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.

Unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future wireless networks to provide extensive coverage and additional computational capabilities for user equipments (UEs). In this article, we study mobile-edge computing (MEC) in air-ground integrated wireless networks, including ground computational access points (GCAPs), UAVs, and UEs, where UAVs and GCAPs cooperatively provide computing resources for UEs. Our goal is to minimize the total energy consumption of UEs by jointly optimizing users' association, uplink power control, channel allocation, computation capacity allocation, and UAV 3-D placement, subject to the constraints on deterministic binary offloading, UEs' latency requirements, computation capacity, UAV power consumption, and available bandwidth. Due to the nonconvexity of the primary problem and the coupling of variables, we introduce a coordinate descent algorithm that decomposes the UEs' energy consumption minimization problem into several subproblems which can be efficiently solved. The simulation results demonstrate the advantages of the proposed algorithm in terms of the reduced total energy consumption of UEs.

The emerging cognitive radio-based Internet-of-Things (CR-IoT) network provides a novel paradigm solution for IoT devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in the CR-IoT network which has been investigated extensively under the Gaussian noise/interference. Since most of the interference in an IoT network is non-Gaussian, in this article, we introduce a novel spectrum sensing method for CR-IoT with additive Gaussian mixture noise/interference. The introduced method maps the observation signal matrix from the original input space to a high-dimensional feature space by a nonlinear Gaussian kernel function and then constructs a kernelized test statistic in the feature space. The approximate analytical expressions of the false alarm and detection probability of the proposed scheme are derived under Gaussian mixture noise, and the decision threshold can be determined according to false alarm probability. The simulation results show that the introduced multiple-input-multiple-output (MIMO) spectrum sensing method achieves good performance under Gaussian mixture noise/interference and significantly outperforms existing detectors.

In this paper, we study a three-dimensional (3D) spectrum sharing between device-to-device (D2D) and unmanned aerial vehicles (UAVs) communications. We consider that UAVs perform spatial spectrum sensing to opportunistically access the licensed channels that are occupied by the D2D communications of ground users. The objective of the considered 3D spectrum sharing networks is to maximize the area spectral efficiency (ASE) of UAV networks while guaranteeing the required minimum ASE of D2D networks. Using the tools from machine learning, we obtain the probability of spatial false alarm and the probability of spatial missed detection at the UAV, which helps us to characterize the density of active UAVs. Then, based on the Neyman-Pearson criterion, we further derive the coverage probability of D2D and UAV communications by leveraging the tools from stochastic geometry. In addition, the ASE of the D2D and UAV networks are also obtained. Simulation results show that a decrease in the spatial spectrum sensing radius of UAVs reduces the coverage probability of UAV communications but improves the ASE of UAV networks. Furthermore, the proposed tools allow obtaining the optimal spatial spectrum sensing radius of UAVs given certain network parameters.

Underlay cognitive radio (CR) holds the promise to address spectrum scarcity and let industrial wireless sensor networks obtain spectrum extension from shared frequency band resources. However, underlay CR devices should be capable of properly adjusting wireless transmission parameters according to the sensing of wireless environments. To realize the goal, in this article, two different signal-to-noise ratio (SNR) estimation methods are proposed for time-frequency overlapped signal estimations in the underlay CR-based industrial Internet of Things (IoT). In the first method, normalized higher order cumulant equations and the theoretical value of normalized higher order cumulants are adopted to estimate the SNR of component signals and the SNR of received signals. In the second one, the power of each component signals and the received signals is estimated based on the second-order time-varying moments. For the performance analysis, the Cramer-Rao lower bound of the SNR estimation for the time-frequency overlapped signals is derived. Simulation results show that the proposed method based on normalized higher order cumulants not only can effectively estimate the SNR of the time-frequency overlapped signals, but also has the strong robustness to the spectrum overlapped rate and the hybrid power ratio. The proposed method with second-order time-varying moments is able to accurately estimate the SNR of the time-frequency overlapped signals effectively, especially in the low-SNR region. These features are extremely useful in the industrial IoT, which usually operate in low-SNR regimes.

Quality of service (QoS) guarantee is critical in urban rail transit. In this paper, the train-centric communication-based train control (CBTC) systems through train-to-train (T2T) wireless communication is introduced based on the modification of LTE vehicle-to-everything (LTE-V2X). To be specific, a novel train-centric CBTC systems is established based on T2T wireless communication where distributed sensing-based semi-persistent scheduling (DS-SPS) is served as the resource allocation scheme in the T2T scenario. The quantized age of information (AoI) is used as an integrated system QoS indicator of the CBTC wireless communication systems in urban rail transit. Machine learning techniques especially Q-learning is further utilized to improve system AoI performance. Simulation results show that the proposed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning can achieve improved system AoI and peak AoI performance compared with fixed SPS policy. Furthermore, the system performance of the designed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning is shown to be better than traditional LTE-M and WLAN based wireless communication systems.

The continued success in the development of neuromorphic computing has immensely pushed today's artificial intelligence forward. Deep neural networks (DNNs), a brainlike machine learning architecture, rely on the intensive vector-matrix computation with extraordinary performance in data-extensive applications. Recently, the nonvolatile memory (NVM) crossbar array uniquely has unvailed its intrinsic vector-matrix computation with parallel computing capability in neural network designs. In this article, we design and fabricate a hybrid-structured DNN (hybrid-DNN), combining both depth-in-space (spatial) and depth-in-time (temporal) deep learning characteristics. Our hybrid-DNN employs memristive synapses working in a hierarchical information processing fashion and delay-based spiking neural network (SNN) modules as the readout layer. Our fabricated prototype in 130-nm CMOS technology along with experimental results demonstrates its high computing parallelism and energy efficiency with low hardware implementation cost, making the designed system a candidate for low-power embedded applications. From chaotic time-series forecasting benchmarks, our hybrid-DNN exhibits 1.16×-13.77× reduction on the prediction error compared to the state-of-the-art DNN designs. Moreover, our hybrid-DNN records 99.03% and 99.63% testing accuracy on the handwritten digit classification and the spoken digit recognition tasks, respectively.

Scalable video coding (SVC) and video caching are two promising techniques in the 5th generation networks to improve the users' quality of experience (QoE) in terms of video retrieval. In this paper, we study the video content retrieval in cache-aided device-to-device (D2D) networks, where each video content is coded into multiple layers via SVC. In video caching placement phase, the probabilistic caching placement policy is applied, while in content retrieval phase, the non-orthogonal transmission scheme is utilized. Besides, different D2D transmitter selection algorithms are considered and cache-aided data rate (CADR) is formulated as a metric in this paper, and it is maximized by jointly optimizing the probability caching policy and the power allocation policy in two phases. Analytical results show that probabilistic caching policy incorporated with power domain non-orthogonal transmission scheme can achieve significant benefits compared with other benchmark schemes.

Beamforming in multiple input multiple output (MIMO) systems is one of the key technologies for modern wireless communication. Creating appropriate sector-specific broadcast beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users' distribution and movement over time. In this work, we present self-tuning sectorization: a deep reinforcement learning framework to optimize MIMO broadcast beams autonomously and dynamically based on users' distribution in the network. Taking directly UE measurement results as input, deep reinforcement learning agent can track and predict the UE distribution pattern and come up with the best broadcast beams for each cell. Extensive simulation results show that the introduced framework can achieve the optimal coverage, and converge to the oracle solution for both single sector and multiple sectors environment, and for both periodic and Markov mobility patterns.

In this paper, superimposed pilot based communication strategies are investigated for a time-division duplex (TDD)-based massive Full-Dimension MIMO (FD-MIMO) network jointly considering both uplink (UL) and downlink (DL) performance. To be specific, the DL multi-cell multi-user MIMO operation is connected to UL channel estimation through the superimposed pilot. The performance of UL channel estimation is analytically characterized and the estimated UL channel is linked to the DL MIMO operation for the massive MIMO network. In this way, the corresponding feedback for DL channel state information (CSI) can be eliminated while the UL pilot overhead can be minimized. Results suggest that superimposed pilot could significantly improve the overall network performance of a massive FD-MIMO network.

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.

Unmanned aerial vehicles (UAVs) are becoming important communication infrastructures. One major challenge of communications with UAV networks is the routing protocol design. Due to the inherent characteristics (e.g., dynamic network topology and limited UAV device capabilities), it is difficult to directly apply existing routing protocols that utilize network topology information and routing path explorations. In this article, two novel routing protocols are designed based on random network coding (RNC) for a swarm UAV network, where UAVs operate cooperatively as a swarm, enabling efficient routing process. The first routing protocol utilizes the unique feature of RNC: Original packets can be decoded as long as an UAV accumulates sufficient generations. This property can be used to effectively expedite the underlying routing process. The second routing protocol further improves the efficiency where each forwarding UAV only needs to create a new generation rather than decoding original packets. Accordingly, the duration of each hop can be significantly reduced. Extensive simulations have been conducted to evaluate the performance of the designed routing protocols. The simulation results demonstrate that our designed routing protocols can effectively enhance the performance on both average transmission delay and delay violation probabilities compared to benchmark methods.

In this paper, we introduce a reservoir computing (RC) structure, namely, windowed echo state network (WESN), for multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) symbol detection. We show that adding buffers in input layers is able to bring an enhanced short-term memory (STM) to the standard echo state network. A unified training framework is developed for the introduced WESN MIMO-OFDM symbol detector using both comb and scattered patterns, where the training set size is compatible with those adopted in 3GPP LTE/LTE-Advanced standards. Complexity analysis demonstrates the advantages of WESN based symbol detector over state-of-the-art symbol detectors when the number of OFDM sub-carriers is large, where the benchmark methods are chosen as linear minimum mean square error (LMMSE) detection and sphere decoder. Numerical evaluations suggest that WESN can significantly improve the symbol detection performance as well as effectively mitigate model mismatch effects using very limited training symbols.

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.

Routing protocol design is one of the major challenges for swarm UAV networks. Due to the characteristics of a dynamic network topology, the low-complexity and the large volume of UAV devices, existing routing protocols based on network topology information, and routing table updates are not applicable in swarm UAV networks. In this paper, a Random Network Coding (RNC) enabled routing protocol is proposed to support an efficient routing process, which does not require network topology information or pre-determined routing tables. With the proposed routing protocol, the routing process could be significantly expedited, since each forwarding UAV may have already overheard some encoded packets in previous hops. As a result, some hops may be required to deliver a few encoded packets, and less hops may need to be completed in the whole routing process. The corresponding simulation study is conducted, demonstrating that our proposed routing protocol is able to facilitate a more efficient routing process.

Mobile edge and vehicle-based depth sending and real-time point cloud communication is an essential subtask enabling autonomous driving. In this paper, we propose a framework for point cloud multicast in VANETs using vehicle to infrastructure (V2I) communication. We employ a scalable Binary Tree embedded Quad Tree (BTQT) point cloud source encoder with bitrate elasticity to match with an adaptive random network coding (ARNC) to multicast different layers to the vehicles. The scalability of our BTQT encoded point cloud provides a trade-off in the received voxel size/quality vs channel condition whereas the ARNC helps maximize the throughput under a hard delay constraint. The solution is tested with the outdoor 3D point cloud dataset from MERL for autonomous driving. The users with good channel conditions receive a near lossless point cloud whereas users with bad channel conditions are still able to receive at least the base layer point cloud.

Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: 1) avoiding causing harmful interference to primary users (PUs) and 2) conducting effective interference coordination with other SUs. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn “appropriate” spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network, called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large.

With the growing popularity of delay-sensitive applications (e.g., real-time conversational video, online gaming, and augmented reality) and future trends toward ultra-reliable low-latency communications such as the tactile Internet, performance analysis of wireless systems under the finite code blocklength constraint becomes extremely important. In this paper, we investigate the maximum achievable throughput of incremental redundancy-hybrid automatic repeat request (IR-HARQ) over the (correlated) Rayleigh fading channel under finite blocklength and delay-violation probability constraints as a function of the modulation scheme. The maximum number of HARQ rounds together with the transport block size specifies the underlying coding latency of the IR-HARQ scheme. A framework, namely the HARQ Markov model (HARQ-MM), is introduced to track the throughput and the probability of error of IR-HARQ over the Rayleigh fading channel as a function of the modulation scheme. The dispersion of parallel additive white Gaussian noise channels with finite input alphabets (e.g., pulse amplitude modulation) is analytically characterized. It is used to identify the state transition probabilities of the underlying HARQ-MM. An algorithm is developed to efficiently compute the steady-state distribution of the HARQ-MM. Extensive performance evaluation is conducted, which shows a good match between the throughput performance characterized by the theoretical framework and that achieved by the practical channel codes.

Spectrum access and interference management are amongst the most challenging issues in device-to-device (D2D) cellular networks. In order to address these issues, this paper introduces spatial spectrum sensing (SSS) for D2D cellular networks to facilitate cellular spectrum sharing by D2D users while providing a quality of service guarantee for cellular users. In order to assess the performance of the proposed scheme, we adopt a stochastic geometry approach in which the locations of base stations and D2D devices are modeled as independent Poisson point processes (PPPs). Assuming that the locations of the active cellular transmitters form another independent PPP, we characterize the area spectral efficiency of D2D networks under cellular users' outage probability constraint. The use of SSS prohibits D2D transmissions around the active cellular users because of which the locations of the active D2D transmitters are modeled as a Poisson hole process driven by the PPP of active cellular user locations. Our analysis carefully accounts for this spatial separation between active cellular users and active D2D devices. Extensive simulation and numerical results are presented to verify our analysis and demonstrate the advantages of SSS-based D2D cellular networks.

Caching is a promising technique for 5G networks to reduce the backhual traffic and increase the overall network efficiency. In this paper, we study the caching placement policy with consideration of cooperative transmission for a two-hop relay-enabled device-to-device (D2D) network. In the caching placement phase, the probabilistic caching placement policy is considered, and in the content transmission phase, the hybrid automatic repeat request (HARQ) scheme with soft information combining [i.e., energy accumulation (EA) and mutual-information accumulation (MIA)] is utilized to improve the content retrieval experience. Cache-aided successful transmission probability (CSTP) is adopted as the main performance metric in this paper. By using tools from stochastic geometry, analytical expressions for the CSTP under different transmission schemes are derived. In moderate or high SIR regime, the optimal caching placement policy is identified based on the analytical expression and the CSTP performance is maximized accordingly. Evaluation results suggest that the MIA-based caching strategy performs better as opposed to existing strategies in most cases, but this outperformance vanishes when the transmission environment becomes severe or when users’ requests become concentrated.

High reliability and low latency are crucial for urban rail transits. In this paper, we introduce communication strategies for communication-based train control (CBTC) systems using long-term evolution for metro (LTE-M) to improve the reliability and latency. To be specific, the FlashLinQ-based Train-to-Train (T2T) communication schemes are introduced considering both the transmission delay and the packet drop. The quantified resilience is also introduced as a system metric to evaluate the preservation and recovery performance of CBTC systems. First, a novel urban rail transit wireless communication model is established using FlashLinQ-based T2T communications. Then, we introduce a novel cognitive control scheme based on LTE-M with T2T communication to enhance the quality of service and the resilience of multi-train CBTC systems. In the introduced scheme, Q-learning is used to generate optimal control strategies considering both wireless communication parameters adaption and train control parameters. Extensive simulations are conducted and the results show that the resilience of CBTC systems can be enhanced using the introduced scheme. Furthermore, using the introduced scheme, not only the gaps in optimal velocity versus distance curve are smaller, but also the unplanned traction and breaking are reduced as well.

As you open this January issue of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), I hope everyone enjoyed a great holiday season and is excited for the new year of 2019. I am very delighted and honored to report several key metrics of IEEE TNNLS to the community.

In this paper, downlink (DL) precoding and power allocation strategies are identified for a time-division-duplex multi-cell multi-user massive full-dimension MIMO network. Utilizing channel reciprocity, DL channel state information feedback is eliminated and the DL multi-user MIMO precoding is linked to the uplink (UL) direction of arrival (DoA) estimation through the estimation of signal parameters via rotational invariance technique. Assuming non-orthogonal/non-ideal spreading sequences of the UL pilots, the performance of the UL DoA estimation is analytically characterized and the characterized DoA estimation error is incorporated into the corresponding DL precoding and power allocation strategy. The simulation results verify the accuracy of our analytical characterization of the DoA estimation and demonstrate that the introduced multi-user MIMO precoding and power allocation strategy outperforms the existing zero-forcing-based massive MIMO strategies.

Massive multiple input multiple output (MIMO) systems are a promising technology for next generation wireless communications due to their ability to increase capacity and enhance both spectrum and energy efficiency. To utilize the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential. Conventional approaches to obtain CSIT for frequency-division duplex (FDD) multi-user massive MIMO systems require downlink training and uplink CSI feedback. However, such training results in large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this paper, we investigate the channel estimation problem in FDD multi-user massive MIMO systems with spatially correlated channels and develop an efficient channel estimation algorithm that exploits the sparsity structure of the downlink channel matrix. The proposed algorithm selects the best features from the measurement matrix to obtain efficient CSI acquisition that can reduce the downlink training overhead compared with the conventional LS/MMSE channel estimators. We compare the performance of our proposed channel estimation method with traditional ones in terms of normalized mean square error (MSE). Simulation results verify that the proposed algorithm can significantly reduce the pilot overhead and has better performance compared with the traditional channel estimation methods.

In 3D massive MIMO (multiple-input multiple-output) systems, especially in time division duplex (TDD) mode, a base station (BS) relies on the uplink sounding signals from mobile stations to obtain the spatial information for downlink MIMO processing. Accordingly, multi-dimensional parameter estimation of MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study the joint estimation of elevation and azimuth angles as well as the delay parameters for 3D massive MIMO orthogonal frequency division multiplexing (OFDM) systems under a parametric channel modeling. To be specific, we introduce a matrix-based joint parameter estimation method, and analytically characterize its performance for massive MIMO OFDM systems. Results show that antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance, and the characterized MSEs match well with the simulated ones. Also, the parametric channel estimation outperforms the MMSE-based channel estimation in terms of the correlation between the estimated channel and the real channel.

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.

This paper investigates the channel access problem of cognitive radio networks. In the cognitive radio network, communication channels are assigned to primary users with priority while secondary users are able to detect the spectrum holes and switch among the channels for data transmission opportunities. The channel access problem of this kind of system can be formulated as a non-cooperative game. However, in prior works, the secondary users are usually assumed to be able to switch to any channel instantaneously, which is not possible in reality because the channel switching will incur transmission delays. In this paper, we formulate the channel access problem as a non-cooperative game where each channel can be used by only one user at a time. Moreover, considering the transmission delays, we limit the channel switching distance of the secondary users to a certain scope. In this case, the optimal channel access policy of each secondary user will depend on the long-term behaviors of primary users as well as the actions of other secondary users. For this non-cooperative game, we propose a multiagent Q-learning algorithm which requires neither the prior knowledge of channel dynamics nor the negotiations among players. Simulation examples are provided to demonstrate the effectiveness of the algorithm.

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.

The susceptibility of millimeter waveform propagation to blockages limits the coverage of millimeter-wave (mmWave) signals. To overcome blockages, we propose to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry, we derive expressions for the downlink coverage probability of relay-assisted mmWave cellular networks when the D2D links are implemented in either uplink mmWave or uplink microwave bands. We further investigate the spectral efficiency (SE) improvement in the cellular downlink, and the effect of D2D transmissions on the cellular uplink. For mmWave links, we derive the coverage probability using dominant interferer analysis while accounting for both blockages and beamforming gains. For microwave D2D links, we derive the coverage probability considering both line-of-sight and non-line-of-sight (NLOS) propagation. Numerical results show that downlink coverage and SE can be improved using two-hop D2D relaying. Specifically, microwave D2D relays achieve better coverage because D2D connections can be established under NLOS conditions. However, mmWave D2D relays achieve better coverage when the density of interferers is large because blockages eliminate interference from NLOS interferers. The SE on the downlink depends on the relay mode selection strategy, and mmWave D2D relays use a significantly smaller fraction of uplink resources than microwave D2D relays.

A new method for attack detection of smart grids with wind power generators using reservoir computing (RC) is introduced in this paper. RC is an energy-efficient computing paradigm within the field of neuromorphic computing and the delayed feedback networks (DFNs) implementation of RC has shown superior performance in many classification tasks. The combination of temporal encoding, DFN, and a multilayer perceptron (MLP) as the output readout layer is shown to yield performance improvement over existing attack detection methods such as MLPs, support vector machines (SVM), and conventional state vector estimation (SVE) in terms of attack detection in smart grids. The proposed algorithms are shown to be more robust than MLP and SVE in dealing with different variables such as the amplitude of the attack, attack types, and the number of compromised measurements in smart grids. The attack detection rate for the proposed RC-based system is higher than 99%, based on the accuracy metric for the average of 10 000 simulations.

Reservoir computing (RC) is a class of neuromorphic computing approaches that deals particularly well with time-series prediction tasks. It significantly reduces the training complexity of recurrent neural networks and is also suitable for hardware implementation whereby device physics are utilized in performing data processing. In this paper, the RC concept is applied to detecting a transmitted symbol in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Due to wireless propagation, the transmitted signal may undergo severe distortion before reaching the receiver. The nonlinear distortion introduced by the power amplifier at the transmitter may further complicate this process. Therefore, an efficient symbol detection strategy becomes critical. The conventional approach for symbol detection at the receiver requires accurate channel estimation of the underlying MIMO-OFDM system. However, in this paper, we introduce a novel symbol detection scheme where the estimation of the MIMO-OFDM channel becomes unnecessary. The introduced scheme utilizes an echo state network (ESN), which is a special class of RC. The ESN acts as a black box for system modeling purposes and can predict nonlinear dynamic systems in an efficient way. Simulation results for the uncoded bit error rate of nonlinear MIMO-OFDM systems show that the introduced scheme outperforms conventional symbol detection methods.

As the novel paradigm in the field of machine learning, reservoir computing possesses exceptional performance, e.g., energy efficiency, in tasks in which the traditional von Neumann computing systems cannot incorporate. This makes reservoir computing an ideal candidate to enable the sustainable development of cyber-physical systems (CPS). In the realm of CPS, the tight interaction among physical objects places security threats under the spotlight of attention. For such systems, especially the power grid network, false data injection could potentially lead to catastrophic consequences such as blackouts in large geographical areas. In this paper, we will introduce a reservoir computing architecture, the delayed feedback system, and apply the reservoir computing architecture for anomaly detection. To be specific, detailed design of the three imperative components in the delayed feedback system will be discussed and the corresponding energy efficiency performance will be analyzed. The application of the reservoir computing architecture to anomaly detection in a smart grid network will be introduced.

In this paper, we provide a comprehensive study and comparison of the energy consumption and bit-rate of multiple-input multiple-output (MIMO) wireless devices using multilevel quadrature amplitude modulation (MQAM). Both spatial diversity and spatial multiplexing (SM) are examined and compared under the assumption of perfect channel state information (CSI) at the transmitter. The transmit energy is derived under a target probability of error Peb at the receiver. The transmit energy and transceiver circuit energy are then utilized to study energy tradeoffs for MIMO diversity and SM. We observe that the amount of energy savings for diversity orders two and three is steeper than that of higher diversity orders. On the other hand, SM provides a linear increase in the overall bit rate along with receiver diversity due to maximum likelihood detection. Using these results, we create a diversity-SM tradeoff for energy consumption vs. bit-rate. Using these results our proposed scheme can save around 4 dB in energy consumption while achieving the highest bit rate after full SM at Peb= 10-3.

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 multiple-input multiple-output (FD-MIMO) orthogonal frequency division multiplexing (OFDM) system. Using estimation of signal parameter via rotational invariance technique (ESPRIT), we investigate into the performance of DoA estimation for two possible scenarios- first, for the case where no angular domain overlapping from interference signals exists with the target user's DoAs, and second, for the case where a number of DoAs of the target user's channel are completely overlapped with DoAs from interference signals, to be specific, from pilot contamination.

Millimeter-wave (mmWave) communications is one of the most promising candidate technologies for next generation cellular networks due to the global bandwidth shortage for mobile broadband access. The susceptibility of millimeter waveform propagation to blockages, however, may largely restrict the coverage of mmWave signals. To overcome blockages, we propose to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry, we develop a coverage probability model for the downlink of a relay- assisted mmWave cellular network using dominant interferer analysis, which accounts for both beamforming gains and blockages. Theoretical analysis and simulation results show that the downlink coverage of a mmWave cellular network can be improved by using two-hop D2D relay transmissions.

Supporting massive numbers of machine-type communication (MTC) devices poses several challenges for future 5G networks, including network control, scheduling, and powering these devices. A potential solution is to offload MTC traffic onto device-to-device (D2D) communication links to better manage radio resources and reduce MTC devices' energy consumption. However, this approach requires D2D users to use their own limited energy to relay MTC traffic, which may be undesirable. This motivates us to exploit recent advancements in RF energy harvesting for powering D2D relay transmissions. In this paper, we consider a D2D communication as an underlay to the cellular network, where D2D users access a fraction of the spectrum occupied by cellular users. This underlay model presents a fundamental trade-off: to protect cellular users, the spectrum available to D2D users needs to be reduced, which limits the number of D2D transmissions, but increases the amount of time that D2D users can spend harvesting energy to support MTC traffic. We study this trade-off by characterizing the spectral efficiency of MTC, D2D, and cellular users using stochastic geometry. The optimal spectrum partition factor is characterized to achieve fairness and balance in the network, while increasing the average MTC spectral efficiency.

Von Neumann bottleneck, which refers to the limited throughput between the CPU and memory, has already become a major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems have started to gain the increasing attentions as compact and energy-efficient computing platforms. As one of the most crucial components in the neuromorphic computing systems, neural encoder transforms the stimulus (input signals) into spike trains. In this paper, we adapt the temporal encoding scheme of interspike intervals (ISIs) and present an analog temporal neural encoder with its verification and recovery schemes. The proposed neural encoder allows efficient mapping of signal amplitude information into a spike-time sequence that represents the input data and offers perfect recovery for band-limited stimuli. With the novel iterative structure, the number of spikes increases exponentially with the number of neurons. From the measurements obtained from the fabricated neural encoder chip, our temporal encoder with ISI encoding is proved to be robust and error tolerant.

In order to meet the challenge of increasing data-rate demand as well as the form factor limitation of the base station (BS), 3-D massive multiple-input multiple-output (MIMO) technology has been introduced as one of the enabling technologies for fifth generation mobile cellular systems. In 3-D massive MIMO systems, a BS will rely on the uplink sounding signals from mobile stations to figure out the spatial information for downlink MIMO operations. Accordingly, multi-dimensional parameter estimation of a MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study the angle and delay estimation for 3-D massive MIMO systems under a parametric channel modeling. To be specific, we first introduce separate low complexity time delay and angle estimation algorithms based on unitary transformation, and analytically characterize the mean squared errors (MSEs) of these estimations for massive MIMO systems. Then, a matrix-based estimation of signal parameters via rotational invariance technique algorithm is applied to jointly estimate the delay and the angles where the MSEs are also analytically characterized. Our results show that the antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance. Simulation results suggest that the characterized MSEs match well with the simulated ones.

In a massive machine type communication (massive MTC) network, wireless connections between machine type devices (MTDs) and eNodeBs are unreliable due to the interference caused by uncoordinated access of other MTDs. Furthermore, it is with a high probability that the retransmission from the outage source MTD will fail again due to the spatiotemporally correlated interference. In this paper, we design and analyze a location-based cooperative strategy to improve the performance of massive MTC networks. In the cooperative strategy, an inactive MTD is selected as a relay if it has successfully decoded the packet and if it is located within a circular area around the eNodeB. Considering the spatial and temporal correlation of interference, the outage probability of the designed cooperative strategy is derived using stochastic geometry. Both the simulation and numerical results demonstrate that spatiotemporal correlation of interference significantly affects the performance analysis of cooperative massive MTC networks and our designed cooperative strategy can significantly reduce the outage probability compared to conventional retransmission.

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.

Device-to-Device (D2D) communication is one of the key technologies to get to very high data rates in future 5G networks through offloading part of the cellular traffic onto D2D networks. While extensive research is targeted on addressing the many challenges D2D brings along in cellular networks, security issues have not gained much attention, especially that the direct connections between proximity devices are more vulnerable to security threats, which in turn deteriorate users' experience. In this paper, we consider a D2D communication overheard by eavesdroppers in an underlay cellular network, where D2D users access a proportion of the spectrum occupied by cellular users. To mitigate the potential security threats, we turn towards a lightweight low-complexity approach by exploiting the physical characteristics of the wireless channels. Using tools from stochastic geometry, we derive the D2D transmission region and the average transmit power that guarantee a minimum secrecy rate and a target outage probability. Results reveal that when offloading less cellular traffic onto D2D links, the large cellular interference can be exploited to protect D2D links from eavesdroppers. On the other hand, with aggressive D2D offloading, D2D users can achieve a much higher secrecy capacity due to significantly reducing the distances between them.

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.

The technological advancements in the health care system have made possible the massive integration of biomedical sensors for monitoring patients' health and disease progression. In this paper, we consider a three tier medical body area network (MBAN): intra-MBAN, inter-MBAN, and beyond-MBAN. The intra-MBAN transmits sensors' data to a controller, which in turn transmits them in the inter-MBAN tier to an access device like a PDA or a tablet device, usually connected to a patient's medical database. The access device then serves as a mean of communication between intra-MBAN and beyond-MBAN to access the hospital information systems. This widely deployed design in hospitals places security and privacy violation threats on the spotlight of attention, especially in the inter-MBAN tier. This has motivated us to optimize the average MBAN controller transmit power that minimizes the probability of eavesdroppers overhearing the communication, using tools from stochastic geometry. We analyze this privacy protection scheme for eHealth systems through simulations. Results show that the proposed scheme achieves higher privacy protection, but at the expense of reduced coverage.

A cloud radio access network (Cloud-RAN) is a network architecture that holds onto the promise of meeting the explosive growth of mobile data traffic. Cloud-RAN consists a central processor (CP) connecting to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. To reduce the backhaul traffic, BS-level caching technique is utilized in which the popular contents are pre-fetched in memories at each BS. This technique plays an important role in future wireless big data processing due to its simplicity, low cost, and natural integration with big data analytical tools. Considered the tradeoff between the backhaul and the transmission power cost, in this paper we define the network cost of the system as a normalized weighted sum. The problem of minimizing the network cost with respect to both the precoding matrix and the cache placement matrix is formulated subject to the quality of service (QoS), peak transmission power, and cache capacity constraints. The l0-norm in the objective function along with the QoS constraints renders the optimization problem non-convex. Additionally, since the entries of cache placement matrix take binary values, the optimization problem falls into a mixed integer nonlinear programming (MINLP) which is a NP-hard problem. An iterative coordinated data assignment algorithm is introduced which achieves a stationary point of the problem. Simulations are conducted to illustrate the performance of introduced algorithm. It suggests that the introduced scheme can significantly reduce the total network cost of the underlying Cloud-RAN network and demonstrate the importance of considering the designing of cache placement matrix.

Reservoir computing (RC) is attracting widespread attention in several signal processing domains owing to its nonlinear stateful computation. It deals particularly well with time-series prediction tasks and reduces training complexity over recurrent neural networks. It is also suitable for hardware implementation whereby device physics are utilized in performing data processing. In this paper, the RC concept is applied to modeling a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. Due to the harsh propagation environment, the transmitted signal undergoes severe distortion that must be compensated for at the receiver. The nonlinear distortion introduced by the power amplifier at the transmitter further complicates this process. An effective channel estimation scheme is therefore required. In this paper, we introduce a MIMO-OFDM channel estimation scheme utilizing Echo State Network (ESN). Echo State Networks are powerful recurrent neural networks that can predict time-series very well. They acts as a black-box for system modeling purposes and models nonlinear dynamic systems efficiently. Simulation results for the bit error rate of the nonlinear MIMO-OFDM system show that the introduced channel estimator outperforms commonly used channel estimation schemes.

We consider a cellular network where mobile transceiver devices that are owned by self-interested users are incentivized to cooperate with each other using tokens, which they exchange electronically to “buy” and “sell” downlink relay services, thereby increasing the network's capacity compared to a network that only supports base station-to-device (B2D) communications. We investigate how an individual device in the network can learn its optimal cooperation policyonline, which it uses to decide whether or not to provide downlink relay services for other devices in exchange for tokens. We propose a supervised learning algorithm that devices can deploy to learn their optimal cooperation strategies online given their experienced network environment. We then systematically evaluate the learning algorithm in various deployment scenarios. Our simulation results suggest that devices have the greatest incentive to cooperate when the network contains (i) many devices with high energy budgets for relaying, (ii) many highly mobile users (e.g., users in motor vehicles), and (iii) neither too few nor too many tokens. Additionally, within the token system, self-interested devices can effectively learn to cooperate online, and achieve up to 20 percent throughput gains on average compared to B2D communications alone, all while selfishly maximizing their own utilities.

Ultra-densification is one of the main features of 5G networks. In an ultra-dense network, how to conduct interference management and spectrum allocation is a challenging issue. Spectrum sensing in cognitive radio networks is a distributed and efficient way to resolve this issue in ultra-dense networks. However, most of the studies on spectrum sensing only focus on sensing temporal spectrum opportunities where one or multiple primary users are active, which does not make full use of spectrum opportunities in the spatial location domain. To overcome the shortcomings of conventional temporal spectrum sensing, we study the problem of spatial spectrum sensing, which senses spatial spectrum opportunities in wireless networks. In this paper, the performance of spatial spectrum sensing and its application in sensing-based device-to-device (D2D) cellular networks are analyzed using stochastic geometry. Specifically, by modeling the locations of active transmitters as a Poisson point process, the spatial spectrum sensing problem is formulated using the framework of a detection theory. Closed-form expressions are obtained for the sensing threshold, probabilities of spatial detection, and false alarm. Furthermore, analytical throughput for D2D users and cellular users under both channel inversion and constant power allocation cases are derived. The optimal sensing radius that maximizes the defined network metric is obtained numerically. Finally, the simulation and numerical results are presented to verify our theoretical analysis.

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.

Coordinated multi-point (CoMP) transmission and reception are introduced as a promising technology in the 3GPP LTE-advanced standard, to manage interference, improve the overall system performance, and enhance system reliability. In this paper, a resource allocation problem is studied for downlink CoMP coordinated beamforming systems, where each base station (BS) serves its own mobile stations. Multiple-input-multiple-output (MIMO) transmit precoding and resource allocation are linked to the underlying proportional-fair scheduling to ensure a good trade-off between cell-average and cell-edge user spectral-efficiency. Due to the coupled interference among mobile stations, the resulting proportional-fair resource allocation optimization problem becomes nonconvex. To solve for optimal operating point for MIMO CoMP network, a parallel successive convex approximation-based algorithm is introduced. The introduced scheme enables all BSs to update their optimization variables in parallel by solving a sequence of strongly convex subproblems. Closed-form expressions of the locally optimal solution in both the high and low signal-to-noise regimes are characterized. The performance of the introduced scheme is also investigated through simulations. Numerical results show the efficiency of the introduced algorithm.

With the promise of meeting future capacity demands, 3-D massive-MIMO/full dimension multiple-input-multiple-output (FD-MIMO) systems have gained much interest in recent years. Apart from the huge spectral efficiency gain, 3-D massive-MIMO/FD-MIMO systems can also lead to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of the system, accurate channel state information is critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for 3-D millimeter wave massive-MIMO orthogonal frequency division multiplexing (OFDM) systems. To be specific, the DoA is estimated using estimation of signal parameter via rotational invariance technique method, and the root mean square error of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system. An ergodic capacity analysis of the system in the presence of DoA estimation error is also conducted, and an optimum power allocation algorithm is derived. Furthermore, it is shown that the DoA-based channel estimation achieves a better performance than the traditional linear minimum mean squared error estimation in terms of ergodic throughput and minimum chordal distance between the subspaces of the downlink precoders obtained from the underlying channel and the estimated channel.

In this paper, direction-of-arrival (DOA) estimation problem for massive multiple-input multiple-output (MIMO) systems with a two dimensional (2D) array is investigated, assuming no knowledge of path number, noise power, path gain correlations and bad data statistics. A novel iterative algorithm operating on tensor represented data is proposed, with integrated features of effective bad data mitigation and automatic source enumeration. Simulation results are presented to illustrate the excellent performance of the proposed algorithm in term of accuracy and robustness.

In this paper, a comprehensive model has been provided to study the energy consumption of wireless cellular devices, by analyzing the relationship between the modulation order and energy consumption of the power amplifier (PA) and other circuits in radio frequency transceivers. Two types of energy consumption for PAs are studied in detail: transmitted energy, which is provided to the antenna to transmit data, and energy dissipated as a heat. First, the transmitted energy is studied along with different modulation orders for different distances between the transmitter and receiver. Next, the dissipated energy with all corresponding parameters such as peak to average ratio (PAR) and the drain efficiency of the PA is discussed. Other circuits are examined to show that the energy of these circuits--unlike other models in the literature--change with modulation order. The results reinforce the idea that increasing the modulation order leads to higher energy consumption in the RF transceiver for large distance. The results also show that the energy dissipated due to PAR and drain efficiency is larger than the transmitted energy.

With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is very critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive- MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the root mean square error (RMSE) of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system.

This paper optimizes resource allocation that maximizes the energy efficiency (EE) of wireless systems with statistical quality of service (QoS) requirement, where a delay bound and its violation probability need to be guaranteed. To avoid wasting energy when serving random sources over wireless channels, we convert the QoS exponent, a key parameter to characterize statistical QoS guarantee under the framework of effective bandwidth and effective capacity, into multi-state QoS exponents dependent on the queue length. To illustrate how to optimize resource allocation, we consider multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A general method to optimize the queue length based bandwidth and power allocation (QRA) policy is proposed, which maximizes the EE under the statistical QoS constraint. A closed-form optimal QRA policy is derived for massive MIMO-OFDM system with infinite antennas serving the first order autoregressive source. The EE limit obtained from infinite delay bound and the achieved EEs of different policies under finite delay bounds are analyzed. Simulation and numerical results show that the EE achieved by the QRA policy approaches the EE limit when the delay bound is large, and is much higher than those achieved by existing policies considering statistical QoS provision when the delay bound is stringent.

In underlay cognitive radio networks (CRNs), secondary users (SUs) have to dynamically control their transmit powers so that the interference to primary users (PUs) is tolerable. Under this constraint, SUs' data link usually suffers from either high error rate with limited transmission range or long end-to-end delay caused by multi-hop transmissions. To improve the performance of SUs, cooperative routing using mutual-information accumulation is introduced in CRNs for the first time in this paper. To be specific, the routing and resource allocation problem in underlay CRNs is investigated and is factored into two sub-problems, each of which can be solved efficiently. Furthermore, a distributed algorithm is introduced and simulation results show that both the centralized and distributed algorithms can reduce upto 77% of the end-to-end delay compared to the traditional multi-hop delay-optimal routing in CRNs. Finally, theoretical analysis on the end-to-end delay for a one-dimensional (1-D) network in the low signal-to-interference ratio (SIR) region is conducted. Both the analytical and simulation results show that mutual-information accumulation can significantly decrease the delay of underlay CRNs, especially in the scenario where PUs have a tight interference power constraint.

Future wireless networks will face the dual challenge of supporting large traffic volumes while providing reliable service for delay-sensitive traffic. To meet the challenge, relay network has been introduced as a new network architecture for the fourth generation (4G) LTE-Advanced (LTE-A) networks. In this paper, we investigate resource allocation including subcarrier and power allocation for LTE-A relay networks under statistical quality of service (QoS) constraints. By dual decomposition, we derive the optimal subcarrier and power allocation strategies to maximize the effective capacity (EC) of the underlying LTE-A relay systems. Characteristics of optimal resource allocation strategies are identified, and a low-complexity suboptimal scheme is developed through optimizing the subcarrier and power allocation individually. Our result suggests that the optimal subcarrier and power allocation strategies depend heavily on the underlying QoS constraint. For example, in the low signal-to-interference-plus-noise (SINR) regime, when there are less stringent QoS constraints, base stations and relay stations tend to allocate all the power to the best available subcarrier. However, as QoS requirements become more stringent, both base stations and relay stations will spread their power over available subcarriers. On the other hand, in the high SINR regime, regardless of the QoS constraints, base stations and relay stations tend to equally allocate power among available subcarriers.

To improve the spectral efficiency of a cognitive radio system, sensing based spectrum sharing (SBSS) technique combines the advantages of spectrum overlay and spectrum underlay. In this paper, we study the performance of SBSS under primary users' (PUs') rate loss constraint. To be specific, efficient algorithms are introduced to find the optimal sensing time and power allocation in both single-carrier and multi-carrier systems. Performance evaluation is conducted to compare the system throughput between SBSS and spectrum overlay techniques. Simulation results suggest that SBSS outperforms spectrum overlay when the transmitter of the secondary user (SUT) is far away from the receiver of the primary user (PUR) in the single-carrier case. In the multi-carrier case, SBSS can achieve 15.2% increase of spectral efficiency over spectrum overlay when all the channel gains are independent and identically distributed.

Future wireless communications will face the dual challenge of supporting large traffic volume while providing reliable service for various kinds of delay-sensitive traffic. In the light of this challenge, this paper investigates the throughput performance of hybrid automatic repeat request (HARQ) systems under finite blocklength constraint. We present a framework to compute the maximum achievable rate with HARQ over the Rayleigh fading channel for a given probability of error. In the proposed framework, the operation of HARQ over the Rayleigh fading channel is modeled as a finite-state Markov chain. The state transition probabilities of the proposed Markov model are estimated from the fading characteristics of the wireless channel as well as the dispersion associated with different channel state sequence realizations. With this framework we are able to link the HARQ throughput performance to the characteristics of the underlying physical channel as well as the system design parameters such as modulation and transmit power. Furthermore, we discuss the relationship between the system throughput, and the number of HARQ rounds. The results show that the required number of HARQ rounds to take full advantage of HARQ depends on the choice of modulation, and varies as a function of the signal-to-noise ratio (SNR).

Future wireless communications will face the dual challenge of supporting large traffic volume while providing reliable service for various kinds of delay-sensitive traffic. In the light of this challenge, this paper investigates the throughput performance of wireless systems under channel coding over finite transport blocks (TBs) with the channel state sequence available only at the receiver. We analyze the performance of a communication scheme with feedback, namely early decoding, where for each codeblock the receiver makes a single decoding attempt at a time determined based on the available channel state information. A finite-state Markov channel (FSMC) is introduced to model the TB-based wireless system where the parameters of the model are linked to the characteristics of the underlying physical channel. The FSMC model is then used to assess the maximum achievable throughput of the early decoding strategy. Numerical results suggest that despite its low computational complexity the proposed scheme significantly reduces the coding latency to achieve rates near the channel capacity.

Future wireless communications will face the dual challenge of supporting large traffic volume while providing reliable service for various kinds of delay-sensitive traffic. In light of this challenge, this paper investigates the throughput performance of wireless communication systems under channel coding over finite transport blocks (TBs) with the channel state sequence available only at the receiver. When we apply coding across multiple TBs, the underlying wireless channel can be effectively modeled as a finite-state Markov chain. By linking the characteristics of the underlying physical channel to the parameters of the Markov chain, we characterize the channel dispersion of the corresponding system. The channel dispersion is then used to assess the coding performance of various communication strategies. We also propose a communication scheme where the receiver determines the decoding time based on the available channel state sequence. Numerical results show that the proposed scheme significantly reduces the coding delay to achieve rates near the channel capacity.

In modern wireless systems such as 3GPP LTE/LTE-Advanced, packets are partitioned into multiple transport blocks where each transport block is a group of resource elements with a common modulation and coding scheme. Accordingly, a transport block is the data unit in the physical layer of modern wireless systems. In this paper, we investigate the throughput performance of modern wireless systems under channel coding over finite transport blocks. When we apply coding over multiple transport blocks, the underlying wireless channel can be effectively modeled as a finite-state discrete-time Markov chain. We link the characteristics of the underlying physical channel to the parameters of the Markov chain, and derive the corresponding “channel dispersion.” The “channel dispersion” is then used to assess the throughput performance of various communication strategies. The results show that for a fixed packet size the system throughput increases with transport block size.

Mobile data traffic is expected to have an exponential growth in the future. In order to meet the challenge as well as the form factor limitation at the base station, 2D “Massive MIMO”, combined with OFDM, has been proposed as one of the enabling technologies to significantly increase the spectral efficiency of a broadband wireless system. In 2D broadband MIMO-OFDM systems, a base station will rely on the spatial information extracted from uplink sounding reference signals to perform downlink MIMO beam-forming. Accordingly, multi-dimensional parameter estimation of a ray-based multipath wireless channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study joint angle and delay estimation for 2D broadband MIMO-OFDM systems and analytically assess its estimation performance. To be specific, a tensor-based standard ESPRIT algorithm is naturally applied to estimate the corresponding 3D channel parameters. Further, we give out the closed-form mean square error expressions for DoA estimation. It is found that the dimensionality of the antenna array at the base station, as well as the implementation of OFDM, play an important role in determining the estimation performance. Simulation is conducted to evaluate the performance of the tensor-based ESPRIT algorithm, and the empirical results match very well with that from the derived analytical expressions. These insights will be useful for designing practical 2D broadband MIMO-OFDM systems in future mobile wireless communications.

Future wireless communication networks will face the dual challenge of supporting large traffic volumes while providing reliable service for heterogeneous traffic types. In order to meet the ever increasing traffic demand, heterogeneous relay network is introduced as an enabling technology for the fourth generation (4G) mobile broadband networks. In this paper, we will investigate optimal power and subcarrier allocation strategies for heterogeneous relay networks under statistical quality of service (QoS) constraints. To be specific, we will characterize the effective capacity of a wireless relay system under QoS constraints. The properties of the optimal resource and subcarrier allocation strategies for delay-sensitive traffic over heterogeneous relay networks will also be identified. Two low complexity resource and subcarrier allocation algorithms will be introduced to optimize the effective capacity. Our results suggest that the optimal power and subcarrier allocation strategies depend heavily on the underlying QoS constraint. In the low signal-to-interference-plus-noise (SINR) regime, when there is no QoS constraint, both base stations and relay nodes will allocate all the transmit power to the best subcarrier. However, as the QoS requirement becomes more stringent, both base stations and relay nodes will spread their transmit power over multiple subcarriers.

Future wireless communications will face the dual challenge of supporting large traffic volume while providing reliable service for various kinds of delay-sensitive traffic. In light of this challenge, this paper investigates the throughput performance of a wireless communication system under channel coding over finite “packets.” When we apply coding over multiple “packets,” the underlying wireless channel can be effectively modeled as a finite-state Markov process. By linking the characteristics of the underlying physical channel to the parameters of the Markov process, we are able to derive the channel dispersion of the corresponding system. The channel dispersion is then used to assess the coding performance of various communication strategies. It is interesting to find that when there is a constraint on the total blocklength, coding over large “packets” will give better performance than that of coding over small “packets”.

Mobile data traffic is expected to have an exponential growth in the future. In order to meet the challenge as well as the form factor limitation on the base station, two-dimensional (2D) “massive MIMO” has been proposed as one of the enabling technologies for future wireless systems. In 2D “massive MIMO” systems, a base station will rely on the uplink sounding signals to figure out the downlink spatial channel information to perform MIMO precoding. Accordingly, direction-of-arrival (DoA) estimation of the underlying three-dimensional (3D) channel at the base station becomes essential for 2D “massive MIMO” systems to realize the predicted capacity gains. In this paper, we will analyze the performance of DoA estimation based on ESPRIT methods and study its impact on the capacity of 2D “massive MIMO” systems. To be specific, for ESPRIT-type algorithms, we will derive the closed-form expressions for the mean square errors of the elevation and azimuth angle estimations. These results will be used to obtain design intuitions for 2D antenna arrays at the base station as well as the capacity of the underlying 2D “massive MIMO” systems.

Wireless multicast for delay-sensitive data is challenging because of the heterogeneity effect where each receiver may experience different packet losses. Fortunately, network coding, a new advanced routing protocol, offers significant advantages over the traditional Automatic Repeat reQuest (ARQ) protocols in that it mitigates the need for retransmission and has the potential to approach the min-cut capacity. Network-coded multicast would be, however, vulnerable to false packet injection attacks, in which the adversary injects bogus packets to prevent receivers from correctly decoding the original data. Without a right defense in place, even a single bogus packet can completely change the decoding outcome. Existing solutions either incur high computation cost or cannot withstand high packet loss. In this paper, we propose a novel scheme to defend against false packet injection attacks on network-coded multicast for delay-sensitive data. Specifically, we propose an efficient authentication mechanism based on null space properties of coded packets, aiming to enable receivers to detect any bogus packets with high probability. We further design an adaptive scheduling algorithm based on the Markov Decision Processes (MDP) to maximize the number of authenticated packets received within a given time constraint. Both analytical and simulation results have been provided to demonstrate the efficacy and efficiency of our proposed scheme.

In this paper, the power optimization of the multicarrier cognitive system underlying the primary network is investigated. We consider the interference coupled cognitive network under individual secondary user's power constraint and primary user's rate constraint. A multicarrier discrete distributed (MCDD) algorithm based on Gibbs sampler is proposed. Although the problem is nonconcave, MCDD is proved to converge to the global optimal solution. To reduce the computational complexity and convergence time, the Gibbs sampler based Lagrangian algorithm (GSLA) is proposed to get a near optimal solution. We also provide simulation results to show the effectiveness of the proposed algorithms.

Network coding offers a promising platform for multicast transmission by approaching its min-cut capacity. However, pushing the network throughput toward this upper bound comes with a sacrifice in delivery delay due to the decoding procedure that requires performing batch of coded packets. Further, in some transmission scenarios where the receivers experience deep fading or unable to collect a full set of the transmitted data, no useful information is recovered. The effect is more severe in the networks where the transmitted information has priority structure with hard deadline constraint due to the limited delivery time and data interdependencies. In this paper, we consider single-hop wireless networks where the transmitter wishes to multicast hard deadline constrained prioritized data to many receivers over lossy channels. We first study the network performance of a variety of transmission techniques, depending on how the transmitter schedules transmission in each time slot. We then propose an adaptive encoding and scheduling technique to maximize the network throughput. To find the optimal transmission scheduling at the presence of the network dynamics, we cast the problem in the framework of Markov Decision Processes (MDP) and use backward induction method to find an optimal solution. We further propose simulation-based algorithm and greedy scheduling technique that obtain high performance with much lower time complexity. Both analytical and simulation results have been provided to corroborate the effectiveness of the proposed techniques.

In order to meet the spectacular growth in mobile data traffic, one of the technologies is heterogeneous network, where small cells overlaid by large cells can be deployed. With such, base stations (BSs) can be of heterogeneous characteristics in bandwidth, capacity, load, backhaul delay, etc. Meanwhile, the traffic of diverse applications of a mobile station (MS) can be of heterogeneous quality of service (QoS) in rate, delay, etc. This paper proposes a framework to solve the problem of how to efficiently deliver heterogeneous traffic with heterogeneous QoS requirements in heterogeneous networks. We formulate optimization problems, to associate traffic flows with different QoS requirements of an MS to multiple BSs, so that QoS requirements can be satisfied, where the BS backhaul conditions are taken into account. Algorithms are proposed based on analysis. Numerical results show our framework can provide more efficiency in delay stringent and delay astringent traffic delivery.

Energy-efficient communication becomes increasingly important for wireless communication systems partially due to the fact that the improvement in battery technology is much slower than the increase for processing power and energy consumption of electronic devices. On the other hand, the telecommunication industry predicts that the mobile data traffic will double almost every year for the next five years with more than two thirds of the future traffic being video. Since many of the video applications are delay-sensitive, it is extremely important to consider energy-efficient design for delay-sensitive traffics over wireless systems. This paper addresses resource allocation for maximizing the energy-efficiency of a wireless link under statistical quality of service (QoS) constraints. In the energy-efficiency analysis, both circuit power and transmission power of a communication system are taken into account. The joint impact of statistical QoS constraints, transmission power, and spectral bandwidth are considered. The unique globally optimal power allocation scheme is characterized. The intuition of the optimal strategy is obtained by looking at both low signal-to-noise ratio (SNR) regime and high SNR regime.

Multi-user MIMO is the enabling technology for LTE-Advanced systems to meet IMT-Advanced targets. The gain of multi-user MIMO is achieved partially through advanced user-grouping, user-scheduling, and precoding. Traditionally, multiuser MIMO scheduling focuses solely on spectral-efficiency [1]. That is, the scheduler will strike to balance the cell-edge user spectral-efficiency as well as the cell-average spectral-efficiency. Similar to spectral-efficiency, energy-efficiency is becoming increasingly important for wireless communications. The energy efficiency is measured by a classical measure, “throughput per Joule”, while both RF transmit power and device electronic circuit power consumptions are considered. In this paper, an energy-efficient proportional-fair scheduling is proposed for downlink multi-user MIMO systems. To specific, the scheduling algorithm is proposed to balance cell-edge energy-efficiency and the cell-average energy-efficiency. The energy-efficient proportional-fair metric is defined and the optimal power allocation maximizing the performance measure is identified. System level evaluation suggests that multi-user MIMO could improve the energy-efficiency of a wireless communication system significantly.

In this paper, we investigate a typical LTE-Advanced multi-user MIMO system where mobile stations (MSs) feedback limited channel state information using codebook based approaches. A joint optimization of multi-user scheduling, linear transmit beam-forming, and receive combining is conducted at the base station to combat the intra-cell interference created by multi-user MIMO operation. Low complexity coordinated beam-forming schemes based on proportional-fair metric is proposed for jointly designing the beam-forming and receive combining vectors. Feedback schemes complied with LTE-Advanced specification is also introduced for the proposed coordinated beam-forming scheme. System level evaluation is conducted to show that the proposed coordinated beam-forming can help improving the performance of LTE-Advanced systems significantly.

Wireless multicast for delay-sensitive data is challenging because different receivers may experience different packet losses. Network coding offers significant advantages over the traditional Automatic Repeat-reQuest (ARQ) protocols in that it mitigates the need for retransmission and has the potential to approach the min-cut capacity. Network-coded multicast would be, however, vulnerable to false packet injection attacks, in which the adversary injects bogus packets to prevent receivers from correctly decoding the original data. Without a right defense in place, even a single bogus packet can completely change the decoding outcome. Existing solutions either incur high computation cost or cannot withstand high packet loss. In this paper, we propose a novel scheme to defend against false packet injection attacks on network-coded multicast for delay-sensitive data. Specifically, we propose an efficient authentication mechanism based on null space properties of coded packets, aiming to enable receivers to detect any bogus packets with high probability. We further design an adaptive scheduling algorithm based on Markov Decision Processes (MDP) to maximize the number of authenticated packets that can be received within a given time constraint. Both analytical and simulation results have been provided to demonstrate the efficacy and efficiency of our proposed scheme.

In this paper, we discuss the hybrid capacity region of a generic multicarrier multilayer broadcast and unicast cellular system with independent channel coding over subcarriers. In particular, we analytically derive the capacity region and provide conditions to achieve its boundary. The simulation results show that the hybrid capacity regions are considerably higher than those of the traditional time division multiplexing scheme.

This paper proposes a new scheme for cooperative hybrid network resource allocation. Different from the traditional cognitive radio networks that aim to utilize the spectrum holes (such as white space in TV spectrum) in an uncoordinated way, this paper proposes an OFDMA based collaborative hybrid network. We study the joint resource allocation problem for both the primary users and the secondary users. Results show that by cooperatively allocating resources in the primary network and the secondary network, we can achieve higher spectral efficiency while provide satisfactory admission control and QoS for all users.

Cooperation between the nodes of wireless multihop networks can increase communication reliability, reduce energy consumption, and decrease latency. The possible improvements are even greater when nodes perform mutual information accumulation. In this paper, we investigate resource allocation for unicast and multicast transmission in such networks. Given a network, a source, and a destination, our objective is to minimize end-to-end transmission delay under energy and bandwidth constraints. We provide an algorithm that determines which nodes should participate in forwarding the message and what resources (time, energy, bandwidth) should be allocated to each. Our approach factors into two sub-problems, each of which can be solved efficiently. For any transmission order we show that solving for the optimum resource allocation can be formulated as a linear programming problem. We then show that the transmission order can be improved systematically by swapping nodes based on the solution of the linear program. Solving a sequence of linear programs leads to a locally optimal solution in a very efficient manner. In comparison to the proposed cooperative routing solution, it is observed that conventional shortest path multihop routing typically incurs additional delays and energy expenditures on the order of 70%. Drawing inspiration from this first, centralized, algorithm, we also present two distributed algorithms. These algorithms require only local channel state information. Simulations indicate that they yield solutions about two to five percent less efficient than the centralized algorithm.

The LTE-Advanced (LTE-A) system is currently under development to allow for significantly higher spectral efficiency and data throughput than LTE systems. In a wireless system based on orthogonal frequency division multiplexing (OFDM) with frequency reuse factor one such as LTE, the achievable cell spectral efficiency is often limited by the inter-cell interference or coverage shortage of base stations. Hence in LTE-A, coordinated multi-point (CoMP) transmission/reception (a.k.a. multi-cell MIMO or base station cooperation) and relaying technologies are being introduced to clear these major performance hurdles. In this paper, overall picture of cooperative communication technologies being discussed in LTE-A systems including CoMP and relaying is presented, together with considerations on system design.

The design of wireless communication schemes tailored to real-time traffic requires an analysis framework that goes beyond the traditional criterion of data throughput. This work considers an approach that relates physical system parameters to the queueing performance of wireless links. The potential benefits of multi-rate techniques such as hybrid-ARQ are assessed in the context of delay-sensitive traffic using large deviations. A continuous-time Markov channel model is employed to partition the instantaneous data-rate received at the destination into a finite number of states, each representing a mode of operation of the hybrid-ARQ scheme. The proposed methodology accounts for the correlation of the wireless channel across time, which is computed in terms of level-crossing rates. The tail asymptote governing buffer overflow probabilities at the transmitter is then used to provide a measure of overall performance. This approach leads to a characterization of the effective capacity of the system which, in turn, is applied to quantify the performance advantages of hybrid-ARQ over traditional schemes.

A wireless communication system where a common base station is scheduled to transmit information to multiple mobile users on a time division (TD) basis is considered. The capacity region of this system is found for the two user case and the optimal scheduling scheme is proposed which achieves the boundary of the capacity region. Furthermore, the optimal scheduling scheme is found for the case when the remote mobile users can perform downlink cooperation and the achievable rate region of the downlink cooperative system is characterized. Finally, a simple iterative algorithm is proposed for finding the resource allocation parameters and the scheduling scheme for the cooperative system.

Cooperation between the nodes of wireless multi-hop networks can increase communication reliability, reduce energy consumption, and decrease latency. The possible improvements are even greater when nodes perform mutual-information accumulation, e.g., by using rateless codes. In this paper, we investigate routing problems in such networks. Given a network, a source and a destination, our objective is to minimize end-to-end transmission delay under a sum energy constraint. We provide an algorithm that determines which nodes should participate in forwarding the message and what resources (time, energy, bandwidth) should be allocated to each. Our approach factors into two sub-problems, each of which can be solved efficiently. For any node decoding order we show that solving for the optimum resource allocation can be formulated as a linear problem. We then show that the decoding order can be improved systematically by swapping nodes based on the solution of the linear program. Solving a sequence of linear program leads to a locally optimum solution in a very efficient manner. In comparison to the cooperative routings, it is observed that conventional shortest-path multihop routings incur additional delays and energy expenditures on the order of 70%. Since this initial solution is centralized, requiring full channel state information, we exploit the insights to design two distributed routing algorithms that require only local channel state information. We provide simulations showing that in the same networks the distributed algorithms find routes that are only about 2-5% less efficient than the centralized solution.

The concept of effective capacity offers a novel methodology to investigate the impact that design decisions at the physical layer may have on system performance at the link layer. Assuming a constant flow of incoming data, the effective capacity characterizes the maximum arrival rate that a wireless system can support as a function of its service requirements. Service requirements in this framework are defined in terms of the asymptotic decay-rate of buffer occupancy. This article studies the effective capacity of a class of multiple-antenna wireless systems subject to Rayleigh flat fading. The effective capacity of the multi-antenna Gaussian channel is characterized, and system performance is evaluated in the low signal-to-noise ratio regime. Additional to the power gain of the multiple receive antenna system, we show that there is a statistical gain associated with a multiple transmit antenna system. When the number of transmit and/or receive antennas becomes large, the effective capacity of the system is bounded away from zero, even under very stringent service constraints. This phenomena, which results from channel-hardening, suggests that a multiple-antenna configuration is especially beneficial to delay-sensitive traffic.

In this paper, a multiuser communication system in which wireless users cooperate to transmit information to a base station is considered. The proposed scheme can significantly enlarge the achievable rate region, provided that the wireless connections between pairs of cooperating users are stronger than the connection from every user to the base station. The gains in transmission rate remain substantial even when the channel phase information is only available at the receivers, not at the transmitters. In the proposed scheme, a transmission period is divided into two time intervals. During the first time interval, wireless users send data to the base station and to the neighboring users simultaneously using a broadcast channel paradigm. During the second time interval, the users cooperate to transmit information to the base station. The achievable rate region corresponding to this paradigm is characterized under a random phase channel model for a two-user system. Results are then generalized to a multiple-user scenario. For fixed system parameters, the achievable rate region is strictly larger than that of the traditional multiple-access channel, thereby allowing a fair distribution of the wireless resources among users. Numerical analysis suggests that cooperating with a single partner is enough to achieve most of the benefits associated with cooperation.

A wireless communication system in which multiple users cooperate to transmit information to a common destination is considered. The traffic generated by the users is subject to a stringent quality of service requirement, which is defined in terms of the asymptotic decay-rate of buffer occupancy. The performance of this communication system is analyzed, and the corresponding achievable rate-region for the two-user scenario is identified. A simple user-cooperation scheme that improves performance is proposed. This cooperative scheme is shown to significantly enlarge the achievable rate-region of the service constrained communication system, provided that the quality of the wireless link between cooperating users is better than the individual connections from the users to the intended destination. Numerical results further indicate that the gains of cooperative strategies can be substantial. This suggests that cooperation allows for a fair distribution of the wireless resources among active users.

Wireless systems offer a unique mixture of connectivity, flexibility, and freedom. It is therefore not surprising that wireless technology is being embraced with increasing vigor. For real-time applications, user satisfaction is closely linked to quantities such as queue length, packet loss probability, and delay. System performance is therefore related to, not only Shannon capacity, but also quality of service (QoS) requirements. This work studies the problem of resource allocation in the context of stringent QoS constraints. The joint impact of spectral bandwidth, power, and code rate is considered. Analytical expressions for the probability of buffer overflow, its associated exponential decay rate, and the effective capacity are obtained. Fundamental performance limits for Markov wireless channel models are identified. It is found that, even with an unlimited power and spectral bandwidth budget, only a finite arrival rate can be supported for a QoS constraint defined in terms of exponential decay rate