Open Radio Access Network (O-RAN) architectures are driving a shift towards modular, software-defined RANs, where virtualization is key for flexible component development. OpenAirInterface (OAI) provide software-based RAN functionalities, complemented by Near-Real-Time RAN Intelligent Controllers (Near-RT RICs) like FlexRIC. However, the performance of these virtualized environments is fundamentally limited by the underlying compute infrastructure. This study establishes a crucial performance baseline by isolating the impact of raw computational constraints within a virtualized O-RAN testbed composed of OAI simulators and the FlexRIC controller. To achieve this, our setup deliberately excludes radio virtualization middleware, ensuring all signal processing and channel emulation to be offloaded to the CPU. We assess the impact of these constraints on the end-user’s Quality of Experience (QoE) by streaming video and audio files to a client within the simulated UE’s network namespace. Our findings reveal that video quality degrades significantly, as reflected in low VMAF, while audio quality holds steady under the same constraints.

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.

Wireless positioning in Non-Line-of-Sight (NLoS) scenarios presents significant challenges due to multipath effects that lead to biased measurements and reduced positioning accuracy. This paper revisits electromagnetic field theory related to diffraction and in the context of wireless positioning and proposes a novel positioning technique that greatly improves accuracy in NLoS environments dominated by diffraction. The method is applied to a critical public safety use case: precisely locating at-risk individuals within buildings, with a particular focus on improving 3D positioning and z-axis accuracy. By leveraging the Geometrical Theory of Diffraction (GTD), the approach introduces an innovative NLoS path length model and a new NLOS positioning technique. Using Fisher information analysis, we establish the conditions required for 3D positioning and derive lower bounds on positioning performance for both 3D and z-axis estimates for the proposed NLOS positioning technique. Additionally, we propose an algorithmic implementation of the proposed NLoS positioning method using non-linear least squares estimation, which we term D-NLS. The positioning performance of our proposed NLOs positioning technique is validated using an extensive ray-tracing simulation. The numerical results highlight the superiority of our approach in outdoor-to-indoor environments, which directly estimates NLoS path lengths and delivers significant performance enhancements over existing methods for both 3D and z-axis positioning scenarios.

This paper presents an analytical analysis of a spread spectrum underlay channel designed to coexist with the 5G New Radio (NR) Orthogonal Frequency Division Multiplexing (OFDM) waveform. This coexistence is intended for scenarios where this underlay channel is utilized by either a 5G base station or users. This analytical investigation plays a crucial role in designing the underlay-5G OFDM waveform and facilitates decisions related to the relative average symbol energy levels of the 5G channels, i.e., Physical Downlink Shared Channel (PDSCH) and underlay, the choice of modulation schemes, and spreading factors based on the target reliability of the channels and the signal-to-noise ratio (SNR) at the receiver. We derive and present the bit error rate (BER) expressions for the PDSCH and the underlay channels. Monte Carlo simulation results show that the proposed BER expressions are highly accurate. The expression for the BER of the PDSCH is generalized for M-QAM modulation schemes, and the BER of the underlay with BPSK/QPSK is presented. Our findings provide insight into the appropriate parameterization of underlay symbol energy level and spreading factors to avoid degradation to the PDSCH channel as a function of the PDSCH modulation scheme and the received SNR.

The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results.

Automated spectrum monitoring necessitates the accurate detection of low probability of intercept (LPI) radio frequency (RF) anomaly signals to identify unwanted interference in wireless networks. However, detecting these unforeseen low-power RF signals is fundamentally challenging due to the scarcity of labeled RF anomaly data. In this paper, we introduce WANDA (Wireless ANomaly Detection Algorithm), an automated framework designed to detect LPI RF anomaly signals in low signal-to-interference ratio (SIR) environments without relying on labeled data. WANDA operates through a two-step process: (i) Information extraction, where a convolutional neural network (CNN) utilizing soft Hirschfeld-Gebelein-Rényi correlation (HGR) as the loss function extracts informative features from RF spectrograms; and (ii) Anomaly detection, where the extracted features are applied to a one-class support vector machine (SVM) classifier to infer RF anomalies. To validate the effectiveness of WANDA, we present a case study focused on detecting unknown Bluetooth signals within the WiFi spectrum using a practical dataset. Experimental results demonstrate that WANDA outperforms other methods in detecting anomaly signals across a range of SIR values (-10 dB to 20 dB).

With the emergence of autonomous vehicles and intelligent transportation systems, vehicular communications will play a significant role in innovating, optimizing and realizing the next generation transportation systems. However, with the rise in these intelligent transport systems, it will be necessary to have flexible, efficient, scalable, and cost-effective vehicular infrastructure in place to support these transportation systems. This work studies the existing 5G NR architectures and radio capabilities, and alternative evolved 5G architectures which can be expanded to V2X communications. A novel V2X architecture with unique characteristics and advanced capabilities is proposed. A Python-based simulator is designed, which models vehicular mobility and signaling and data traffic. All the candidate V2X architectures are studied and evaluated using the simulator. These candidate architectures are evaluated using a variety of relevant metrics including the backhaul bandwidth utilization, IP packet latency, Physical Resource Block (PRB) Utilization efficiency, end-to-end packet transmission ratio. The simulation results clearly demonstrate that the proposed novel V2X architecture provides superior performance compared to other candidate architectures and provides additional benefits of flexibility, scalability, ease of deployment on a massive scale, power efficiency, and low cost.

Open radio access network (O-RAN) enables cooperative signal processing among multiple cells at a centralized O-RAN distributed unit (O-DU). It is a key technology for cellular networks to increase spectrum efficiency. To achieve cooperative signal processing across multiple cells, a new scheduler is needed. Specifically, the scheduler must jointly determine RB allocation, MCS assignment, and beamforming matrices for all users from all the cells that are involved in multi-cell processing. In addition, the scheduler must obtain its scheduling solution within each TTI (i.e., at most 1 ms) to be useful for the frame structure defined by 5G NR. In this paper, we present O-$\mathbf M^{3}$—a real-time scheduler formulti-cellMIMO networks under the O-RAN architecture. O-$\mathbf M^{3}$can meet the stringent timing requirement with joint optimization of beamforming matrices, RB allocation, and MCS assignment among multiple cells. O-$\mathbf M^{3}$is developed through a novel multi-pipeline design that exploits parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is performed for cell-center users to explore MU-MIMO transmission. We implement O-$\mathbf M^{3}$on a commercial off-the-shelf (COTS) GPU. Experimental results show that O-$\mathbf M^{3}$is capable of offering a scheduling solution within 500$\mu \text{s}$for an O-RAN system with 7 O-RAN radio units (O-RUs), 100 users, 100 RBs, and$2\times 8$MIMO. O-$\mathbf M^{3}$can also meet the 1 ms requirement for$2\times 12$MIMO systems. Meanwhile, O-$\mathbf M^{3}$can provide ~40% throughput gain on average through joint transmission across multiple cells.

This paper introduces a novel blind iterative projections algorithm to address challenges associated with codebook-based beamforming in 5G and beyond systems. In contrast to existing methodologies, our proposed algorithm eliminates quantization noise and search space scanning latency. The blind nature of our approach capitalizes on information-bearing symbols, yielding performance close to theoretical expectations. In contrast to the prevalent iterative least squares (LS) algorithms in the literature, our proposal does not necessitate the constant modulus or finite alphabet constraints on the desired signal. This makes our algorithm more general and allows us to demonstrate its stability analytically and capability to perform singular value decomposition (SVD) of the received signal matrix. Our algorithm achieves SVD optimality with lower computational costs than conventional SVD methods. We present an analysis of the Signal to Noise Ratio (SNR) at each iteration, elucidating the impact of batch size and noise variance on the SNR gain at convergence. We apply our proposed algorithm to receive-side hybrid beamforming and show that it offers superior performance compared to various existing approaches documented in the literature.

This paper investigates a bandwidth minimization problem with Age of Information (AoI) constraints—a fundamental problem that has not been studied in AoI research. The problem is of critical importance in bandwidth-limited IoT environment while, at the same time, there is an expectation of AoI requirement on the application side. We present a novel polynomial-time algorithm called Aion that can construct a scheduler to satisfy AoI constraints with strong theoretical guarantee in terms of minimizing required bandwidth. Specifically, we prove that the bandwidth required by Aion is minimum if the AoI constraint vector meets a special mathematical structure calledFractional Consecutively Divisible(FCD). In the general case when the given AoI constraint vector is not FCD, we show that the bandwidth required by Aion is tightly upper bounded by a factor of the minimum. We validate the performance of Aion through a large number of simulations and all results confirm our theoretical findings. The results from this paper lay a foundation for future research on bandwidth minimization with AoI guarantee.

Open Radio Access Network (O-RAN) offers a new paradigm for the design and deployment of future RANs. The unique architecture of O-RAN presents two main challenges when designing a scheduler. First, it is impractical to obtain accurate and full Channel State Information (CSI) due to estimation errors and limited bandwidth of the fronthaul link between Open Radio Unit (O-RU) and Open Distributed Unit (O-DU). Second, the large-scale processing at an O-DU introduces difficulties in meeting the stringent time requirement in O-RAN, especially in the real-time (RT) control loop. To address these challenges, we propose R3—a real-time robust Multi-user, Multiple Input, Multiple Output (MU-MIMO) scheduler for O-RAN. R3 serves as a comprehensive scheduling solution encompassing RB allocation, MCS selection, and beamforming calculation. Most notably, R3 utilizes a limited number of CSI samples to offer probabilistic QoS guarantees. To meet the timing requirements of O-RAN, R3 decomposes the scheduling problem into two distinct sub-problems and integrates them into separate control loops. Moreover, each sub-problem is designed with a parallel structure, utilizing a reduced search space, and implemented on a GPU platform to accelerate the computation time. Experimental results demonstrate that R3 offers competitive throughput performance as the state-of-the-art while simultaneously fulfilling the QoS guarantees. Further, R3 meets the timing requirements of various control loops in O-RAN over a wide range of operating conditions.

For emergency response scenarios like firefighting in urban environments, there is a need to both localize emergency responders inside the building and also support a high bandwidth communication link between the responders and a command-and-control center. The emergency networks for such scenarios can be established with the quick deployment of Unmanned Aerial Vehicles (UAVs). Further, the 3D mobility of UAVs can be leveraged to improve the quality of the wireless link by maneuvering them into advantageous locations. This has motivated recent propagation measurement campaigns to study low-altitude air-to-ground channels in both 5G-sub6 GHz and 5G-mmWave bands. In this paper, we develop a model for the link in a UAV-assisted emergency location and/or communication system. Specifically, given the importance of Line-of-Sight (LoS) links in localization as well as mmWave communication, we derive a closed-form expression for the LoS probability. This probability is parameterized by the UAV base station location, the size of the building, and the size of the window that offers the best propagation path. An expression for coverage probability is also derived. The LoS probability and coverage probabilities derived in this paper can be used to analyze the outdoor UAV-to-indoor propagation environment to determine optimal UAV positioning and the number of UAVs needed to achieve the desired performance of the emergency network.

The detection of unauthorized frequency hopping (FH) signals has several applications in securing the radio frequency spectrum and achieving spectrum awareness in both tactical and cyber-physical systems. However, the blind detection of adversary FH signals is a challenging task, particularly in low signal-to-noise ratio (SNR) regimes, due to the adoption of dynamic hopping patterns. In this study, we propose a cyclo-stationary signal features-based blind FH signal detection scheme to address this challenge. Our proposed scheme consists of two steps: (i) feature extraction, where cyclic features are extracted from the spectral correlation function of the signals, and (ii) feature classification, where the extracted features are associated with ON/OFF detection states using a trained support vector machine (SVM) classifier. We leverage both binary and one-class SVM classifiers to enable adversary FH signal detection with and without pre-existing signal labels. Extensive simulations are conducted to verify the efficacy of the proposed FH signal detection scheme in low SNR regimes. Simulation results also provide insights into the interplay of various system parameters, such as the numbers of cyclic features and emission bandwidth, on the detection performance of the proposed SVM classifiers.

With the recent introduction of electromagnetic meta-surfaces and reconfigurable intelligent surfaces, a paradigm shift is currently taking place in the world of wireless communications and related industries. These new technologies are of great interest as we transition from the 5thgeneration mobile network (5G-NR) towards the 6thgeneration mobile system standard (6G). In this paper, we explore the possibility of using a reconfigurable intelligent surface in order to disrupt the ability of an unintended receiver to geolocate the source of transmitted signals in a 5G-NR communication system. We investigate how the performance of the Multiple Signal Classification (MUSIC) algorithm at the unintended receiver is degraded by correlated reflected signals introduced by a reconfigurable intelligent surface in the wireless channel. We analyze the impact of the direction of arrival, delay, correlation, and strength of the reconfigurable intelligent surface signal with respect to the line-of-sight path from the transmitter to the unintended receiver. An effective method is introduced for defeating direction-finding efforts using dual sets of surface reflections. This novel method is called Geolocation-Probability Reduction using dual Reconfigurable Intelligent Surfaces (GPRIS). We also show that the efficiency of this method is highly dependent on the geometry, that is, the placement of the reconfigurable intelligent surface relative to the unintended receiver and the transmitter.

Pilot contamination poses a critical challenge for channel estimation in dense cell-free (CF) distributed multiple-input multiple-output (CF-DMIMO) wireless networks. State-of-the-art channel estimation schemes require inversion of a high-dimensional channel covariance matrix, which is practically infeasible for dense CF-DMIMO networks owing to the requirement of large storage and high dimensional computational complexity. In this work, we investigate channel estimation problem for a CF-DMIMO network, where both terrestrial and aerial users are jointly supported by distributed access points. We formulate the problem of estimating channel coefficients from the received in-phase/quadrature (I/Q) samples as a non-linear regression problem and propose two deep-learning aided channel estimation schemes for the considered network, namely, deep model-agnostic neural network (DMANN) and deep successive contamination cancellation (DSCC) schemes. Compared to the state-of-the-art channel estimation schemes for CF-DMIMO networks, the proposed schemes (i) tackle the unavoidable pilot contamination issue in dense CF-DMIMO networks while estimating the channel gains for both terrestrial and aerial users; (2) does not require prior knowledge of signal-to-noise ratios; and (3) works well in the presence of non-Gaussian correlated noise. Simulation results demonstrate the effectiveness of the proposed schemes over state-of-the-art channel estimation schemes in various use cases of the CF-DMIMO networks.

Recently, has been recognized that there is a practical limitation with the original notion of Age of Information (AoI) metric in terms of quantifying the freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. In this article, we introduce the notion of AoII+ metric by modifying AoII with practical considerations. Then, we investigate a scheduling problem to minimize AoII+ in an IoT data collection network. We derive a theoretical lower bound for the minimum AoII+. Then, we present Heh—a low-complexity online scheduler to minimize AoII+. The design of Heh is based on the estimation of a novel offline scheduling priority metric without any future knowledge. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes AoII+. Through extensive simulations, we show that the lower bound is very tight and that the AoII+ obtained by Heh is close to optimal.

Cloud Radio Access Network (C-RAN) is a novel centralized architecture for cellular networks. C-RAN can significantly improve spectrum efficiency by performing cooperative signal processing for multiple cells at a centralized baseband unit (BBU) pool. However, a new resource scheduler is needed before we can take advantage of C-RAN's multi-cell processing capability. Under C-RAN architecture, the scheduler must jointly determine RB allocation, MCS assignment, and beamforming matrices for all users under all covering cells. In addition, it is necessary to obtain a scheduling solution within each TTI (at most 1 ms) to be useful for the frame structure defined by 5G NR. In this paper, we present M3—a sub-ms scheduler for multi-cell MIMO networks under C-RAN architecture. M3addresses the stringent timing requirement through a novel multi-pipeline design that exploits parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is for cell-center users to explore MU-MIMO transmission. Experimental results show that M3is capable of offering a scheduling solution within 1 ms for 7 remote radio heads (RRHs), 100 users, 100 RBs, and 2×12 MIMO. Meanwhile, M3provides ~40%. throughput gain on average by employing joint transmission.

Recently, it has been recognized that there is a serious limitation with the original Age of Information (AoI) metric in terms of quantifying true freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. By further refining this new metric with practical considerations, we introduce Age of Outdated Information (Ao2I) metric. In this paper, we investigate a scheduling problem for minimizing Ao2I in an IoT data collection network. We derive a theoretical lower bound for the minimum Ao2I that any scheduler can achieve. Then we present Heh—a low-complexity online scheduler. The design of Heh is based on the estimation of a novel offline scheduling priority metric in the absence of knowledge of the future. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes Ao2I. Through extensive simulations, we show that the lower bound is very tight and that the Ao2I obtained by Heh is close-to-optimal.

With the increasing diversity in the requirement of wireless services with guaranteed Quality of Service (QoS), radio access network (RAN) slicing becomes an important aspect in implementation of next-generation wireless systems (5G). RAN slicing involves the division of network resources into many logical segments where each segment has specific QoS and can serve users of the mobile virtual network operator (MVNO) with these requirements. This allows the network operator (NO) to provide service to multiple MVNOs each with different service requirements. Efficient allocation of the available resources to slices becomes vital in determining the number of users and therefore, the number of MVNOs that a NO can support. In this work, we study the problem of the modulation and coding scheme (MCS)-aware RAN slicing (MaRS) in the context of a wireless system having MVNOs which have users with minimum data rate requirement. Channel quality indicator (CQI) report sent from each user in the network determines the MCS selected, which in turn determines the achievable data rate. But the channel conditions might not remain the same for the entire duration of a user being served. For this reason, we consider the channel conditions to be dynamic where the choice of the MCS level varies at each time instant. We model the MaRS problem as a NonLinear Programming problem and show that it is NP-Hard. Next, we propose a solution based on the greedy algorithm paradigm. We then develop an upper performance bound for this problem and finally evaluate the performance of the proposed solution by comparing it against the upper bound under various channel and network configurations.

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.

Emerging 802.11p vehicle-to-vehicle (V2V) networks rely on periodic Basic Safety Messages (BSMs) to disseminate time-sensitive safety-critical information, such as vehicle position, speed, and heading - that enables several safety applications and has the potential to improve on-road safety. Due to mobility, lack of global-knowledge and limited communication resources, designing an optimal BSM broadcast rate-control protocol is challenging. Recently, minimizing Age of Information (AoI) has gained momentum in designing BSM broadcast rate-control protocols. In this paper, we show that minimizing AoI solely does not always improve the safety of V2V networks. Specifically, we propose a novel metric, termed Trackability-aware Age of Information TAoI, that in addition to AoI, takes into account the self risk assessment of vehicles, quantified in terms of self tracking error (self-TE) - which provides an indication of collision risk posed by the vehicle. Self-TE is defined as the difference between the actual location of a certain vehicle and its self-estimated location. Our extensive experiments, based on realistic SUMO traffic traces on top of ns-3 simulator, demonstrate that TAoI based rate-protocol significantly outperforms baseline AoI based rate protocol and default 10 Hz broadcast rate in terms of safety performance, i.e., collision risk, in all considered V2V settings.

Due to ease-of-deployment, autonomous control and low cost, unmanned aerial vehicles (UAVs), as fixed aerial base stations, are increasingly being used as relays to collect time-sensitive information (i.e., status updates) from IoT devices and deliver it to the nearby terrestrial base station (TBS), where the information gets processed. In order to ensure timely delivery of information to the TBS (from all IoT devices), optimal scheduling of time-sensitive information over two hop UAV-relayed IoT networks (i.e., IoT device to the UAV [hop 1], and UAV to the TBS [hop 2]) becomes a critical challenge. To address this, we propose scheduling policies for Age of Information (AoI) minimization in such two-hop UAV-relayed IoT networks. To this end, we present a low-complexity MAF-MAD scheduler, that employs Maximum AoI First (MAF) policy for sampling of IoT devices at UAV (hop 1) and Maximum AoI Difference (MAD) policy for updating sampled packets from UAV to the TBS (hop 2). We show that MAF-MAD is the optimal scheduler under ideal conditions, i.e., error-free channels and generate-at-will traffic generation at IoT devices. On the contrary, for realistic conditions, we propose a Deep-Q-Networks (DQN) based scheduler. Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i.e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10’s of IoT devices). However, it does not scale well with network size whereas MAF-MAD outperforms all other schedulers under all considered scenarios for larger networks.

Decentralized vehicle-to-everything (V2X) networks (i.e., Mode-4 C-V2X and Mode 2a NR-V2X), rely on periodic Basic Safety Messages (BSMs) to disseminate time-sensitive information (e.g., vehicle position) and has the potential to improve on-road safety. For BSM scheduling, decentralized V2X networks utilize sensing-based semi-persistent scheduling (SPS), where vehicles sense radio resources and select suitable resources for BSM transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). In this paper, we show that such a BSM scheduling (with a fixed RRI) suffers from severe under- and over-utilization of radio resources under varying vehicle traffic scenarios; which severely compromises timely dissemination of BSMs, which in turn leads to increased collision risks. To address this, we extend SPS to accommodate an adaptive RRI, termed as SPS++. Specifically, SPS++ allows each vehicle - (i) to dynamically adjust RRI based on the channel resource availability (by accounting for various vehicle traffic scenarios), and then, (ii) select suitable transmission opportunities for timely BSM transmissions at the chosen RRI. Our experiments based on Mode-4 C-V2X standard implemented using the ns-3 simulator show that SPS++ outperforms SPS by at least 50% in terms of improved on-road safety performance, in all considered simulation scenarios.

The Age of Information (AoI) is a key metric to measure the freshness of information for IoT applications. Most of the existing analytical models for AoI are overly idealistic and do not capture state-of-the-art transmission technologies such as 5G as well as channel dynamics in both frequency and time domains. In this article, we present Kronos, a real-time 5G-compliant scheduler that minimizes AoI for IoT data collection. Kronos is designed to cope with highly dynamic channel conditions. Its main function is to perform RB allocation and to select the modulation and coding scheme for each source node based on channel conditions, with the objective of minimizing long-term AoI. To meet the stringent real-time requirement for 5G, we develop a GPU-based implementation of Kronos on commercial off-the-shelf Nvidia GPUs. Through extensive experimentation, we show that Kronos can find near-optimal solutions under submillisecond time scale. To the best of our knowledge, this is the first real-time AoI scheduler that is 5G compliant.

The spectrum access system (SAS) is being deployed as a key component of the emerging spectrum sharing paradigm to address the spectrum crunch facing the U.S. wireless industry. Ensuring security and privacy of this system against potential attacks is a task of paramount importance. In this article, we first introduce the SAS system, describing its three-tier access model, its functional architecture, and the spectrum management protocol. We then provide a comprehensive analysis of a variety of security and privacy attacks that an SAS is vulnerable to, and discuss their countermeasures. We identify key challenges, formalize threat models, and organize the discussion of SAS security into four categories: 1) SAS server security and privacy; 2) citizens broadband radio service device security; 3) security of environment sensing capability; and 4) communication protocol security. Finally, we suggest future research directions for spectrum management security.

Spectrum sharing alleviates the severe shortage of spectrum in sub-6 GHz frequency bands through the harmonious coexistence of two or more wireless technologies on the same frequency resources. In this work, we study underlay radar-massive MIMO cellular coexistence in LoS/near-LoS channels, where both systems have 3D beamforming capabilities. Using mathematical tools from stochastic geometry, we derive an upper bound on the average interference power at the radar due to the 3D massive MIMO cellular downlink under the worst-case ‘cell-edge beamforming’ conditions. To overcome the technical challenges imposed by asymmetric and arbitrarily large cells, we devise a novel construction in which each Poisson Voronoi (PV) cell is bounded by its circumcircle to bound the effect of the random cell shapes on average interference. Since this model is intractable for further analysis due to the correlation between adjacent PV cells’ shapes and sizes, we propose a tractable nominal interference model, where we model each PV cell as a circular disk with an area equal to the average area of the typical cell. We quantify the gap in the average interference power between these two models and show that the upper bound is tight for realistic deployment parameters. We also compare them with a more practical but intractable MU-MIMO scheduling model to show that our worst-case interference models show the same trends and do not deviate significantly from realistic scheduler models. Under the nominal interference model, we characterize the interference distribution using the dominant interferer approximation by deriving the equi-interference contour expression when the typical receiver uses 3D beamforming. Finally, we use tractable expressions for the interference distribution to characterize radar’s spatial probability of false alarm/detection in a quasi-static target tracking scenario. Our results reveal useful trends in the average interference as a function of the deployment parameters (BS density, exclusion zone radius, antenna height, transmit power of each BS, etc.). We also provide useful system design insights using radar receiver operating characteristic (ROC) curves by applying our analytical results to design the minimum exclusion zone radius in current and future radar-cellular spectrum sharing scenarios.

As several new spectrum bands are opening up for shared use, a new paradigm of Diverse Band-aware Dynamic Spectrum Access (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in multiple bands, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios.

Vehicle-to-Vehicle (V2V) communication networks enable safety applications via periodic broadcast of Basic Safety Messages (BSMs) or safety beacons. Beacons include time-critical information such as sender vehicle's location, speed and direction. The vehicle density may be very high in certain scenarios and such V2V networks suffer from channel congestion and undesirable level of packet collisions; which in turn may seriously jeopardize safety application reliability and cause collision risky situations. In this work, we perform experimental analysis of safety application reliability (in terms of collision risks), and conclude that there exists a unique beacon rate for which the safety performance is maximized, and this rate is unique for varying vehicle densities. The collision risk of a certain vehicle is computed using a simple kinematics-based model, and is based on tracking error, defined as the difference between vehicle's actual position and the perceived location of that vehicle by its neighbors (via most-recent beacons). Furthermore, we analyze the interconnection between the collision risk and two well-known network performance metrics, Age of Information (AoI) and throughput. Our experimentation shows that AoI has a strong correlation with the collision risk and AoI-optimal beacon rate is similar to the safety-optimal beacon rate, irrespective of the vehicle densities, queuing sizes and disciplines. Whereas throughput works well only under higher vehicle densities.

Nonlinearity of radio frequency components can lead to undesirable effects such as desensitization, cross-modulation and intermodulation. It is especially of concern in wideband receivers in emerging shared spectrum spaces where adjacent channel signals, or blockers, can enter the receiver circuitry and cause third order intermodulation distortion. This paper analyzes the fundamental aspects of communication system performance with receiver nonlinearity. We derive symbol error rate expressions as a function of blocker power levels and receiver nonlinearity for modulated signals with modulated blockers. These expressions enable analyzing dynamic spectrum access performance and devising receiver-cognizant spectrum access systems. Our numerical results show that SNR losses of more than 2 dB can incur in heterogeneous radio environments.

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.

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, we consider an underlay radar- massive MIMO spectrum sharing scenario in which massive MIMO base stations (BSs) with elevation beamforming capabilities are allowed to operate outside a circular exclusion zone centered at the radar. Modeling the locations of the massive MIMO BSs as a homogeneous Poisson point process (PPP), we derive an analytical expression for a tight upper bound on the average interference at the radar due to cellular transmissions. The challenge lies in bounding the worst-case elevation angle for each massive MIMO BS, for which we devise a novel construction based on the circumradius distribution of a typical Poisson-Voronoi (PV) cell. While these worst-case elevation angles are correlated for neighboring BSs due to the structure of the PV tessellation, it does not explicitly appear in our analysis because of our focus on the average interference. We also provide an estimate of the nominal average interference by approximating each cell as a circle with area equal to the average area of the typical cell. Using these results, we demonstrate that the gap between the two results remains approximately constant with respect to the exclusion zone radius. Our analysis reveals useful trends in average interference power, as a function of key deployment parameters such as radar/BS antenna heights, number of antenna elements per radar/BS, BS density, and exclusion zone radius.

The 5.9 GHz spectrum band is proposed for vehicular communications using Dedicated Short Range Communications (DSRC), but this band may need to be shared with unlicensed Wi-Fi devices. A recent Federal Communications Commission (FCC)'s Notice of Proposed Rulemaking (NPRM) outlines two interference mitigation techniques for spectrum coexistence: Detect and Vacate and Re-channelization. A major technical challenge of Re-channelization is that DSRC may experience harmful or a reduced functionality because of adjacent channel interference from Wi-Fi transmitters. We therefore conducted Wi-Fi/DSRC adjacent channel interference experiments to evaluate the significance of Wi-Fi signals adjacent to DSRC transmissions. Our measurements show a significant degradation of DSRC performance from 802.11ac adjacent channel interference only for certain scenarios where the distances between the Wi-Fi transmitter to DSRC receiver is 15 m or below and the distances between the DSRC transmitter to receiver is 300m or higher. However, there is no severe effect when the DSRC transmit power is at the standard 33 dBm level, even considering the recommended fading margin of 5-10 dB.

Spectrum regulators and stakeholders from the wireless industry and Intelligent Transportation System (ITS) communities are exploring the use of the 5.9 GHz band for the dissemination of basic safety messages. Dedicated Short Range Communications (DSRC) sends out these messages at a constant rate of 10 Hz and packet collisions occur in dense vehicular environments. In this paper, we propose a priority-based dynamic beaconing scheme. The scheme determines a higher beacon transmission rate for vehicles that are at a higher risk of collision. Two risk based beacon rate protocols are evaluated in our ns-3 simulator, one that adapts the beacon rate between 1 and 10 Hz, and another between 1 and 20 Hz. This improves the packet delivery ratio (PDR) performance by up to 45% in congested environments using the 1-10 Hz adaptive beacon rate protocol and by 38% using the 1-20 Hz adaptive scheme. The simulation results also show that the likelihood of a vehicle collision due to missed packets decreases by up to 77% in a three lane dense highway scenario with 160 vehicles operating at different speeds.

Modern wireless standards such as Long-Term Evolution (LTE) and 5G New Radio (5G NR) use pilot-aided SINR estimates to adapt the modulation and coding scheme (MCS) and transmission mode of data blocks, to fully utilize the channel capacity. However, when interference is localized exclusively on non-pilot resources, pilot-aided SINR estimates become inaccurate. We show that this leads to congestion due to retransmissions, and in the worst case, outage due to very high block error rate (BLER). We demonstrate this behavior through numerical as well as experimental results with the 4G LTE downlink, which show high BLER and significant throughput detriment in the presence of non-pilot interference (NPI). To provide useful insights on the impact of NPI on low-latency communications, we derive an approximate relation between the retransmission- induced latency and BLER. Our results show that NPI can severely compromise low-latency applications in vehicle-to-vehicle (V2V) communications and 5G NR. We identify robust link adaptation schemes as the key to reliable communications.

This Innovative Practice Full Paper introduces a novel tool for educating electrical engineering students about hardware impairments in wireless communications. A radio frequency (RF) front end is an essential part of a wireless transmitter or receiver. It features analog processing components and data converters which are driven by today's digital communication systems. Advancements in computing and software-defined radio (SDR) technology have enabled shaping waveforms in software and using experimental and easily accessible plug-and-play RF front ends for education, research and development. We use this same technology to teach nonlinear effects of RF front ends and their implications. It uses widely available RF instruments and components and SDR technology-well-established affordable hardware and free open source software-to teach students how to characterize the nonlinearity of RF receivers while providing hands-on experience with SDR tools. We present the hardware, software and procedures of our laboratory session that enable easy reproducibility in other classrooms. We discuss different forms of evaluating the suitability of the new class modules and conclude that it provides a valuable learning experience that bolsters the theory that is typically provided in lectures only.

The primary source of nonlinear distortion in wireless transmitters is the power amplifier (PA). Conventional digital predistortion (DPD) schemes use high- order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is not practical for scaling to tens or hundreds of PAs in massive multiple-input multiple-output (MIMO) systems. There is more than one candidate precoding matrix in a massive MIMO system because of the excess degrees-of- freedom (DoFs), and each precoding matrix requires a different DPD polynomial order to compensate for the PA nonlinearity. This paper proposes a low-order DPD method achieved by exploiting massive DoFs of next-generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero forcing precoding and DPD. Our solution uses a 3rd order polynomial to achieve the same performance as the conventional DPD using an 11th order polynomial for a 100×10 massive MIMO configuration. Experimental results show a 70% reduction in computational complexity, enabling ultra-low latency communications.

In this paper, we propose and evaluate rate-maximizing pilot configurations for Unmanned Aerial Vehicle (UAV) communications employing OFDM waveforms. OFDM relies on pilot symbols for effective communications. We formulate a rate-maximization problem in which the pilot spacing (in the time-frequency resource grid) and power is varied as a function of the time-varying channel statistics. The receiver solves this rate-maximization problem, and the optimal pilot spacing and power are explicitly fed back to the transmitter to adapt to the time-varying channel statistics in an air-to-ground (A2G) environment. We show the enhanced throughput performance of this scheme for UAV communications in sub-6 GHz bands. These performance gains are achieved at the cost of very low computational complexity and feedback requirements, making it attractive for A2G UAV communications in 5G.

RF front-end nonlinearity makes receivers vulnerable to adjacent channel interference that significantly impacts receiver performance. Next generation (5G) wireless networks will see unprecedented diversity across receiver and radio technologies accessing the same band of spectrum in spatio-temporal vicinity. Ensuring adjacent channel co-existence is of prime importance for successful deployment and operations of 5G systems. In this paper, we develop a fundamental framework to analyze the adjacent channel co-existence by quantifying the impact of receiver RF front-end nonlinearity on performance. We develop novel tractable discrete representation of third order intermodulation, cross-modulation, and compressive distortion of the receiver front end to describe adjacent channel interference using unit basis vectors. We further analyze the impact of nonlinearity on receiver performance by evaluating the limits on achievable rate accounting for RF front-end nonlinearity. Based on this analysis we provide a framework to compare disparate receivers by forming generalized metrics. We then use these metrics to quantify the performance detriment for a reference input spectrum. We illustrate the importance of the proposed frameworks and the ensuing rate analysis for reference inputs for adjacent channel co-existence analysis and quantifying receiver performance.

Radio frequency (RF) front-end nonlinearity significantly impairs receiver performance in non-intuitive ways. Receivers are susceptible to harmful adjacent channel interference, especially in next-generation networks with diverse radio access technologies, co-existing in space, time, and frequency. Vulnerabilities of receiver front-ends can have a severe detrimental effect on network performance and spectrum co-existence. In this paper, we propose centralized controller-based receiver-centric framework for spectrum access that accounts for receiver front-end nonlinearity, pre-selector filter bandwidth, and transmitter out-of-band emission characteristics for networks with diverse RF-layer characteristics. Furthermore, we propose computationally efficient algorithms to optimize the receiver-centric framework and examine network level performance. We demonstrate through extensive network simulations that the proposed receiver-centric framework provides substantially higher spectrum efficiency gains over receiver-agnostic spectrum access and improves co-existence in dense and diverse next-generation wireless networks. We further demonstrate through simulations that the proposed algorithms achieve close to optimal solutions for receiver-centric network optimization.

In this demonstration proposal, we present a prototype of a rapidly deployable and cost-effective railroad crossing early warning system integrated with the railway system. Specifically, the proposed demonstration deal with the safety applications based on dedicated short range communications (DSRC) protocol and devices using our different antennas. We will demonstrate the feasibility and advantages of our proposed system, including the antenna design, system deployment, the over-the-air transmission, and the software applications that we developed for the end users1.

Adjacent channel interference caused by intermodulation distortion adversely affects the network operations in next generation heterogeneous and dynamic spectrum access networks. Multitudes of radio access technologies make the receivers susceptible to harmful interference due to nonlinear RF front ends. In this paper we analyze the intermodulation distortion arising from pairwise interactions of adjacent channel signals from a spectrum centric point of view and develop frameworks to ascertain the adjacent channel signals causing interference at a given desired channel. We further propose achievable schemes for interference avoidance and assess the scalability of the next generation Nonlinear Adjacent Channel Interference Networks. We further propose schemes for complete interference protection of incumbents with sensitive receiver requirements from secondary operations in adjacent channels in the spatio-temporal vicinity. This paper presents valuable insights on scalability and schemes for nonlinear adjacent channel interference avoidance in next generation shared spectrum networks.

To cope with increasing demand on wireless services, next-generation wireless systems are expected to use multiple radio access technologies, with different receive and transmit characteristics, operating over the same band of spectrum in a spatial-temporal neighborhood. This will make the RF front-ends susceptible to unprecedented adjacent-channel interference (Ad), which can jeopardize communication performance. In this paper, we propose a novel ACI-aware joint channel and power allocation framework that takes into account the receiver imperfections arising due to (i) imperfect image frequency rejection, and (ii) analog-to-digital converter aliasing. The proposed resource allocation framework aims at minimizing the number of allocated channels and the aggregate power transmitted while satisfying the rate demands of different links in a multi-RAT environment. The results demonstrate the criticality of receiver-characteristic awareness when designing resource allocation schemes for different types of networks. Also, the trade-off between channel allocation and power assignment is explained.

Mission-critical wireless networks are being upgraded to 4G long-term evolution (LTE). These networks require very high reliability and security as well as easy deployment and operation in the field. Wireless communications systems have been vulnerable to jamming, spoofing and other radio frequency (RF) attacks since the early days of analog systems. Although wireless systems have evolved, important security and reliability concerns still exist. This paper presents our methodology for testing 4G LTE operating in harsh signaling environments. We use software-defined radio technology and open-source software to develop a fully configurable protocol-aware interference waveform. We define several test cases that target the entire LTE signal or part of it and evaluate the performance of a mission- critical production LTE system. Our RF experiments show that LTE synchronization signal interference causes significant throughput degradation at low interference power. By dynamically evaluating the performance measurement counters, the k-nearest neighbor classification method can detect the specific RF signaling attack to aid in effective mitigation.

The long-term evolution (LTE) has spread around the globe for deploying 4G cellular networks for commercial use. These days, it is gaining interest for new applications where mobile broadband services can be of benefit to society. Whereas the basic concepts of LTE are well understood, its long-term evolution has just started. New areas of Ramp;amp;D look into operation in unlicensed and shared bands, where new versions of LTE need to coexist with other communication systems and radars. Virginia Tech has developed an LTE testbed with unique features to spur LTE research and education. This pa-per introduces Virginia Tech's LTE testbed, its main features and components, access and configuration mechanisms, and some of the research thrusts that it enables. It is unique in several aspects, including the extensive use of software-defined radio technology, the combination of industry-grade hardware and software-based systems, and the remote access feature for user- defined configurations of experiments and radio frequency paths.

In recent years, there has been great interest from the cellular service providers to use the unlicensed spectrum for their service offerings. On the other hand, existing unlicensed users in these bands (e.g., Wi-Fi in the 5-GHz band) have serious concern that such coexistence will jeopardize their service quality. Although there are some proposals on how to achieve coexistence, they are driven by the service providers and as such there remain many issues and skepticism. In this paper, we take a novel human-centric approach to understand coexistence between Wi-Fi and LTE by focusing on human satisfaction. Through mathematical modeling, problem formulation, and extensive simulations studies, we show that in terms of maximizing total human satisfaction function, there does not appear to be any advantage with the coexistence of unlicensed spectrum for Wi-Fi and LTE under static partitioning of unlicensed spectrum. This finding serves as a powerful counter argument to some LTE service providers' proposal to share the unlicensed spectrum with Wi-Fi through static partitioning. On the other hand, we find that there is a significant improvement in human satisfaction in coexistence between Wi-Fi and LTE under adaptive spectrum partitioning. Since adaptive spectrum partitioning may require a user to change its service provider whenever there is a change among the users, we propose a practical (semi-adaptive) algorithm for implementation without affecting existing users' service providers. Through performance evaluation, we show that the proposed semi-adaptive algorithm is highly competitive.

During the past three decades there has been an increasing focus on the commercialization of technologies developed by academe. Prior to this, research funding by the U.S. Government was skewed toward pure science and mention of commercialization was viewed negatively more often than not. Today's research funding landscape has reversed the negative view of commercialization, with many Requests for Proposals having specific requirements toward commercialization assessments. This change in mindset has dramatically increased the importance of intellectual property (IP) created by academic research. In fact, most universities have Technology Transfer Offices actively seeking commercialization opportunities for IP created through research activities. The impact of commercialization focus has also affected funding beyond universities. Startup efforts leveraging university IP have become commonplace, with the number of funding mechanisms increasing dramatically. Corporate engagement and interest in university research has evolved as well through the creation of consortia that have expanded the interconnection between industry and academe beyond sponsored research projects. The role of university Technology Transfer Offices has had to evolve to accommodate business engagements at startup and corporate levels, leading to a complex IP landscape that involves universities, startups, and large industrial players. The education of future researchers remains the prime directive of universities, while corporate/startup recruiting is, and will continue to be, a critical component to the relationship between business and academe. Through this Point of View contribution, we provide insight into the cycle of university IP and funding that has evolved through increased commercialization focus for research technologies, highlighting and the resulting relationships between academe, startups, and corporations and offer our opinions on how the future of universities and intellectual property should evolve. Two university IP-based startups from our personal experience are then described to provide examples of possible paths for such efforts.

Existing spectrum sharing paradigms have set clear boundaries between the primary and secondary networks. There is either no or very limited node-level cooperation between the primary and secondary networks. In this paper, we develop a new and bold spectrum-sharing paradigm beyond the state of the art for future wireless networks. We explore network cooperation as a new dimension for spectrum sharing between the primary and secondary users. Such network cooperation can be defined as a set of policies under which different degrees of cooperation are to be achieved. The benefits of this paradigm are numerous, as they allow integrating resources from two networks. There are many possible node-level cooperation policies that one can employ under this paradigm. For the purpose of performance study, we consider a specific policy called United cooperation of Primary and Secondary (UPS) networks. UPS allows a complete cooperation between the primary and secondary networks at the node level to relay each other's traffic. As a case study, we consider a problem with the goal of supporting the rate requirement of the primary network traffic while maximizing the throughput of the secondary sessions. For this problem, we develop an optimization model and formulate a combinatorial optimization problem. We also develop an approximation solution based on a piece-wise linearization technique. Simulation results show that UPS offers significantly better throughput performance than that under the interweave paradigm.

Cooperation has become an essential element in spectrum sharing between the primary and secondary networks. A new trend in cooperation is to allow the primary and secondary networks to cooperate on the node level for data forwarding. This new paradigm allows to pool network resources from both the primary and secondary networks and allows users in each network to access a much richer network infrastructure in a combined network. This paper offers an in-depth study of such node-level cooperation by explaining its optimal throughput curve—the maximum achievable throughput for both the primary and secondary users. We formulate the problem as a multicriteria optimization problem with the goal of maximizing the throughput of both the primary and secondary users. Through a novel approach based on weighted Chebyshev norm, we transform the multicriteria optimization problem into a single criteria optimization problem and find a sequence of Pareto-optimal points iteratively. Based on the Pareto-optimal points, we construct the throughput curve and show that it provides an $\varepsilon $ -approximation to the optimal curve. We prove some important properties of the optimal throughput curve. Through a case study, we show that the throughput region (the area under the throughput curve) under node-level cooperation is substantially larger than that when there is no node-level cooperation.

Communications spectrum-that is, the set of electromagnetic frequencies suitable for communications and radar-is a precious resource. Like oil,it is limited and has a far-reaching impact on economic activity and national security. However, unlike oil, it cannot be stored for later use, and it cannot be exported, although it can be reused. Depending on the licensing regime, spectrum can have the characteristics of a private good or a common good. Spectrum can also be locally traded, and it can, by application of scientific creativity, be made more productive. Much research needs to be done in order to achieve the fullest possible productivity.

The use of state-of-the-art 3G cellular CDMA technologies in a utility owned AMI network results in a large amount of control traffic relative to data traffic, increases the average packet delay and hence are not an appropriate choice for smart grid distribution applications. Like the CDG, we consider a utility owned cellular like CDMA network for smart grid distribution applications and classify the distribution smart grid data as scheduled data and random data. Also, we propose SMAC protocol, which changes its mode of operation based on the type of the data being collected to reduce the data collection latency and control overhead when compared to 3G cellular CDMA2000 MAC. The reduction in the data collection latency and control overhead aids in increasing the number of smart meters served by a base station within the periodic data collection interval, which further reduces the number of base stations needed by a utility or reduces the bandwidth needed to collect data from all the smart meters. The reduction in the number of base stations and/or the reduction in the data transmission bandwidth reduces the CAPital EXpenditure CAPEX and OPerational EXpenditure OPEX of the AMI network. The proposed SMAC protocol is analyzed using markov chain, analytical expressions for average throughput and average packet delay are derived, and simulation results are also provided to verify the analysis.

Machine learning based link optimization of wireless communications often relies on past experience, accurate estimation of channel conditions, and theoretical performance models. Typically, theoretical models poorly match given situations, past experiences are limited, and spectrum sensing of noise and channel conditions pose many hurdles. Hence, traditional cognitive radio engines based on genetic algorithms and case based reasoning have faltered, especially when facing new environments. Our approach uses efficient experimental designs to generate an empirical performance model as an alternate to theoretical models. The procedure systematically probes the system by setting a unique combination of input parameters and transmitting a data file across the link. Performance metrics, such as packet error rate and throughput, associated with each row of a response surface methodology (RSM) design estimate simple models of performance. Goals of this research include validating accuracy of empirical models based on type and efficiency of experimental design, using empirical models in place of theoretical models during the optimization process, and comparing success of an experimental design driven decision compared to a benchmark genetic algorithm cognitive radio engine. Over-the-air implementation on software defined radios demonstrated the statistical approach performing within 4% of a traditional genetic algorithm cognitive engine even in cases where the statistical fit of the estimation model is poor.

The Internet of Things (IoT) is envisioned to be a large-scale system that interconnects sensors, mundane objects, and other physical devices via an effective communication infrastructure. Given the heterogeneous and large-scale nature of the IoT, security has emerged as a key challenge. This challenge is further exacerbated by the fact that security solutions for the IoT must account for the limited computational capabilities of the IoT's nodes. That makes enhancing the security at the physical layer level an attractive solution for IoT networks. In this paper, a novel anti- jamming mechanism is proposed to enable a fusion center to defend the IoT from a malicious radio jamming attack. The problem is formulated as a Colonel Blotto game in which the fusion center, acting as defender, aims to detect the jamming attack by increasing the number of bits allocated to certain nodes for reporting their measured interference level, while the jammer aims to disturb the network performance and still be undetected. To solve this game, an algorithm based on fictitious play is proposed to reach the equilibrium of the game. Simulation results show that the proposed mechanism outperforms the mechanism of allocating the available bits in a random manner for two different cases of network architecture.

RF front-end characteristics significantly impact the performance of the receiver. Receivers are vulnerable to harmful adjacent channel interference, especially in shared spectrum environments, which allow a free-style of spectrum access with diverse receiver technologies accessing the same band of spectrum. Channel assignments agnostic to receiver characteristics can have a severe detrimental effect on network level performance of a wireless network. In this paper, we develop a novel dynamic channel assignment framework which accounts for the vulnerabilities posed by RF front end, in particular, receiver pre-selector bandwidth and front-end nonlinearity. We further propose an approximate, heuristic, greedy algorithm for channel assignment which is computationally efficient and provides a near-optimal solution. We demonstrate through simulations that the developed framework of receiver characteristics aware dynamic channel assignment will substantially improve the network-wide data rate, and thereby the overall spectrum efficiency of the wireless network. We further demonstrate that the approximate algorithm provides a near-optimal solution for channel assignment in the statistical sense.

Reducing the size of exclusion zones (EZs) in spectrum sharing is vital for efficient utilization of fallow spectrum as well as for the economic viability of spectrum sharing itself. In this paper, we explore two approaches for reducing the size of EZs. We show that multi-tiered EZs can be used to improve spectrum utilization efficiency by implementing the concept of differential spectrum access hierarchy. Also, we provide quantitative results that show the impact of using a point-to-point mode terrain profile in calculating an EZ's contour. Such a terrain profile captures the effects of propagation losses due to area-specific topography, which are not considered by the F-curves, a common method of calculating an EZ's boundary. Our results indicate that the use of such a terrain profile results in a noticeable decrease in the size of an EZ.

This paper describes the state of the art in software frameworks for executing Software Defined Radio (SDR) components. These frameworks are catalyzing drastic changes in signal processing by enabling software engineers and signal processing engineers to work in tandem on core challenges, such as effectively processing large amounts of data in real-time on limited hardware resources. In addition to a historical perspective of this area, we showcase the REDHAWK framework as an example of a modern SDR framework which provides many facilities for distributed SDR deployment.

When different stakeholders share a common resource, such as the case in spectrum sharing, security and enforcement become critical considerations that affect the welfare of all stakeholders. Recent advances in radio spectrum access technologies, such as cognitive radios, have made spectrum sharing a viable option for significantly improving spectrum utilization efficiency. However, those technologies have also contributed to exacerbating the difficult problems of security and enforcement. In this paper, we review some of the critical security and privacy threats that impact spectrum sharing. We propose a taxonomy for classifying the various threats, and describe representative examples for each threat category. We also discuss threat countermeasures and enforcement techniques, which are discussed in the context of two different approaches: ex ante (preventive) and ex post (punitive) enforcement.

The policy of permanently assigning a frequency band to a single application has led to extremely low utilization of the available spectrum. Cognitive radio, with its ability to be both intelligent and frequency agile, is thought to be one of the prime contenders to provide the necessary capabilities needed for dynamic spectrum access systems. With this in mind, this paper discusses the practical issues inherent to the deployment of spectrum management systems utilizing cognitive radios.

The selection of the appropriate communication technology for different smart grid applications has drawn a great attention in the recent past. In this paper, we propose a Hybrid Spread Spectrum (HSS) based Advanced smart Metering Infrastructure (AMI) that reduces the overhead and latency in data transfer when compared to the use of 3G/4G technologies for smart meter data collection. We present a preliminary PHY and MAC layer design of a HSS based AMI network and evaluate their performance using matlab and NS2 simulations.

This paper provides an overview of how our access to the electromagnetic spectrum has evolved and will continue to expand over time. We first focus on the historical origins of technological and regulatory choices, and provide some insight into how these choices have impacted the efficiency with which we currently utilize the spectrum, and how we can better use it in the future. In turn, we summarize the relevant technologies being discussed in today's standardization and research and development efforts. Finally, we provide a vision for the evolution of spectrum access technologies that, intertwined with progressive regulatory and economic policies, will enable flexible and secure sharing of spectrum to deliver seamless mobility with ubiquitous service for users worldwide.

This paper shows how cognitive radio (CR) can help to optimize system power consumption of multiple input multiple output (MIMO) communication systems. Leveraging results from information theory and capabilities of a CR (e.g., the awareness of the component capabilities and characteristics), a theoretical framework is developed to minimize the system power consumption of MIMO systems while still considering radiated power. This paper mathematically formulates the system power consumption minimization problem under a sum rate constraint for MIMO systems. The impact of channel correlation and partial channel state information at the transmitter is considered. Numerical algorithms are developed to solve the constrained optimization problem. The simulation results show that significant power savings (e.g., up to 75% for a 4 x 4 MIMO system with Class A power amplifiers) can be achieved compared to conventional power allocation schemes. The results also show that the more computationally efficient suboptimal heuristic algorithms can achieve power savings comparable to the exhaustive search algorithm.

Dynamic Spectrum Access technology is now well beyond the developmental stages that it was several years ago. Several prototype devices have been created and have been successfully demonstrated at various venues. Recently the FCC has approved the decision for moving ahead with allowing white space devices to operate in the same frequency bands as TV transmitters. Through rigourous testing the FCC has validated the fundamental concept of these DSA radios showing that they can avoid primary users and cause a minimal amount of interference in trusted wireless environments. However, wireless environments can not always be trusted to contain naturally occuring features. Malicious users may introduce false environments such as increased noise levels or emulated primary user signals, in order to fool the DSA device. These uncontrolled environments must be explored in order to develop and embed proper security protocols within these next-generation devices to prevent future security issues. Even in the case of IEEE 802.11, the commercial world found that the security threats were severely overlooked, causing developers to play catch up while many systems were, and still are, left vulnerable. In this paper, we provide a security analysis on a well established DSA radio developed by Shared Spectrum Company under the DARPA xG program. Our analysis demonstrates the unique security vulnerabilities associated with DSA protocols and expose the extremely low barrier required for a malicious user to exploit them if not security measures are in place. We identify several of these unique vulnerabilities and suggest methods for mitigating them.

We investigate the problem of scalable video multicast in emerging cognitive radio (CR) networks. Although considerable advances have been made in CR research, such important problems have not been well studied. Naturally, 'bandwidth hungry' multimedia applications are excellent candidates for fully capitalizing the potential of CRs. We propose a crosslayer optimization approach to multicast video in CR networks. Specifically, we consider an infrastructure-based CR network collocated with N primary networks and model CR video multicast over the N channels as a mixed integer nonlinear programming (MINLP) problem. The objective is three-fold: to optimize the overall received video quality; to achieve proportional fairness among multicast users; and to keep the interference to primary users below a prescribed threshold. We propose a sequential fixing algorithm and a greedy algorithm to solve the MINLP, while the latter has low complexity and proven optimality gap. Our simulations with MPEG-4 fine grained scalability (FGS) video demonstrate the efficacy and superior performance of the proposed algorithms.

Wireless distributed computing has several unique problems compared with currently well investigated wireless sensor networks. These problems include the impact of channel variation on power allocation, different traffic pattern with higher utilization, and more restricted delay constraints. This paper investigates the impact of communication channel condition on the average execution time of the computing task within wireless distributed computing networks (WDCN). It has been found that the delay performance of wireless distributed computing is influenced by both the average channel condition and the variation of channels. In addition, the impact of channel heterogeneity is also investigated to show the possibility of the optimal workload distribution for energy saving and robustness. Finally, a workload distribution approach combined with a power allocation scheme exploiting the spatial heterogeneity of the channel condition is proposed to balance energy efficiency and robustness.

We investigate the challenging problem of enabling multicast video service in emerging cognitive radio (CR) networks. We propose a cross-layer optimization approach to multicast video in CR networks. Specifically, we model CR video multicast as an optimization problem, while considering important design factors including scalable video coding, video rate control, spectrum sensing, dynamic spectrum access, modulation, scheduling, retransmission, and primary user protection. The objective is to optimize the overall received video quality as well as achieving proportional fairness among multicast users, while keeping the interference to primary users below a prescribed threshold. Although the problem can be solved using advanced optimization techniques, we propose a sequential fixing algorithm and a greedy algorithm with low complexity and proven optimality gap. Our simulations using MPEG-4 fine grained scalability (FGS) demonstrate the efficacy and superior performance of the proposed approach as compared with an alternative equal allocation scheme.

Advanced wireless applications such as sensor networks involve a close interaction between the communication and computation processes that deliver the services under stringent power constraints. Wireless network distributed computing (WNDC) is a potential solution to reducing the power consumption per node as well as that of the network. In WNDC, a computational task is executed among a network of collaborative nodes in a distributed manner as against performing the same task on a single node. In addition to providing power savings, WNDC enables power demand-supply matching that allows for system operation under a constrained power supply such as solar power. This paper presents fundamental power efficiency analysis of WNDC. The conditions for achieving power demand-supply matching and positive network power savings under power and computational latency constraints are derived. The results show the impact of non-linearity in the computational system characteristics and the communication overhead on the power savings.

Spectrum sensing is the very task upon which the entire operation of cognitive radio rests. For cognitive radio to fulfill the potential it offers to solve the spectrum underutilization problem and do so in a reliable and computationally feasible manner, we require a spectrum sensor that detects spectrum holes (i.e., underutilized subbands of the radio spectrum), provides high spectral-resolution capability, estimates the average power in each subband of the spectrum, and identifies the unknown directions of interfering signals. Cyclostationarity is another desirable property that could be used for signal detection and classification. The multitaper method (MTM) for nonparametric spectral estimation accomplishes these tasks accurately, effectively, robustly, and in a computationally feasible manner. The objectives of this paper are to present: 1) tutorial exposition of the MTM, which is expandable to perform space-time processing and time-frequency analysis; 2) cyclostationarity, viewed from the Loeve and Fourier perspectives; and 3) experimental results, using Advanced Television Systems Committee digital television and generic land mobile radio signals, followed by a discussion of the effects of Rayleigh fading.

More than a dozen Wireless @ Virginia Tech faculty are working to address the broad research agenda of cognitive radio and cognitive networks. Our core research team spans the protocol stack from radio and reconfigurable hardware to communications theory to the networking layer. Our work includes new analysis methods and the development of new software architectures and applications, in addition to work on the core concepts and architectures underlying cognitive radios and cognitive networks. This paper describes these contributions and points towards critical future work that remains to fulfill the promise of cognitive radio. We briefly describe the history of work on cognitive radios and networks at Virginia Tech and then discuss our contributions to the core cognitive processing underlying these systems, focusing on our cognitive engine. We also describe developments that support the cognitive engine and advances in radio technology that provide the flexibility desired in a cognitive radio node. We consider securing and verifying cognitive systems and examine the challenges of expanding the cognitive paradigm up the protocol stack to optimize end-to-end network performance. Lastly, we consider the analysis of cognitive systems using game theory and the application of cognitive techniques to problems in dynamic spectrum sharing and control of multiple-input multiple-output radios.

Performance evaluation of cognitive radio (CR) networks is an important problem but has received relatively limited attention from the CR community. Unlike traditional radios, a cognitive radio may change its objectives as radio scenarios vary. Because of the dynamic pairing of objectives and contexts, it is imperative for cognitive radio network designers to have a firm understanding of the interrelationships among goals, performance metrics, utility functions, link/network performance, and operating environments. In this paper, we first overview various performance metrics at the node, network, and application levels. From a game-theoretic viewpoint, we then show that the performance evaluation of cognitive radio networks exhibits the interdependent nature of actions, goals, decisions, observations, and context. We discuss the interrelationships among metrics, utility functions, cognitive engine algorithms, and achieved performance, as well as various testing scenarios. We propose the radio environment map-based scenario-driven testing (REM-SDT) for thorough performance evaluation of cognitive radios. An IEEE 802.22 WRAN cognitive engine testbed is presented to provide further insights into this important problem area.

Direct extensions of distributed greedy interference avoidance (IA) techniques developed for centralized networks to networks with multiple distributed receivers (as in ad hoc networks) are not guaranteed to converge. Motivated by this fact, we develop a waveform adaptation (WA) algorithm framework for IA based on potential game theory. The potential game model ensures the convergence of the designed algorithms in distributed networks and leads to desirable network solutions. Properties of the game model are then exploited to design distributed implementations of the algorithm that involve limited feedback in the network. Finally, variations of IA algorithms including IA with respect to legacy systems and IA with combined transmit-power and WA adaptations are investigated.

Various iterative algorithms for interference avoidance (IA) in networks with co-located receivers, suitable for distributed implementation, have been proposed in the literature. In this paper, the IA problem is cast in a game-theoretic framework and is formulated as a potential game. This formulation accommodates previously proposed algorithms and, in addition, gives us a framework that enables the design of new distributed and convergent algorithms for IA including algorithms with nonidentical utility functions for the users. Two new convergence results for potential games are then derived. The first result establishes the convergence of a class of potential games to the global solution while following best response iterations and when noise is added. The second result establishes the convergence of potential games to the Nash equilibria of the game while following random better response iterations. The first result combined with the potential game formulation allows us to show that for a large class of network scenarios, arbitrarily small noise assures the convergence of best response IA algorithms, including the eigeniterations, to an arbitrarily small neighborhood of the globally optimal signature sequence set. The second result enables the design of reduced feedback mechanisms for IA that converge to desirable solutions.

Cognitive radio (CR) is believed to be an enabling technology for increasing spectrum efficiency. A CR collects spectrum usage information from not only its own spectrum sensing module, but also from peer CRs. The heavy dependence on spectrum knowledge from other CRs requires identification of malicious CR devices that could generate spoofed information. In addition, it also needs to track the users associated with problematic CR devices which unintentionally violate spectrum usage etiquette. The specific emitter identification (SEI) concept is applied to identification of such non-cooperative CR devices. In this paper, second-order cyclic features of OFDM signals are proposed as a means of increasing CR network security and stability through SEI. For this exploratory work, IEEE 802.11a/g signals from different WLAN cards are measured and classified using hidden Markov Models (HMMs).

In this paper, we study the exposed terminal problem in multi-hop wireless networks with log-normal shadowing channels. Assuming that location information is known, we first calculate the success probability for the concurrent transmissions from exposed nodes. We then propose a new MAC protocol which schedules concurrent transmissions in the presence of log- normal shadowing, thus mitigating the exposed terminal problem and increasing network throughput. The performance of the proposed protocol is evaluated with ns-2 simulations, and it is shown to achieve considerable improvements in both end-to-end throughput and delay over the IEEE 802.11 MAC.

In this paper, we show how cognitive radio can help minimize energy consumption of a wireless mobile communication device. We propose an energy optimization framework using cognitive radio for a given quality of service requirement based on the channel and the radio capabilities. The cognitive radio not only adjusts modulation, coding, and radiated power, as with conventional adaptive modulation, but also adjusts component characteristics (e.g., power amplifier characteristics) so that the radio operates with the highest energy efficient possible way. Simulation results show that significant energy savings (up to 75%) can be achieved compared to conventional adaptive modulation. This framework also can be applied to optimize radio operations to achieve additional goals.

This paper presents exact ABER performance analysis for selective decode and forward (SDF) cooperative diversity system with BPSK modulation under Rayleigh fading where the relay has a MAP based receiver and the retransmission is based on log- likelihood-ratio (LLR) threshold. We also derive the optimum LLR threshold that minimizes ABER performance. It is shown that the LLR relay based SDF cooperative diversity system performs better than a SNR threshold based SDF system with lower implementation complexity than lambda-MRC and C-MRC schemes.

This paper develops an efficient analytical framework for evaluating the average bit-error probability (ABER) of bandlimited coherent binary phase shift keying (BPSK) in generalized fast fading channels, where the fading rate is approximately equal to the symbol rate, subject to timing errors and asynchronous cochannel interferers. Selected simulation and computational results are presented that are of interest for outdoor microcellular and macrocellular system studies. Aside from this, our ABER results also serve as lower performance bounds for practical realizable receivers (where ideal coherent detection is difficult to implement) and as an upper performance bound for bandlimited BPSK in slow fading channels where the fading rates are much slower than the symbol rate.

Outage probability and ergodic capacity are important performance measures of communication systems over fading channels. Point-to-point communication systems are known to achieve tremendous improvements in channel capacity when MIMO schemes are applied. In this paper we investigate an efficient Fixed-Talbot algorithm for numerical Laplace inversion and apply it to evaluate the outage probability and ergodic capacity of OSTBC system. The framework developed can be applied to a wide range of fading distributions (including Rice, Nakagami-m, Nakagami-Hoyt and Weibull stochastic channel models) with unequal channel gains and also to independent and non-identically distributed (i.n.d) MIMO channels.

Cognitive Radio (CR) is a promising technology that can alleviate the spectrum shortage problem by enabling unlicensed users equipped with CRs to coexist with incumbent users in licensed spectrum bands while causing no interference to incumbent communications. Spectrum sensing is one of the essential mechanisms of CRs and its operational aspects are being investigated actively. However, the security aspects of spectrum sensing have garnered little attention. In this paper, we identify a threat to spectrum sensing, which we call theprimary user emulation (PUE) attack. In this attack, an adversary's CR transmits signals whose characteristics emulate those of incumbent signals. The highly flexible, software-based air interface of CRs makes such an attack possible. Our investigation shows that a PUE attack can severely interfere with the spectrum sensing process and significantly reduce the channel resources available to legitimate unlicensed users. To counter this threat, we propose a transmitter verification scheme, calledLocDef (localization-based defense), which verifies whether a given signal is that of an incumbent transmitter by estimating its location and observing its signal characteristics. To estimate the location of the signal transmitter, LocDef employs anon-interactive localizationscheme. Our security analysis and simulation results suggest that LocDef is effective in identifying PUE attacks under certain conditions.

The 17 papers in this special issue focus on the theory and applications of cognitive radio.

The biggest challenge faced by dynamic spectrum sharing (SS) systems is the design of SS schemes that do not adversely impact existing legacy systems in the absence of perfect knowledge. We address this issue by developing a framework to evaluate the interference profile at a legacy receiver under different system scenarios which include the hidden node and imperfect-sensing problems. By analyzing the interference distributions and by comparing the two basic approaches to SS - interference-avoidance-based overlay and interference averaging-based underlay - we identify desirable characteristics for SS radio systems.We then leverage this knowledge to motivate the use of a hybrid SS approach that combines the benefits of the two basic approaches and substantially reduces the impact to the legacy system. The advantage provided by this approach is shown to increase with an increase in the bandwidth available to the SS system. In addition, the approach is more robust to imperfect information. The inclusion of log-normal shadowing is shown to further accentuate these performance trends.

This paper presents a joint power control and waveform adaptation algorithm for networks with non-colocated receivers, amenable to a distributed implementation. The proposed algorithm allows users to meet their target signal to interference plus noise ratio (SINK) requirements while reducing the transmit power-levels in the network. The performance of the algorithm is investigated via theoretical-analysis and simulations. It is shown that the joint algorithm results in better solutions than a pure power-control or a pure waveform adaptation algorithm.

This paper investigates the impact of multiple-input multiple-output (MIMO) implementations on both capacity and coverage of reverse link direct spread / code division multiple access (DS/CDMA) cellular systems. For the analytical analysis of the impact on the system, signal-to-interference ratio (SIR) of the reverse link DS/CDMA cellular systems with MIMO implementations is derived, and the outage probability is evaluated based on the derived formula.

This paper investigates the efficacy of MIMO implementations with adaptive spatial diversity in the uplink of direct spread/code division multiple access (DS/CDMA) cellular systems. Analytical framework to analyze both system capacity and coverage is developed based on the evaluation of outage probability of signal-to-interference ratio (SIR). It is shown that MIMO implementations can significantly improves the system performances; however, transmit diversity and receive diversity shows different system performances contrary to their link level bit error rate (BER) performances.

This paper proposes and evaluates a new method for mitigating inter-cell interference. We show that the proposed method results in a minor degradation of block error rate under low traffic loading but results in significant improvements for high traffic loads. We also propose and compare two options for resource re-allocation following allocation conflicts: immediate (synchronous) re-allocation and round-robin re-allocation. Simulation results indicates that round-robin reallocation results in good BLER performance when the traffic load is high.

It has been shown in prior work that when used in multi-hop wireless networks, the 802.11 MAC suffers low throughput performance, especially when the number of hops is large. This paper clarifies the relation between exposed node and interference range, and proposes a location-assisted MAC protocol that schedules concurrent transmissions in a multi-hop wireless network. In the proposed algorithm, after identifying a node as an exposed node, a simple procedure is executed to validate the concurrent transmission of the exposed node (called scheduled transmission). Based on location information, the scheduled transmission is allowed if the current and scheduled transmitters are out of the interference range of each other's target receiver. Simulation results show that the proposed algorithm can effectively improve the throughput of multi-hop wireless networks.

For media streaming in ad hoc networks, service replication has been demonstrated to be a quite effective countermeasure to streaming interruptions caused by fragile paths and dynamic topology. In this paper, we study the problem of joint routing and server selection for double description (DD) video streaming in ad hoc networks. We formulate the task as a combinatorial optimization problem and present tight lower and upper bounds for the achievable distortion. The upper bound provides a feasible solution to the formulated problem. Our extensive numerical results show that the bounds are very close to each other for all the cases studied, indicating the near-global optimality of the derived upper bounding solution. Moreover, we observe significant gains in video quality achieved by the proposed approach over existing server selection schemes. This justifies the importance of jointly considering routing and server selection for optimal MD video streaming

A framework to construct convergent interference avoidance (IA) algorithms in networks with multiple distributed receivers (as in ad hoc networks) based on potential game theory is developed in this paper. This is motivated by the fact that direct extensions of distributed greedy IA techniques for centralized networks to these de-centralized networks do not always lead to convergence. Some channel conditions that lead to non-convergence are also identified in the paper. A waveform adaptation algorithm for IA, designed on the basis of the framework, is then proposed. It is shown that this algorithm leads to a reduction of the interference in the network and also incorporates fairness in the allocation of resources.

Distributed multiple-input-multiple-output system (e.g., inter-cluster communication via cooperating nodes in a wireless network) is a topic of emerging interest. Much of previous studies assume perfect synchronization among cooperating nodes and identically distributed communication links. Such assumptions are rarely valid in practical operating scenarios. This paper develops an efficient analytical framework for computing average bit error rate of a distributed multiple-input-single- output (MISO) space-time coded system with intersymbol interference (due to imperfect location predictions and clock jitters between cooperating nodes, as well as the choice of data pulse shaping filters) over generalized fast fading channels. We show that under certain conditions distributed MISO system can outperform perfectly synchronized single-input-single-output (SISO) system.

Distributed multiple-input-multiple-output system (e.g., inter-cluster communication with cooperating nodes in a wireless sensor network) is a topic of emerging interest. Much of the previous studies in this area, however, assumed perfect synchronization among cooperating nodes and identically distributed communication links. Such assumptions are rarely valid in practical operating scenarios. This paper develops an analytical framework for computing an upper bound on the average bit error rate of a distributed space-time coded system with inter-symbol interference (due to imperfect location predictions and clock jitters between cooperating nodes and choice of data pulse shaping filters) over generalized fast fading channels. As an illustrative example, the performance of 2x1 multiple-input-single-output system that uses distributed orthogonal space-time block coding is presented, although this approach can be readily extended to analyze distributed transmit maximal ratio diversity and other variants of space-time schemes

We consider a two-tier wireless sensor network and focus on the flow routing problem for the upper tier aggregation and forwarding nodes (AFNs). Assuming each AFN is equipped with directional antennas for transmission, we are interested in how to perform flow routing at each node such that the network lifetime is maximized. We present a flow routing algorithm that provably has the following properties: (1) when the average source rate of each AFN is known a priori, the flow routing algorithm is optimal and gives maximum network lifetime performance; (2) when the average source rate of each AFN is unknown but is within a fraction, /spl epsiv/, of an estimated rate value, then the network lifetime given by the proposed flow routing algorithm is no more than 2/spl epsiv//(1-/spl epsiv/) from optimal. As a result, the proposed flow routing algorithm can provide predictable lifetime performance, even when the source bit rate can be time-varying.

Multiple description (MD) coding has a great potential for multimedia communications in wireless ad hoc networks. In this paper, we study the important problem of joint routing and server selection for MD video in ad hoc networks. We take a cross-layer approach to formulate the task as a combinatorial optimization problem and present tight lower and upper bounds for the achievable distortion. The upper bound also provides a feasible solution to the formulated problem. Our extensive numerical results show that the bounds are very close to each other for all the cases studied, indicating the near-global optimality of the derived upper bounding solution. Moreover, we observe significant gains in video quality achieved by the proposed approach over existing server selection schemes. This justifies the importance of jointly considering routing and server selection for optimal MD video streaming in wireless ad hoc networks. The proposed algorithms are computationally efficient and can be easily incorporated into existing routing protocols.

Using analytical approach, we investigate the impact of spatial diversity on the Erlang capacity of reverse link direct sequence code division multiple access (DS/CDMA) system. Several parameters which can affect the capacity are considered: transmit diversity order, spatial receive diversity order, the number of multipaths, fade distribution and arbitrary multipath intensity profile, maximum transmit power, soft-handoff, and traffic distribution with call admission control scheme.

This article investigates the impact of antenna diversity implementation at both mobile handsets and basestations on the reverse link DS/CDMA capacity in a myriad of fading environments. Our numerical results reveal that transmit diversity implementation at mobile handsets is most beneficial in indoor propagation channels (since only a few significant resolvable multipaths are available) with low order antenna diversity at the base-station and in harsh fading environments.

We show that the fixed power, synchronous interference avoidance (IA) scheme of (C. Rose et al, IEEE Trans. on Wireless Comm., vol.1, no.3, p. 415-427, 2002) employing the (greedy) eigen-iteration can be modeled as the recently developed potential game of (D. Monderer et al, Journal of Games and Economic Behavior, vol.14, no.0044, p.124-143, 1996). Motivated by the fact that receivers can make small mistakes, we consider the convergence of the eigen-iteration when noise is added in a manner similar to (P. Anigstein, IEEE Trans. On Inf. Theory vol.49, no.4, 2003). Further, we restrict ourselves to a class of signal environments that we call levelable environments. Applying game-theory, we obtain a convergence result similar to that of the Anigstein method, for levelable environments: arbitrarily small noise assures that the eigen-iteration almost surely converges to a neighborhood of the optimum signature set.

In this paper, we examine the conditions and behavior of several common convergence dynamics from game theory and show how they influence the structure of networks of cognitive radios. We then apply these to previously proposed distributed power control algorithms and describe how they impact network complexity.

This paper develops an analytical framework for characterizing the average symbol error rate and outage performance of a smart antenna system in cellular mobile radio environments. Specifically, the carrier-to-interference ratio statistics with N remaining (uncancelled) "weakest" cochannel interference (CCI) signals from a total of N/sub I/ signals are derived, given that both the desired user signal and the CCI signal amplitudes are subjected to Rayleigh, Rice, Nakagami-m, or Nakagami-q fading. General expressions for the outage probability and the average symbol-error rate performance of different digital modulation schemes in the presence of CCI signals are derived. Selected numerical results are presented to demonstrate the utility of the analysis in assessing the selective interference nulling performance in different fading environments.

In this paper, we present model-based techniques for joint detection of two near equal power co-channel MSK signals. It is shown that an MSK signal is piecewise linear in the parameters of interest and therefore can be represented in the standard linear form. The problem of joint detection is then reduced to one of parameter estimation and this allows us to develop receiver architectures based on the minimum variance and minimum mean square error criterion. Simulation results have shown that model-based techniques provide a performance gain of about 12 dB for equal power signals and about 8-10 dB when the signals are near equal power (SIR/spl sim/1dB) over the conventional correlation receiver.

Current bandwidth-efficient modulated signals are significantly distorted during nonlinear amplification. Memoryless digital predistortion is a cost-effective method to compensate for such distortion caused by a nonlinear amplifier. However, significant frequency-dependent effects in high power, wideband nonlinear amplifiers operating on signals such as multicarrier WCDMA reduce the effectiveness of memoryless predistortion and thus cancellation of memory effects is necessary. In this paper, we have presented a nonlinear tapped delay line (NTDL) predistorter structure with memory compensation as well as indirect learning capability, and demonstrated its performance for a 2-carrier WCDMA signal.

This paper considers the application of minimum mean squared error linear space-time processing in tandem with successive interference cancellation (MMSE-SIC) to the extraction of symbol-asynchronous /spl pi//4-DQPSK signals with RRCOS pulse shaping (35%-75% rolloff) in overloaded environments. The performance is analyzed through simulation in terms of the symbol error rate ranked by cancellation order. The effect of signal excess-bandwidth, adjacent channel interference, the number of users, SNR, and array type are considered. The channel statistics of all users are assumed known.

Analytical expressions for the throughput (in kbit/s) as a function of channel symbol signal-to-noise ratio (E/sub s//N/sub 0/) are derived for the six Bluetooth ACL packets that use automatic repeat request (ARQ). The analysis is exact under the assumptions that the outer CRC code provides perfect error detection and that the channel remains stationary for the duration of each packet. Using an expression for noncoherent correlated (h<0.5) full response FSK signals, numerical results are provided for AWGN and quasi-static Rayleigh fading channels. These curves are an appropriate benchmark against which practical demodulators and custom error control techniques may be compared.

We develop a statistical geometric propagation model for a macrocell mobile environment that provides the statistics of angle-of-arrival (AOA) of the multipath components, which are required to test adaptive array algorithms for cellular applications. This channel model assumes that each multipath component of the propagating signal undergoes only one bounce traveling from the transmitter to the receiver and that scattering objects are located uniformly within a circle around the mobile. This geometrically based single bounce macrocell (GBSBM) channel model provides three important parameters that characterize a channel: the power of the multipath components, the time-of-arrival (TOA) of the components, and the AOA of the components. Using the GBSBM model, we analyze the effect of directional antennas at the base station on the fading envelopes. The level crossing rate of the fading envelope is reduced and the envelope correlation increases significantly if a directional antenna is employed at the base station.

A beamformer-RAKE receiver allows processing of the signal in both the spatial and temporal domain by combining an adaptive antenna array with a RAKE. This can significantly improve the system performance along the reverse link of a wideband code division multiple access (WCDMA) system by providing multiple access interference (MAI) suppression and multipath diversity. We compare the performance of two different beamformer-RAKE receivers that employ the minimum mean square error (MUSE) and the maximum signal to interference and noise ratio (MSINR) criteria to form the beams in the spatial domain. The pilot symbol assisted (PSA) technique and the blind code gated algorithm (CGA) were selected to exploit the aforementioned criteria.

Smart antenna systems have been considered for base station (BS) and mobile station (MS) to improve spectral efficiency and link quality. The newly emerging 3rd generation mobile (W-CDMA and cdma2000) systems are required to support high bit rate services. This type of high bit rate traffic inevitably consumes a significant amount of capacity and causes considerable interference to low data rate users. Under these circumstances, the smart antenna system is a good candidate to mitigate this interference and keep link quality at adequate levels. One of the crucial components, which can be very complex, is the searcher whose functionality is to continuously search for new multipath signals for combining. A spatio-temporal searcher structure is proposed to enhance the detection capability of the multipath traffic channel searcher. This structure basically employs a spatio-temporal signal structure to search for a new signal. The detection and false alarm probabilities of the new and conventional schemes are calculated and numerical examples of mean acquisition time are given.

The next generation CDMA standard, wideband CDMA (W-CDMA), has provision for many performance enhancement options to support high data-rate services. Transmit diversity has been proposed as one mechanism for performance enhancement. This paper presents a real-time implementation of a wideband transmit diversity system. The implementation was carried out to demonstrate transmit diversity in an indoor wireless channel at the handset with a two-element antenna array at 2.05 GHz for a bandwidth of 5 MHz. The diversity algorithm is a closed loop technique and is based on scanning the phase of signal on one antenna relative to the other. Single antenna measurements were also performed to serve as a baseline measure. Measurements were carried out both in line-of-sight (LOS) and in non-LOS (NLOS) environments. The diversity gains when assessed using a cumulative distribution function (CDF), showed gains of 2 dB and 7 dB respectively compared to individual antenna element for the LOS environment (at 1% CDF level). For the NLOS case, the diversity gains were more than 10 dB at the 1% CDF level.

We reformulate several different models of antenna array enabled W-CDMA receivers operating in a frequency selective multipath fading channel. We study the problem of full exploitation of both spatial diversity offered by the antenna array and temporal diversity offered by the RAKE while suppressing the interference.

An antenna array is overloaded when the number of cochannel signals in its operating environment exceeds the number of elements. This paper proposes an iterative joint detection technique, spatially reduced search joint detection (SRSJD), that well approximates the joint maximum likelihood (JML) receiver, while reducing its computational complexity by several orders of magnitude. This complexity reduction is achieved by first exploiting the spatial separation between interfering signals with a linear preprocessing stage, and second, performing iterative joint detection with a reduced-state trellis formed over space instead of time. These novel joint detection trellises are possibly tail-biting and vary in structure from stage to stage. Through simulation, SRSJD is shown to demodulate over 2M synchronous quaternary phase-shift keying signals of zero excess bandwidth with an M element circular array. The channels of all users are assumed known.

Existing adaptive filtering techniques usually attempt to minimize the mean square error (MSE) of some aspect of a received signal, with respect to the desired aspect of that signal. However, adaptive minimization of MSE does not always guarantee minimization of bit error rate (BER). Instead, the probability density function of the received signal can be estimated and used to adaptively determine a solution that minimizes BER. To this end, a new adaptive procedure called the minimum BER estimation (MBE) algorithm has been developed. The MBE is shown to provide, in some cases, a lower BER signal output than traditional MSE-based methods of adaptive filtering.

The convergence behavior of the least squares constant modulus (CM) algorithm in an adaptive beamforming application is examined. It is assumed that the desired signal and the interference are uncorrelated. The improvement in output signal-to-interference ratio (SIR) with each iteration of the algorithm is predicted for several different signal environments. Deterministic results are presented for an environment containing two complete sinusoids. Probabilistic results are presented for a CM desired signal with a CM interferer and with a Gaussian interferer. The asymptotic improvement in output SIR as the output SIR becomes high is also derived. The results of Monte Carlo simulations using sinusoidal, frequency modulation, and quadrature phase-shift keying signals are included to support the derivations.

This paper presents alternate methods to generate bit errors that simulate the effect of a convolutional code on an MPEG-2 sequence. Given the complexity of Viterbi decoders, a fast way to generate bit errors that closely resembles the performance of a convolutional code/Viterbi decoder is desirable in order to minimize the execution time of Monte Carlo simulations. Three methods of generating the bit error sequences are presented in this paper. A simulation of a Viterbi decoder is used as a reference against which the proposed methods are compared. A two-random-variable method is presented that closely matches the performance of the simulation of the Viterbi decoder in both BER and PSNR. A simplification of the two-random-variable method is also presented, where only a single random variable is used, and a deterministic form of the behavior of the convolutional code/Viterbi decoder is used. This single-random-variable method yields results that are similar to the two-random-variable method, with comparable values of BER and PSNR. The statistics of the bit error distribution are sacrificed in exchange for a simplified model and less knowledge of the behavior of the Viterbi decoder. The third method proposed is a simplification of the single-random-variable method, where a single bit error is used to model whole bit error bursts. This method yields inaccurate BER estimates but also PSNR values that are biased but close to the behavior of the convolutional code.

With the introduction of antenna array systems into wireless communication networks comes the need to better understand the spatial characteristics of the channel. Scattering models provide both angle of arrival (AOA) and time of arrival (TOA) statistics of the channel. A number of different scattering models have been proposed in the literature including elliptical and circular models. These models assume that scatterers lie within an elliptical and circular region in space, respectively. In this paper, the joint TOA/AOA, the marginal TOA, and the marginal AOA probability density functions (PDFs) are derived for the elliptical and circular scattering models. These PDFs provide insight into the properties of the spatial wireless channel.

Conventional handoff algorithms are susceptible to varying propagation environments, traffic intensities, and user speeds due to the lack of parameter adaptation. This paper proposes a new class of adaptive handoff algorithms that views the handoff problem as a pattern classification problem. Adaptive direction biasing is proposed to reduce the processing load and improve the cell membership properties. The paper shows that the desired balance among the system characteristics can be achieved by making appropriate design tradeoffs in a pattern classification based handoff framework.

Soft synchronization of direct sequence spread spectrum (DSSS) signals introduces the capability of despreading the signals with improving estimates of the spreading code. The technique presented here exploits the eigenstructure of a frequency-channelized DSSS signal to estimate the code. The estimate improves steadily with time, resulting in an improving estimate of the message signal. This allows commencement of despreading at an early stage when the code-estimate is still imperfect. This paper presents the development of the algorithm for a single user environment under infinite time-average assumptions. The performance of the technique under finite time-average conditions is demonstrated through computer simulations for single user as well as multi-user environments.

Soft synchronization of direct-sequence spread-spectrum (DS/SS) signals introduces the capability of despreading with a continuously improving estimate of the spreading code. The soft synchronization technique presented and demonstrated in this paper exploits the eigenstructure of a frequency-channelized DS/SS signal to estimate the code. The estimate improves steadily as more data is collected, resulting in an improved estimate of the message signal. This allows commencement of despreading at an early stage when the code-estimate is still imperfect. Under infinite-time average assumptions a perfect code estimate can be obtained when the signal is received in arbitrary levels of white background noise. This paper demonstrates the synchronization performance of the technique through simulations under finite time-average conditions for environments with fading, multipath, and multi-user interference. The soft synchronization capability is demonstrated. Results show that the technique can remove frequency offsets on the received signal that are integer multiples of the code repeat rate, and that its performance is not degraded by multipath.

A new technique for soft synchronization of direct-sequence spread-spectrum (DSSS) signals is presented. The technique, referred to as the dominant mode despreading (DMDS) algorithm, exploits the eigenstructure of a frequency-channelized DSSS signal to estimate the spreading code and underlying message sequence of the signal. Unlike other despreading techniques, the estimate of the code and data improves steadily with the number of code repeats. The technique is applicable to arbitrary spreading codes and message sequences and can operate in environments containing arbitrary levels of white background noise, and for signals with arbitrary unknown timing phase or carrier frequency offset. The technique requires the DSSS signal to have a constant-modulus spreading code and unrelated message and code-repeat rates. This paper introduces the basic technique, theoretically analyzes the algorithm to prove convergence under infinite time-average conditions, and demonstrates the algorithm via computer simulation for a single DSSS signal received in the presence of white Gaussian noise.

Cyclostationarity is a common characteristic for many digital and analog communication signals. AMPS signals exhibit this characteristic because of the supervisory audio tone (SAT). The effect of the SAT is to replicate the modulated voice spectrum at multiples of the SAT frequency, and thus, the signal exhibits spectral correlation. A time-dependent adaptive filter can be designed to exploit the cyclostationary characteristic of the AMPS signal and suppress the co-channel interference. The adaptive algorithm used to optimize the filter does not need any reference signal to train the filter and it works in realistic channels. Using digitized signals, a two-stage time-dependent filter improves the CIR from 11 dB (typical at the boundary for a three-cell reuse pattern) to 18 dB (typical at the boundary for a standard seven-cell reuse pattern), even in the presence of strong multipath and Rayleigh fading.

A time dependent adaptive array (TDAA) is a combination of the time dependent optimal filter (or FRESH filter) and an adaptive array. A TDAA exploits spatial, frequency, and time diversities. The idea behind the TDAA is that additional sources of correlated data can be obtained from each spatially separated array element through frequency shifting the data at each antenna element. For some signal types, the TDAA can be blindly adapted by configuring the TDAA as a spectral correlation predictor. The performance of the TDAA configured as a spectral correlation predictor is compared with the least-squares CMA array (LSCMA), the least-squares SCORE (LSSCORE) array, the conventional array with a training signal, the TDAA with a training signal, and a no-diversity system. The test signals used are advanced mobile phone service (AMPS) signals.

The optimal time-dependent receiver (OTDR) is presented and its performance is compared to that of the matched filter receiver. The OTDR, a time-dependent adaptive filter, is shown through simulation to be superior to the matched filter for signals corrupted by cyclostationary interference because it exploits statistical periodicities of the interference.>

A technique is presented for effectively rejecting CDMA interference. No knowledge of the interfering signals is required. The demodulator uses a cyclic Wiener filter (FRESH filter) to decrease the bit error rate, and increase spectral efficiency. In some situations, the bit error rate is reduced by more than two orders of magnitude and capacity is tripled. For despreading and demodulating the signal-of-interest, the demodulator takes on a structure similar to that of fractionally-spaced equalizers. Substantial improvement over the conventional correlation receiver is obtained by exploiting spectral correlation of the signal-of-interest and the multiple access interference even with severe multipath distortion. The computational complexity of the receiver is equivalent to that of conventional equalizers.>