This paper presents an autonomous system that determines optimal sensor placements for 3D digital-twins construction, leveraging multiple robots equipped with Light Detection and Ranging (LiDAR) sensors and employing reinforcement learning. The objective function is designed to encourage exploration by rewarding lower similarity between current- and initial-environmental maps and higher entropy, which indicate different environmental coverage and increased information content in the reconstructed digital-twin, respectively. Reinforcement learning with Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) ensures optimal sensor placement by identifying robot configurations that maximize the reward of the objective function. The presented system provides an adaptive and coordinated sensing solution, overcoming limitations of fixed or heuristic sensor placement methods in various real-world environments.

As cloud applications and 5G use cases increase the demand for additional spectrum, there has emerged interest in spectrum sharing between terrestrial and satellite (SAT) networks. This paper assesses Dynamic Exclusion Zones (DEZ) and Power Control for spectrum coexistence between 5G Networks and SAT Digital Broadcasting Stations (DVB-S2). Relying on a baseband Software Defined Radio (SDR) based emulation on the COSMOS testbed, we propose a spectrum sharing control model that leverages a joint Dynamic Radio Resource Management (RRM) algorithm (Power Control with DEZ) for optimized control. Furthermore, the model combines RRM strategies with artificially generated atmospheric attenuation and path losses for robust control in a standard communication environment. Evaluating our implementation with a ETSI Rural Macro case study, we emphasize our system’s effective power control to preserve DVB-S2 Receiver Packet Error Rate (PER) performance, while maintaining a satisfactory 5G network PER. Against varying weather conditions, our results reveal joint RRM strategies can restore link performance within 73% to 99% of the ideal (no-interference) in a single base station (BS) interference scenario and 82% of the ideal against multiple BS conditions.

Building the next-generation wireless systems that could support services such as the metaverse, digital twins (DTs), and holographic teleportation is challenging to achieve exclusively through incremental advances to conventional wireless technologies like metasurfaces or holographic antennas. While the 6G concept of artificial intelligence (AI)-native networks promises to overcome some of the limitations of existing wireless technologies, current developments of AI-native wireless systems rely mostly on conventional AI tools such as auto-encoders and off-the-shelf artificial neural networks. However, those tools struggle to manage and cope with the complex, nontrivial scenarios faced in real-world wireless environments and the growing quality-of-experience (QoE) requirements of the aforementioned, emerging wireless use cases. In contrast, in this article, we propose to fundamentally revisit the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems. Our envisioned AGI-native wireless systems acquire common sense by exploiting different cognitive abilities such as reasoning and analogy. These abilities in our proposed AGI-native wireless system are mainly founded on three fundamental components: a perception module, a world model, and an action-planning component. Collectively, these three fundamental components enable the four pillars of common sense that include dealing with unforeseen scenarios through horizontal generalizability, capturing intuitive physics, performing analogical reasoning, and filling in the blanks. Toward developing these components, we start by showing how the perception module can be built through abstracting real-world elements into generalizable representations. These representations are then used to create a world model, founded on principles of causality and hyperdimensional (HD) computing. Specifically, we propose a concrete definition of a world model, viewing it as an HD causal vector space that aligns with the intuitive physics of the real world—a cornerstone of common sense. In addition,we discuss how this proposed world model can enable analogical reasoning and manipulation of the abstract representations. Then, we show how the world model can drive an action-planning feature of the AGI-native network. In particular, we propose an intent-driven and objective-driven planning method that can maneuver the AGI-native network to plan its actions. These planning methods are based on brain-inspired frameworks such as integrated information theory and hierarchical abstractions that play a crucial role in enabling human-like decision-making. Next, we explain how an AGI-native network can be further exploited to enable three use cases related to human users and autonomous agent applications: 1) analogical reasoning for the next-generation DTs; 2) synchronized and resilient experiences for cognitive avatars; and 3) brain-level metaverse experiences exemplified by holographic teleportation. Finally, we conclude with a set of recommendations to ignite the quest for AGI-native systems. Ultimately, we envision this article as a roadmap for the next generation of wireless systems beyond 6G.

Given the increasing complexity of integrated circuits, the utilization of machine learning in simulation-based hardware design verification (DV) has become crucial to ensure comprehensive coverage of hard-to-hit states. Our paper proposes a deep deterministic policy gradient (DDPG) algorithm combined with prioritized experience replay (PER) to determine the stimulus settings that result in the highest average FIFO depth in a modified exclusive shared invalid (MESI) cache controller architecture. This architecture includes four FIFOs, each corresponding to a distinct CPU. Through extensive experimentation, DDPG coupled with PER (DDPG-PER) proves to be more effective than DDPG with uniform experience replay in enhancing average FIFO depth and coverage within the DV process. Furthermore, our proposed DDPG-PER framework significantly increases the occurrence of higher FIFO depths, thereby addressing the challenges associated with reaching hard-to-hit states in DV. The proposed DDPG-PER and DDPG algorithms also demonstrate a larger average FIFO depth over four CPUs, requiring considerably less execution time than Bayesian Optimization (BO).

This paper presents the watch-from-sky framework, where multiple unmanned aerial vehicles (UAVs) play four roles, i.e., sensing, data forwarding, computing, and patrolling, for predictive police surveillance. This paper reports a simulation of UAV dispatching using reinforcement learning and distributed ML inference.

A source generates time-stamped update packets that are sent to a server and then forwarded to a monitor. This occurs in the presence of an adversary that can infer information about the source by observing the output process of the server. The server wishes to release updates in a timely way to the monitor but also wishes to minimize the information leaked to the adversary. We analyze the trade-off between the age of information (AoI) and the maximal leakage for systems in which the source generates updates as a Bernoulli process. For a time slotted system in which sending an update requires one slot, we consider three server policies: (1) Memoryless with Bernoulli Thinning (MBT): arriving updates are queued with some probability and head-of-line update is released after a geometric holding time; (2) Deterministic Accumulate-and-Dump (DAD): the most recently generated update (if any) is released after a fixed time; (3) Random Accumulate-and-Dump (RAD): the most recently generated update (if any) is released after a geometric waiting time. We show that for the same maximal leakage rate, the DAD policy achieves lower age compared to the other two policies but is restricted to discrete age-leakage operating points.

Novel transmission schemes, enabled by recent advances in the fields of metamaterial (MTM), leaky-wave antenna (LWA) and directional modulation (DM), are proposed for enhancing the physical layer (PHY) security. MTM-LWAs, which offer compact, integrated, and cost-effective alternatives to the classic phased-array architectures, are particularly of interest for emerging wireless communication systems including Internet-of-Things. The proposed secure schemes are devised to accomplish the functionalities of directional modulation (DM) transmitters for orthogonal frequency-division multiplexing (OFDM) and non-contiguous OFDM transmissions, while enjoying the implementation benefits of MTM-LWAs. Specifically, transmitter architectures based on the idea of time-modulated MTM-LWA have been put forth as a promising solution for PHY security for the first time. The PHY security for the proposed schemes are investigated from the point of view of both passive and active attacks where an adversary aims to decode secret information and feed spurious data to the legitimate receiver, respectively. Numerical simulations reveal that even when the adversary employs sophisticated state-of-the-art deep learning based attacks, the proposed transmission schemes are resistant to these attacks and reliably guarantee system security.

Motivated by traffic analysis attacks based on the packet sizes and timing information in the Internet of Things (IoT) networks, we establish a rigorous event-level differential privacy (DP) model on infinite packet streams. We propose a traffic shaper satisfying a first-come-first-served queuing discipline that outputs traffic dependent on the input using a DP mechanism. We show that in special cases the proposed mechanism recovers existing shapers which standardize the output independently from the input. To find the optimal shapers for given levels of privacy and transmission efficiency, we formulate the constrained problem of minimizing the expected delay per packet and propose using the expected queue size across time as a proxy. We further show that the constrained minimization is a convex program. We demonstrate the effect of shapers on both synthetic data and packet traces from actual IoT devices. The experimental results reveal inherent privacy-overhead tradeoffs: more shaping overhead provides better privacy protection. Under the same privacy level, there is a tradeoff between dummy traffic and delay. When shaping heavier or less bursty traffic, all shapers become more overhead-efficient. We also show that increased traffic from more IoT devices makes guaranteeing event-level privacy easier. The DP shaper offers tunable privacy that is invariant with the change in the input traffic distribution and has an advantage in handling burstiness over traffic-independent shapers. This approach accommodates heterogeneous network conditions and user demands in privacy and overhead.

Prediction of user traffic in cellular networks is one of the promising ways to improve resource utilization among base stations. In this study, we employ deep learning techniques, specifically a long-short-term memory module to forecast cellular traffic. We consider traffic from neighboring cells and other non-cellular traffic-related attributes such as weather, busy period data from open-source API as features to augment the cellular traffic data and improve prediction. Specifically, we augment cellular traffic data from the City of Milan and its surroundings and we perform two types of analyses: (i) a one-step prediction or a point-by-point forecast of traffic and (ii) a trend analysis which is the forecast of traffic over an extended period. We compare the results with existing statistical methods such as auto-regression integrated moving averages (ARIMA) and exponential smoothing and observe gains in the trend analysis by providing the augmented data, whereas the one-step prediction is not much impacted.

Securing the physical components of a city's interdependent critical infrastructure (ICI), such as power, natural gas, and water systems is a challenging task due to their interdependence and a large number of involved sensors. In this article, using a novel integrated state-space model that captures the interdependence, a two-stage cyberattack on an ICI is studied in which the attacker first compromises the ICI's sensors by decoding their messages, and, subsequently, it alters the compromised sensors' data to cause state estimation errors. To thwart such attacks, the administrator of each critical infrastructure (CI) must assign protection levels to the sensors based on their importance in the state estimation process. To capture the interdependence between the attacker and the ICI administrator's actions and analyze their interactions, a Colonel Blotto game framework is proposed. The mixed-strategy Nash equilibrium of this game is derived analytically. At this equilibrium, it is shown that the administrator can strategically randomize between the protection levels of the sensors to deceive the attacker. Simulation results coupled with theoretical analysis show that using the proposed game, the administrator can reduce the state estimation error by at least 50% compared to a nonstrategic approach that assigns protection levels proportional to sensor values.

Ad hoc wireless mesh networks formed by unmanned aerial vehicles (UAVs) equipped with wireless transceivers (access points (APs)) are increasingly being touted as being able to provide a flexible "on-the-fly" communications infrastructure that can collect and transmit sensor data from sensors in remote, wilderness, or disaster-hit areas. Recent advances in the mechanical automation of UAVs have resulted in separable APs and replaceable batteries that can be carried by UAVs and placed at arbitrary locations in the field. These advanced mechanized UAV mesh networks pose interesting questions in terms of the design of the network model and the optimal UAV scheduling algorithms. This paper proposes the design of wireless mesh networks that depend on the mechanized automation (AP separation and battery replacement) capabilities of UAVs, which includes mathematical formulations and heuristic UAV scheduling algorithms for each network model. Through performance evaluation, the proposed design is benchmarked against the theoretical lower bound.

In this paper, the problems of user offloading and resource optimization are jointly addressed to support ultrareliable and low latency communications (URLLC) in HetNets. In particular, a multi-tier network with a single macro base station (MBS) and multiple overlaid small cell base stations (SBSs) is considered that includes users with different latency and reliability constraints. Modeling the latency and reliability constraints of users with probabilistic guarantees, the joint problem of user offloading and resource allocation (JUR) in a URLLC setting is formulated as an optimization problem to minimize the cost of serving users for the MBS. In the considered scheme, SBSs bid to serve URLLC users under their coverage at a given price, and the MBS decides whether to serve each user locally or to offload it to one of the overlaid SBSs. Since the JUR optimization is NP-hard, we propose a low complexity learningbased heuristic method (LHM) which includes a support vector machine-based user association model and a convex resource optimization (CRO) algorithm. To further reduce the delay, we propose an alternating direction method of multipliers (ADMM)- based solution to the CRO problem. Simulation results show that using LHM, the MBS significantly decreases the spectrum access delay for users (by ↑ 93%) as compared to JUR, while also reducing its bandwidth and power costs in serving users (by ↑ 33%) as compared to directly serving users without offloading.

Autonomous connected vehicles (ACVs) rely on intra-vehicle sensors such as camera and radar as well as inter-vehicle communication to operate effectively which exposes them to cyber and physical attacks in which an adversary can manipulate sensor readings and physically control the ACVs. In this paper, a comprehensive control and learning framework is proposed to thwart cyber and physical attacks on ACV networks. First, an optimal safe controller for ACVs is derived to maximize the street traffic flow while minimizing the risk of accidents by optimizing the ACV speed and inter-ACV spacing. It is proven that the proposed controller is robust to physical attacks which aim at making ACV systems unstable. Next, two data injection attack (DIA) detection approaches are proposed to address cyber attacks on sensors and their physical impact on the ACV system. The proposed approaches rely on leveraging the stochastic behavior of the sensor readings and on the use of a multi-armed bandit (MAB) algorithm. It is shown that, collectively, the proposed DIA detection approaches minimize the vulnerability of ACV sensors against cyber attacks while maximizing the ACV system's physical robustness. Simulation results show that the proposed optimal safe controller outperforms the current state of the art controllers by maximizing the robustness of ACVs to physical attacks. The results also show that the proposed DIA detection approaches, compared to Kalman filtering, can improve the security of ACV sensors against cyber attacks and ultimately improve the physical robustness of an ACV system.

Motivated by privacy issues in the Internet of Things (IoT), we generalize a previously proposed privacy-preserving packet obfuscation scheme to guarantee differential privacy. We propose a locally differentially private packet obfuscation mechanism as a defense against packet-size side-channel attacks in IoT networks. We formulate the problem as an optimization over a conditional probability distribution (channel) between the original and obfuscated packet sizes and show that the optimal set of obfuscated packet sizes is a strict subset of the set of original packet sizes. We study the optimal mechanisms for minimizing the (average or min-max) bandwidth overhead subject to a privacy constraint by solving the corresponding (linear or convex) program. We demonstrate our methods on synthetic and real data to illustrate privacy-bandwidth tradeoffs in different settings. Systems with many bandwidth-intensive devices can easily mask low-bandwidth devices. For data collected from actual smart home IoT devices, we show how the packet size distributions become increasingly indistinguishable as the level of privacy protection increases. The proposed mechanism highlights the possibility for bandwidth-constrained users to optimally tune their privacy preferences and trade off privacy with bandwidth.

We study the impact of end-user behavior on user/network association in a HetNet with multiple service providers (SPs). Specifically, we consider the uncertainty in the service guarantees offered by SPs in a HetNet, and use Prospect Theory (PT) to model end-user decision making. We formulate user association with SPs as a multiple leader Stackelberg game where each SP offers a data rate to each user with a certain service guarantee and at a certain price, while the user chooses the best offer among multiple such bids. Using the specific example of a HetNet with one cellular base station and one WiFi access point, we show that when the end users underestimate the advertised service guarantees, then some of the Nash Equilibrium strategies under the Expected Utility Theory (EUT) model become infeasible under PT, and for those Nash Equilibria that are feasible under both EUT and PT, the resulting user utilities are less under PT. We propose resource allocation and bidding mechanisms for the SPs to mitigate these effects.

Advanced persistent threat (APT) attackers apply multiple sophisticated methods to continuously and stealthily steal information from the targeted cloud storage systems and can even induce the storage system to apply a specific defense strategy and attack it accordingly. In this paper, the interactions between an APT attacker and a defender allocating their central processing units (CPUs) over multiple storage devices in a cloud storage system are formulated as a Colonel Blotto game. The Nash equilibria of the CPU allocation game are derived for both symmetric and asymmetric CPUs between the APT attacker and the defender to evaluate how the limited CPU resources, the data storage size and the number of storage devices impact the expected data protection level and the utility of the cloud storage system. A CPU allocation scheme based on “hotbooting” policy hill-climbing that exploits the experiences in similar scenarios to initialize the quality values to accelerate the learning speed is proposed for the defender to achieve the optimal APT defense performance in the dynamic game without being aware of the APT attack model and the data storage model. A hotbooting deep${Q}$-network-based CPU allocation scheme further improves the APT detection performance for the case with a large number of CPUs and storage devices. Simulation results show that our proposed reinforcement learning-based CPU allocation can improve both the data protection level and the utility of the cloud storage system compared with the${Q}$-learning-based CPU allocation against APTs.

Advanced persistent threats (APTs) are a major threat to cyber-security, causing significant financial and privacy losses each year. In this paper, cumulative prospect theory (CPT) is applied to study the interactions between a cyber system and an APT attacker when each of them makes subjective decisions to choose their scan interval and attack interval, respectively. Both the probability distortion effect and the framing effect are applied to model the deviation of subjective decisions of end-users from the objective decisions governed by expected utility theory, under uncertain attack durations in a pure-strategy game and scan interval in a mixed-strategy game. The CPT-based APT detection game incorporates both the probability weighting distortion and the framing effect of the subjective attacker and security agent of the cyber system, rather than discrete decision weights, as in earlier prospect theoretic study of APT detection. The Nash equilibria of the APT detection game are derived, showing that a subjective attacker becomes risk-seeking if the frame of reference for evaluating the utility is large, and becomes risk-averse if the frame of reference for evaluating the utility is small. A policy hill-climbing (PHC) based detection scheme is proposed to increase the policy uncertainty to fool the attacker in the dynamic game, and a “hotbooting” technique that exploits experiences in similar scenarios to initialize the quality values is developed to accelerate the learning speed of PHC-based detection. A practical example of a mobile network is presented to evaluate the performance of the proposed detection strategy. Simulation results show that the proposed strategy can improve detection performance with a higher data protection level and utilities of the cloud in the presence of an attacker compared with a standard Q-learning strategy.

Increasing data traffic demands over wireless spectrum have necessitated spectrum sharing and coexistence between heterogeneous systems such as radar and cellular communications systems. In this context, we specifically investigate the co- channel coexistence between an air traffic control (ATC) radar and a wide area cellular communication (comms) system. We present a comprehensive characterization and analysis of interference caused by the comms system on the ATC radar with respect to multiple parameters such as radar range, protection radius around the radar, and radar antenna elevation angle. The analysis suggests that maintaining a protection radius of $50$ km around the radar will ensure the required INR protection criterion of $-10$ dB at the radar receiver with $\sim 0.9$ probability, even when the radar beam is in the same horizon as the comms BS. Detailed evaluations of the radar target detection performance provide a framework to choose appropriate protection radii around the radar to meet specific performance requirements.

An Advanced Persistent Threat (APT) attacker applies multiple sophisticated methods to continuously and stealthily attack targeted cyber systems. In this paper, the interactions between an APT attacker and a cloud system defender in their allocation of the Central Processing Units (CPUs) over multiple devices are formulated as a Colonel Blotto game (CBG), which models the competition of two players under given resource constraints over multiple battlefields. The Nash equilibria (NEs) of the CBG-based APT defense game are derived for the case with symmetric players and the case with asymmetric players each with different total number of CPUs. The expected data protection level and the utility of the defender are provided for each game at the NE. An APT defense strategy based on the policy hill-climbing (PHC) algorithm is proposed for the defender to achieve the optimal CPU allocation distribution over the devices in the dynamic defense game without being aware of the APT attack model. Simulation results have verified the efficacy of our proposed algorithm, showing that both the data protection level and the utility of the defender are improved compared with the benchmark greedy allocation algorithm.

We exploit human color metamers to send light-modulated messages decipherable by cameras, but camouflaged to human vision. These time-varying messages are concealed in ordinary images and videos. Unlike previous methods which rely on visually obtrusive intensity modulation, embedding with color reduces visible artifacts. The mismatch in human and camera spectral sensitivity creates a unique opportunity for hidden messaging. Each color pixel in an electronic display image is modified by shifting the base color along a particular color gradient. The challenge is to find the set of color gradients that maximizes camera response and minimizes human response. Our approach does not require a priori measurement of these sensitivity curves. We learn an ellipsoidal partitioning of the 6-dimensional space of base colors and color gradients. This partitioning creates metamer sets defined by the base color of each display pixel and the corresponding color gradient for message encoding. We sample from the learned metamer sets to find optimal color steps for arbitrary base colors. Ordinary displays and cameras are used, so there is no need for high speed cameras or displays. Our primary contribution is a method to map pixels in an arbitrary image to metamer pairs for steganographic camera-display messaging.

The number of small wireless devices is rapidly increasing, making the radio channel efficiency in limited geographic areas (individual rooms or buildings) an important metric for MAC protocols. Many of these emerging devices have use-cases that are difficult to satisfy with current hardware solutions and channel access methods; for instance device mobility, small energy reserves, and requirements for low cost and small form factors. However, for most of these applications, such as health care monitoring or sensing, feedback to the radio device is unnecessary and unidirectional communication techniques are not only sufficient, but can also be advantageous. We propose an efficient, reliable technique for unidirectional communication, called Transmit Only (TO), that satisfies these requirements while maintaining packet throughput guarantees and reducing energy consumption. In this paper we will demonstrate the feasibility and performance of this kind of highly asymmetric, transmit-only protocol through theoretical, simulated, and experimental results.

We develop an analytical framework for distribution of popular content in an information centric network (ICN) that is comprised of access ICNs, a transit ICN, and a content provider. Using a generalized Zipf distribution to model content popularity, we devise a game theoretic approach to jointly determine caching and pricing strategies in such an ICN. Under the assumption that the caching cost of the access and transit ICNs is inversely proportional to popularity, we show that the Nash caching strategies in the ICN are 0-1 (all or nothing) strategies. Further, for the case of symmetric access ICNs, we show that the Nash equilibrium is unique and the caching policy (0 or 1) is determined by a threshold on the popularity of the content (reflected by the Zipf probability metric), i.e., all content more popular than the threshold value is cached. We also show that the resulting threshold of the Access and Transit ICNs, as well as all prices can be obtained by a decomposition of the joint caching and pricing problem into two independent caching only and pricing only problems.

Cloud storage is vulnerable to advanced persistent threats (APTs), in which an attacker launches stealthy, continuous, and targeted attacks on storage devices. In this paper, prospect theory (PT) is applied to formulate the interaction between the defender of a cloud storage system and an APT attacker who makes subjective decisions that sometimes deviate from the results of expected utility theory, which is a basis of traditional game theory. In the PT-based cloud storage defense game with pure strategy, the defender chooses a scan interval for each storage device and the subjective APT attacker chooses his or her interval of attack against each device. A mixed-strategy subjective storage defense game is also investigated, in which each subjective defender and APT attacker acts under uncertainty about the action of its opponent. The Nash equilibria (NEs) of both games are derived, showing that the subjective view of an APT attacker can improve the utility of the defender. A Q-learning-based APT defense scheme that the storage defender can apply without being aware of the APT attack model or the subjectivity model of the attacker in the dynamic APT defense game is also proposed. Simulation results show that the proposed defense scheme suppresses the attack motivation of subjective APT attackers and improves the utility of the defender, compared with the benchmark greedy defense strategy.

Cloud storage is vulnerable to Advanced Persistent Threats (APTs), which are stealthy, continuous, well funded and targeted. In this paper, prospect theory is applied to study the interactions between a subjective cloud storage defender and a subjective APT attacker. Two subjective APT games are formulated, in which the defender chooses its interval to scan the storage device and the attacker decides its duration between launching two attacks under uncertain APT attack durations and action of the opponent, respectively. The Nash equilibria of the static subjective APT games are derived. We also study the dynamic APT game and propose a Q-learning based APT defense strategy for cloud storage. Simulation results show that the APT defense benefits from the subjective view of the attacker and the proposed defense strategy can improve detection performance with a higher utility.

In this paper we study how individual sensors can compress their observations in a privacy-preserving manner. We propose a randomized requantization scheme that guarantees local differential privacy, a strong model for privacy in which individual data holders must mask their information before sending it to an untrusted third party. For our approach, the problem becomes an optimization over discrete mem-oryless channels between the sensor observations and their compressed version. We show that for a fixed compression ratio, finding privacy-optimal channel subject to a distortion constraint is a quasiconvex optimization problem that can be solved by the bisection method. Our results indicate interesting tradeoffs between the privacy risk, compression ratio, and utility, or distortion. For example, in the low distortion regime, we can halve the bit rate at little cost in distortion while maintaining the same privacy level. We illustrate our approach for a simple example of privatizing and recompressing lowpass signals and show that it yields better tradeoffs than existing approaches based on noise addition. Our approach may be useful in several privacy-sensitive monitoring applications envisioned for the Internet of Things (IoT).

Embedded screen-camera communication techniques encode information in screen imagery that can be decoded with a camera receiver yet remains unobtrusive to the human observer. These techniques have applications in tagging content on screens similar to QR-code tagging for other objects. This paper characterizes the design space for flicker-free embedded screen-camera communication. In particular, we identify an orthogonal dimension to prior work: spatial content-adaptive encoding, and observe that it is essential to combine multiple dimensions to achieve both high capacity and minimal flicker. From these insights, we develop content-adaptive encoding techniques that exploit visual features such as edges and texture to unobtrusively communicate information. These can then be layered over existing techniques to further boost the capacity. Our experimental results show that there is potential to achieve an average goodput of about 22 kbps, significantly outperforming existing work while remaining flicker-free.

We present a novel method for communicating between a moving camera and an electronic display by embedding and recovering hidden, dynamic information within an image. A small intensity pattern is added to alternate frames of a time-varying display. A handheld camera pointed at the display can receive not only the display image, but also an underlying message. Differencing the camera-captured alternate frames leaves the small intensity pattern, but results in errors due to photometric effects that depend on camera pose. Detecting and robustly decoding the message requires careful photometric modeling for message recovery. The key innovation of our approach is an algorithm that performs simultaneous radiometric calibration and message recovery in one convex optimization problem. By modeling the photometry of the system using a camera-display transfer function (CDTF), we derive an optimal online radiometric calibration (OORC) for robust computational messaging as demonstrated with nine different commercial cameras and displays. The online radiometric calibration algorithms described in this paper significantly reduces message recovery errors, especially for low intensity messages and oblique camera angles.

Active consumer participation is seen as an integral part of the emerging smart grid. Examples include demand-side management programs, incorporation of consumer-owned energy storage or renewable energy units, and active energy trading. However, despite the foreseen technological benefits of such consumer-centric grid features, to date, their widespread adoption in practice remains modest. To shed light on this challenge, this paper explores the potential of prospect theory, a Nobel-prize winning theory, as a decision-making framework that can help understand how risk and uncertainty can impact the decisions of smart grid consumers. After introducing the basic notions of prospect theory, several examples drawn from a number of smart grid applications are developed. These results show that a better understanding of the role of human decision making within the smart grid is paramount for optimizing its operation and expediting the deployment of its various technologies.

Applications on wearable personal imaging devices, or Smart-glasses as they are called, can largely benefit from accurate and energy-efficient recognition of objects that are within the user's view. Existing solutions such as optical or computer vision approaches are too energy intensive, while low-power active radio tags suffer from imprecise orientation estimates. To address this challenge, this paper presents the design, implementation, and evaluation of a radio-optical hybrid system where a radio-optical transmitter, or tag, whose radio-optical beacons are used for accurate relative orientation tracking of tagged objects by a wearable radio-optical receiver. A low-power radio link that conveys identity is used to reduce the battery drain by synchronizing the radio-optical transmitter and receiver so that extremely short optical (infrared) pulses are sufficient for orientation (angle and distance) estimation. Through extensive experiments with our prototype we show that our system can achieve orientation estimates with 1-to-2 degree accuracy and within 40 cm ranging error, with a maximum range of 9 m in typical indoor use cases. With a tag and receiver battery power consumption of 81 μW and 90 mW, respectively, our radio-optical tags and receiver are at least 1.5x energy efficient than prior works in this space.

We evaluate joint caching and pricing strategies among access networks, transit networks and content providers in an Information Centric Network (ICN). Specifically, we develop game theoretic models to study the interaction between the caching and pricing strategies among the above entities in the ICN. We first study static Nash strategies under various conditions and observe that the ICNs always choose 0-1 (all or nothing) caching strategies depending on the relative caching costs and prices at the transit ICN. We then use the insights gained to design dynamic pricing and caching strategies using a repeated game formulation. Using a model where the cost of caching changes as a function of the state of the cache, we study joint caching and pricing strategies for the access networks, transit networks and content providers, when the caching costs change linearly as well as exponentially.

Object recognition on wearable devices using computer vision is too energy intensive and challenging when objects are similar looking, while low-power active radio frequency identification (RFID) systems suffer from imprecise orientation (angle and distance) estimates. To address this challenge, this paper presents a novel radio-optical based recognition system where a radio-optical transmitter, or tag, that emits a beacon whose infra-red (IR) signal strength is used for accurate relative orientation tracking of tagged objects at a wearable radio-optical receiver. A low-power radio link that conveys identity is used to reduce the battery drain by synchronizing the radio- optical transmitter and receiver so that extremely short optical pulses are sufficient for precise orientation estimation. Through extensive experiments with our prototype we show that our system can achieve orientation estimates with 1-2° accuracy and within 40cm ranging error, with a maximum range of 9m in typical indoor use cases. With a tag battery power consumption of 86μW, the radio-optical tags show potential to achieve about half a decade lifetimes.

Jamming games between a cognitive radio enabled secondary user (SU) and a cognitive radio enabled jammer are considered, in which end-user decision making is modeled using prospect theory (PT). More specifically, the interactions between a user and a smart jammer regarding their respective choices of transmit power are formulated as a game under the assumption that the end-user decision making under uncertainty does not follow the traditional objective assumptions stipulated by expected utility theory, but rather follows the subjective deviations specified by PT. Two PT-based static jamming games are formulated to describe how subjective SU and jammer choose their transmit power to maximize their individual signal-to-interference-plus-noise ratio (SINR)-based utilities under uncertainties regarding the opponent's actions and channel states, respectively. The Nash equilibria of the games are presented under various channel models and transmission costs. Moreover, a PT-based dynamic jamming game is presented to investigate the long-term interactions between a subjective and a smart jammer according to a Markov decision process with uncertainty on the SU's future actions and the channel variations. Simulation results show that the subjective view of an SU tends to exaggerate the jamming probabilities and decreases its transmission probability, thus reducing the average SINR. On the other hand, the subjectivity of a jammer tends to reduce its jamming probability, and thus increases the SU throughput.

An anti-jamming communication game between a cognitive radio enabled secondary user (SU) and a cognitive radio enabled jammer is considered, in which end-user decision making is modeled using prospect theory (PT). More specifically, the interactions between a user and a smart jammer (i.e., their respective choices of transmission probability) are formulated as a game under the assumption that end-user decision making under uncertainty does not follow the traditional objective assumptions stipulated by expected utility theory (EUT), but rather follows the subjective deviations specified by PT. Under the assumption that the capacity of the system is governed by the primary user activity, the Nash equilibria of the game are characterized under various conditions and the impact of the players' subjectivity (deviation from EUT behavior) on the SU's throughput is measured. Simulation results show that the subjective view of an SU tends to exaggerate the jamming probabilities and decreases its transmission probability, thus reducing the average throughput. On the other hand, the subjectivity of a jammer tends to reduce its jamming probability and thus increases the SU throughput.

In this paper, the interactions and energy exchange decisions of a number of geographically distributed storage units are studied under decision-making involving end-users. In particular, a noncooperative game is formulated between customer-owned storage units where each storage unit's owner can decide on whether to charge or discharge energy with a given probability so as to maximize a utility that reflects the tradeoff between the monetary transactions from charging/discharging and the penalty from power regulation. Unlike existing game-theoretic works which assume that players make their decisions rationally and objectively, we use the new framework of prospect theory (PT) to explicitly incorporate the users' subjective perceptions of their expected utilities. For the two-player game, we show the existence of a proper mixed Nash equilibrium for both the standard game-theoretic case and the case with PT considerations. Simulation results show that incorporating user behavior via PT reveals several important insights into load management as well as economics of energy storage usage. For instance, the results show that deviations from conventional game theory, as predicted by PT, can lead to undesirable grid loads and revenues thus requiring the power company to revisit its pricing schemes and the customers to reassess their energy storage usage choices.

Ubiquitous light emitting devices and low-cost commercial digital cameras facilitate optical wireless communication system such as visual MIMO where handheld cameras communicate with electronic displays. While intensity-based optical communications are more prevalent in camera-display messaging, we present a novel method that uses modulated light phase for messaging and time-of-flight (ToF) cameras for receivers. With intensity-based methods, light signals can be degraded by reflections and ambient illumination. By comparison, communication using ToF cameras is more robust against challenging lighting conditions. Additionally, the concept of phase messaging can be combined with intensity messaging for a significant data rate advantage. In this work, we design and construct a phase messaging array (PMA), which is the first of its kind, to communicate to a ToF depth camera by manipulating the phase of the depth camera's infrared light signal. The array enables message variation spatially using a plane of infrared light emitting diodes and temporally by varying the induced phase shift. In this manner, the phase messaging array acts as the transmitter by electronically controlling the light signal phase. The ToF camera acts as the receiver by observing and recording a time-varying depth. We show a complete implementation of a 3×3 prototype array with custom hardware and demonstrating average bit accuracy as high as 97.8%. The prototype data rate with this approach is 1 Kbps that can be extended to approximately 10 Mbps.

With the increasing interest in connected vehicles, it is useful to evaluate the capability of delivering large content over a WiFi infrastructure to vehicles. The throughput achieved over WiFi channels can be highly variable and also rapidly degrades as the distance from the access point increases. While this behavior is well understood at the data link layer, the interactions across the various protocol layers (data link and up through the transport layer) and the effect of mobility may reduce the amount of content transferred to the vehicle, as it travels along the roadway. This paper examines the throughput achieved at the TCP layer over a carefully designed outdoor WiFi environment and the interactions across the layers that impact the performance achieved, as a function of the receiver mobility. The experimental studies conducted reveal that impairments over the WiFi link (frame loss, ARQ and increased delay) and the residual loss seen by TCP causes a cascade of duplicate ACKs to be generated. This triggers large congestion window reductions at the sender, leading to a drastic degradation of throughput to the vehicular client. To ensure outdoor WiFi infrastructures have the potential to sustain reasonable downlink throughput for drive-by vehicles, we speculate that there is a need to adapt how WiFi and TCP (as well as mobility protocols) function for such vehicular applications.

Cameras are ubiquitous and increasingly being used not just for capturing images but also for communicating information. For example, the pervasive QR codes can be viewed as communicating a short code to camera-equipped sensors and recent research has explored using screen-to-camera communications for larger data transfers. Such communications could be particularly attractive in pervasive camera based applications, where such camera communications can reuse the existing camera hardware and also leverage from the large pixel array structure for high data-rate communication. While several prototypes have been constructed, the fundamental capacity limits of this novel communication channel in all but the simplest scenarios remains unknown. The visual medium differs from RF in that the information capacity of this channel largely depends on the perspective distortions while multipath becomes negligible. In this paper, we create a model of this communication system to allow predicting the capacity based on receiver perspective (distance and angle to the transmitter). We calibrate and validate this model through lab experiments wherein information is transmitted from a screen and received with a tablet camera. Our capacity estimates indicate that tens of Mbps is possible using a smartphone camera even when the short code on the screen images onto only 15% of the camera frame. Our estimates also indicate that there is room for at least 2.5x improvement in throughput of existing screen - camera communication prototypes.

Base stations with a large number of transmit antennas can potentially serve a large number of users at high rates. However, the receiver processing in the uplink relies on channel estimates, which are known to suffer from pilot interference. In this paper, making use of the similarity of the uplink received signal in CDMA with that of a multi-cell multi-antenna system, we perform a large system analysis when the receiver employs an MMSE filter with a pilot contaminated estimate. We assume a Rayleigh fading channel with different received powers from users. We find the asymptotic signal to interference plus noise ratio (SINR) as the number of antennas and number of users per base station grow larger while maintaining a fixed ratio. Through the SINR expression we explore the scenario where the number of users being served are comparable to the number of antennas at the base station. The SINR explicitly captures the effect of pilot contamination and is found to be the same as that employing a matched filter with a pilot contaminated estimate. We also find the exact expression for the interference suppression obtained using an MMSE filter, which is an important factor when there are a significant number of users in the system as compared to the number of antennas. In a typical set up, in terms of the five percentile SINR, the MMSE filter is shown to provide significant gains over matched filtering and is within 5 dB of MMSE filter with perfect channel estimate. Simulation results for achievable rates are close to large system limits for even a 10-antenna base station with 3 or more users per cell.

Game theoretic models have found widespread use in the analysis and engineered system design of radio resource management algorithms for a wide variety of systems such as cellular, ad hoc and sensor networks. The fundamental principle behind such models and much of game theory has been the reliance on Expected Utility Theory (EUT). Motivated by the increasing amount of end-user control afforded in programmable radio devices, we envision a scenario where end-user actions essentially "interfere" with the underlying engineered system design. As an exemplary scenario, we consider in this paper wireless random access where players follow the precepts of Prospect Theory (PT), a theory developed by Kahneman and Tversky to explain real-life decision making that often deviates from the behavior expected under EUT. Specifically, we consider a game where selfish players adjust their transmission probabilities over a random access channel under throughput rewards, delay penalties and energy costs. By analyzing the Nash Equilibrium achieved, we prove under mild conditions that deviations from EUT results in degradation of system throughput while increasing delay and energy consumption. Finally, we consider a data pricing model and study the impact of end-user decision-making (i.e., players service choices) at the NE on wireless network performance.

Augmented Reality (AR) applications benefit from accurate detection of the objects that are within a person's view. Typically, it is not only desirable to identify what is currently within view, but also to navigate the users view to the item of interest - for example, finding a misplaced object. In this paper we demonstrate a low-power hybrid radio-optical beaconing system, where objects of interest are tagged with battery-powered RFID-like tags equipped with infrared light emitting diodes (LED) that emit periodic infrared beacons. These beacons are used for accurately estimating the angle and distance from the object to the receiver so as to locate it. The beacons are synchronized using the radio link that is also used to convey the object's unique ID.

The advances in radio design and wireless networking, along with the growth of multimedia home entertainment technologies, is creating the concept of a cognitive digital home requiring spectrum coexistence of various devices and networks of networks. A framework for resource allocation in a Cognitive Digital Home (CDH) with a multiplicity of radio access technologies (RAT) such as cognitive radios and legacy radio devices supporting heterogeneous applications is developed. We consider two channel access models in the CDH for addressing spectrum coexistence of legacy devices: (i) Pessimistic Controllability (PC) Model where the Home Genie node (HG) has no influence over legacy devices, and (ii) Switched RAT (SR) Model where the HG has perfect control of legacy devices. Distributed algorithms for maximizing sum rate and maximizing service capacity are designed using partial dual decomposition techniques. A distributed power control scheme is also designed for efficient use of energy. An admission control scheme based on pricing information obtained from the distributed algorithms is used to improve system feasibility.

Bandwidth barter is an effective way of satisfying throughput requirements in wireless networks; we could expect a station (STA) allows another STA to borrow its bandwidth as long as it is also beneficial for the STA. Our previous work proved bandwidth barter between two STAs is optimized based on Nash bargaining solution (NBS), which brings the Pareto efficiency and the proportional fairness in the bartering game. However, it still remains an open issue how to solve the bartering game when the number of STAs is N (N >; 2), which is discussed in this paper.

In the past decade, remarkable progress has been made in the area of network coding in terms of theory, code design, and applications. However, from a cross-layer perspective, the understanding of resource allocation for random network coding (RNC) and its impact on network throughput has been limited. In this article we address the problem of resource allocation for RNC in a general wireless network, using the recent framework that models the dynamics of RNC using a system of differential equations. Based on this framework, we design a gradient algorithm that can be used to improve a variety of throughput objectives.

The growing ubiquity of cameras in hand-held devices and the prevalence of electronic displays in signage creates a novel framework for wireless communications. Traditionally, the term MIMO is used for multiple-input multiple-output where the multiple-input component is a set of radio transmitters and the multiple-output component is a set of radio receivers. We employ the concept of visual MIMO where pixels are transmitters and cameras are receivers. In this manner, the techniques of computer vision can be combined with principles from wireless communications to create an optical line-of-sight communications channel. Two major challenges are addressed: (1) The message for transmission must be embedded in the observed display so that the message is hidden from the observer and the electronic display can simultaneously be used for its originally intended purpose (e.g. signage, advertisements, maps); (2) Photometric and geometric distortions during the imaging process corrupt the information channel between the transmitter display and the receiver camera. These distortions must be modeled and removed. In this paper, we present a real-time messaging paradigm and its implementation in an operational visual MIMO optical systems. As part of the system, we develop a novel algorithm for photographic message extraction which includes automatic display detection, message embedding and message retrieval. Experiments show that the system achieves an average accuracy of 94.6% at the bitrate of 6222.2 bps.

The field of wireless and mobile communication has a remarkable history that spans over a century of technology innovations from Marconi's first transatlantic transmission in 1899 to the worldwide adoption of cellular mobile services by over four billion people today. Wireless has become one of the most pervasive core technology enablers for a diverse variety of computing and communications applications ranging from third-generation/fourth-generation (3G/4G) cellular devices, broadband access, indoor WiFi networks, vehicle-to-vehicle (V2V) systems to embedded sensor and radio-frequency identification (RFID) applications. This has led to an accelerating pace of research and development in the wireless area with the promise of significant new breakthroughs over the next decade and beyond. This paper provides a perspective of some of the research frontiers of wireless and mobile communications, identifying early stage key technologies of strategic importance and the new applications that they will enable. Specific new radio technologies discussed include dynamic spectrum access (DSA), white space, cognitive software-defined radio (SDR), antenna beam steering and multiple-input-multiple-output (MIMO), 60-GHz transmission, and cooperative communications. Taken together, these approaches have the potential for dramatically increasing radio link speeds from current megabit per second rates to gigabit per second, while also improving radio system capacity and spectrum efficiency significantly. The paper also introduces a number of emerging wireless/mobile networking concepts including multihoming, ad hoc and multihop mesh, delay-tolerant routing, and mobile content caching, providing a discussion of the protocol capabilities needed to support each of these usage scenarios. In conclusion, the paper briefly discusses the impact of these wireless technologies and networking techniques on the design of emerging audiovisual and multimedia applications as they migrate to mobile Internet platforms.

We investigate bandwidth allocation in next generation cellular systems employing relays similar to LTE advanced systems with type-I relays. We jointly optimize the bandwidth and power usage under constraints on required rate, bandwidth and transmit power. We study scenarios wherein, the relay acts as a forwarder for multiple User Equipments (UEs/users) in both uplink and/or downlink. This includes scenarios when the relay has its own data to send along with forwarding the data of other users. We examine the weighted power minimization problem for relaying with multiple users. We also show specific results with N user scenario and also single user case in order to understand how the bandwidth and power are allocated. Numerical evaluations with N users on a three sector LTE-A cell employing Fractional Frequency Reuse (FFR) indicate that power savings of at least 3 dB can be achieved by optimizing over both bandwidth and power.

In this paper, we consider multicast with Random Network Coding (RNC) over a wireless network using Orthogonal Frequency Division Multiple Access (OFDMA). Specifically, we propose a cross-layer resource allocation mechanism to minimize the total transmit power in the network to achieve a target throughput. The problem in its original form is a NP-hard mixed integer program. We alleviate this problem with a greedy power and subcarrier allocation algorithm that is combined with a node selection strategy that is enabled by RNC, which we refer to as “min-cut chasing.” We compare it with a reference algorithm that assigns subcarriers independently based on the max-min fairness criterion followed by optimal power allocation. Our results reveal that the proposed greedy algorithm with min-cut chasing, which is of polynomial complexity, yields power savings of 3dB and is within 1dB of a lower bound based on an interference-free assumption.

Forming secure associations between wireless devices that do not share a prior trust relationship is an important problem. This paper presents ProxiMate, a system that allows wireless devices in proximity to securely pair with one another autonomously by generating a common cryptographic key directly from their shared time-varying wireless environment. The shared key synthesized by ProxiMate can be used by the devices to authenticate each others' physical proximity and then to communicate confidentially. Unlike traditional pairing approaches such as Diffie-Hellman, ProxiMate is secure against a computationally unbounded adversary and its computational complexity is linear in the size of the key. We evaluate ProxiMate using an experimental prototype built using an open-source software-defined platform and demonstrate its effectiveness in generating common secret bits. We further show that it is possible to speed up secret key synthesis by monitoring multiple RF sources simultaneously or by shaking together the devices that need to be paired. Finally, we show that ProxiMate is resistant to even the most powerful attacker who controls the public RF source used by the legitimate devices for pairing.

The inherent limitations in RF spectrum availability and susceptibility to interference make it difficult to meet the reliability required for automotive safety applications. To address this challenge, this work explores an alternative communication system called Visual MIMO that uses light emitting arrays as transmitters and cameras as receivers. Visual MIMO applied to vehicular communication proposes to reuse existing LED rear and headlights as transmitters and existing cameras (e.g. those used for parking assistance, rear-view cameras) as receivers. In this work we show a proof of concept based demonstration of the Visual MIMO system consisting of an LED transmitter array and a high-speed camera.

We propose a rate adaptation scheme for visual MIMO camera-based communications, wherein parallel data transmissions from light emitting arrays are received by multiple receive elements of a CCD/CMOS camera image sensor. Unlike RF MIMO, multipath fading is negligible in the visual MIMO channel. Instead, the channel is largely dependent on receiver perspective (distance and angle) and visibility issues (partial line-of-sight availability and occlusions). This allows for slower adaptation but requires the adaptation algorithm to choose among a more complex set of modes. In this paper, we define a set of operating modes for visual MIMO transmitters and propose a rate adaptation scheme to switch between these modes. Our Visual MIMO Rate Adaptation (VMRA) is a packet based rate adaptation protocol that bases its rate selection decisions on the packet error rate feedback. Using trace-based simulation results for a vehicle-to-vehicle communication scenario, we illustrate how our VMRA algorithms can adapt over distance as well as visibility variations in an optical link and achieve a higher average throughput.

The advances in radio design and wireless networking, along with the growth of multimedia home entertainment technologies, is creating the concept of a cognitive digital home requiring spectrum coexistence of various devices and networks of networks. Recent work in has considered a framework for resource allocation in a cognitive digital home (CDH) with a multiplicity of radio access technologies (RAT) such as cognitive radios and legacy radio devices supporting heterogeneous applications. A spectrum manager, named the home genie node (HG), is used to coordinate spectrum coexistence across different services, with degrees of freedom ranging from transmission parameters such as frequency, bandwidth, and power, to the choice of radio access technology itself. Distributed algorithms for the resource allocation problems in a CDH, namely maximizing sum rate and maximizing service capacity, which have been previously solved using centralized approaches in, are designed and studied using partial Lagrangian decomposition techniques in this paper.

The problem of optimal resource allocation is studied for ergodic fading orthogonal multi-access relay channels (MARCs) in which the users (sources) communicate with a destination with the aid of a half-duplex relay that transmits and receives on orthogonal channels. Under the assumption that the instantaneous fading state information is available at all nodes, the maximum sum-rate and the optimal user and relay power allocations (policies) are developed for a decode-and-forward (DF) relay. A known lemma on the sum-rate of two intersecting polymatroids is used to determine the DF sum-rate and the optimal user and relay policies, and to classify fading MARCs into one of three types: (i) partially clustered MARCs in which a user is clustered either with the relay or with the destination, (ii) clustered MARCs in which all users are either proximal to the relay or to the destination, and (iii) arbitrarily clustered MARCs which are a combination of the first two types. Cutset outer bounds are used to show that DF achieves the capacity region for a sub-class of clustered orthogonal MARCs.

We develop a framework based on differential equations (DE) and differential inclusions (DI) for analyzing Random Network Coding (RNC) in an arbitrary wireless network. The DEDI framework serves as a powerful numerical and analytical tool to study RNC. For demonstration, we first build a system of DE's with this framework, under the fluid approximation, to model the means of the rank evolution processes. By converting this system to DI's and explicitly solving them, we show that the average multicast throughput is equal to the min-cut bound. We then turn to the precise system of DE's regarding the means and variances of the rank evolution processes. By analyzing this system, we show that the rank evolution processes asymptotically concentrate to the solution of the DI's obtained previously. From this result, it immediately follows that the min-cut bound can be achieved as the number of source packets becomes large. We demonstrate the numerical accuracy and flexibility in performance analysis enabled by the DEDI framework via illustrative examples of networks with multiple multicast sessions, complex topology and correlated reception. We also briefly discuss its application in MAC and PHY adaptation and the extension to Random Coupon Selection.

We compare the performance of half-duplex relays in downlink cellular system against a baseline system without relays. We simulate the performance of (i) a collaborative power addition scheme, where the relay boosts the received power (P-CPA) at the mobile locations, and (ii) a CPA scheme with power control (PC-CPA) at the base station and relays. Evaluations are done in the context of a 19-cell, 57-sector set-up in which each of the served users must be delivered a message. The user messages are taken to have the same size and 90% of users in the network must be served. Improvements over the baseline due to relay deployments are measured in terms of increase in common rate of users as well as power savings in terms of reduction in peak or average power transmitted by base stations. In the CPA schemes with base stations and relays transmitting at full power, the peak power saving is 1.46 dB, alternately, the throughput improvement over a 1 bit/sec/Hz baseline rate is 21%. In the PC-CPA scheme, the peak power saving is 2.6 dB and the average total power in the system can be reduced by 3 dB.

Since the FCC's approval of unlicensed use of TV white spaces, the issue of how to use these white spaces has led to innovative technologies such as cognitive radios as well as a variety of spectrum policy proposals. There have been proposals to devise alternate rules for spectrum usage citing the overly conservative restrictions on secondary transmissions to protect incumbents. In this paper, instead, we propose to utilize white spaces for a backhaul network for internet traffic based on existing restrictions. Using the available white spaces and backhaul traffic demands in New Jersey as a case study, we evaluate the feasibility of such backhauling and present a methodology that can be used for other areas as well. Using a basic design involving fixed towers and directional antennas, our results show that the TV white spaces can be an effective medium for radio backhaul as an alternative to the costly laying of optical fiber. We also show that meeting FCC requirements on sensing and avoiding harm to wireless microphones will have only a minor impact on capacity.

The advances in radio design and wireless networking, along with the growth of multimedia home entertainment technologies, is creating the concept of a cognitive digital home requiring spectrum coexistence of various devices and networks of networks. In this paper, we introduce a framework for resource allocation in a cognitive digital home with a multiplicity of radio access technologies such as cognitive radios and legacy technologies supporting heterogeneous applications. A spectrum manager, named the home genie node, is introduced to coordinate spectrum coexistence across different services, with degrees of freedom ranging from transmission parameters such as frequency, bandwidth, and power, to the choice of radio access technology itself. We study problems of sum rate maximization as well as maxmin rate allocation which are mixed integer nonlinear programs, and propose simple heuristic solutions for the same.

We propose a PHY-authentication protocol to detect spoofing attacks in wireless networks, exploiting the rapid-decorrelation property of radio channels with distance. In this protocol, a PHY-authentication scheme that exploits channel estimations that already exist in most wireless systems, cooperates with any existing-either simple or advanced-higher-layer process, such as IEEE 802.11i. With little additional system overhead, our scheme reduces the workload of the higher-layer process, or provides some degree of spoofing protection for ``naked" wireless systems, such as some sensor networks. We describe the performance of our approach as a function of the spoofing pattern and the snapshot performance that can be easily measured through field tests. We discuss the implementation issues of the authentication protocol on 802.11 testbeds and verify its performance via field tests in a typical office building.

FCC requires that any white space device be able to sense wireless microphone (WM) signals at -114dBm typically corresponding to a SNR of -20dB, an extremely challenging task. This paper presents a novel WM detector based on the ESPRIT algorithm along with an auxiliary prewhitening filter that meets this requirement. Compared with existing detectors, the proposed WM detector not only successfully combats noise, but also provides a technique to mitigate the effect of adjacent channel interference caused by DTV transmission leakage, an issue much less studied.

We develop a framework called DEDI based on differential equations (DE) and differential inclusions (DI) to describe the rank evolution of random network coding (RNC). The DEDI serves as a powerful numerical and analytical tool to study RNC and we demonstrate this via numerical examples as well as an alternate proof of a well known result on RNC - a multicast at rate R exists if and only if a unicast at rate R exists separately for each destination.

Mobile optical communications has so far largely been limited to short ranges of about ten meters, since the highly directional nature of optical transmissions would require costly mechanical steering mechanisms. Advances in CCD and CMOS imaging technology along with the advent of visible and infrared (IR) light sources such as (light emitting diode) LED arrays presents an exciting and challenging concept which we call as visual-MIMO (multiple-input multiple-output) where optical transmissions by multiple transmitter elements are received by an array of photodiode elements (e.g. pixels in a camera). Visual-MIMO opens a new vista of research challenges in PHY, MAC and Network layer research and this paper brings together the networking, communications and computer vision fields to discuss the feasibility of this as well as the underlying opportunities and challenges. Example applications range from household/factory robotic to tactical to vehicular networks as well pervasive computing, where RF communications can be interference-limited and prone to eavesdropping and security lapses while the less observable nature of highly directional optical transmissions can be beneficial. The impact of the characteristics of such technologies on the medium access and network layers has so far received little consideration. Example characteristics are a strong reliance on computer vision algorithms for tracking, a form of interference cancellation that allows successfully receiving packets from multiple transmitters simultaneously, and the absence of fast fading but a high susceptibility to outages due to line-of-sight interruptions. These characteristics lead to significant challenges and opportunities for mobile networking research

The multipath-rich wireless environment associated with typical wireless usage scenarios is characterized by a fading channel response that is time-varying, location-sensitive, and uniquely shared by a given transmitter-receiver pair. The complexity associated with a richly scattering environment implies that the short-term fading process is inherently hard to predict and best modeled stochastically, with rapid decorrelation properties in space, time, and frequency. In this paper, we demonstrate how the channel state between a wireless transmitter and receiver can be used as the basis for building practical secret key generation protocols between two entities. We begin by presenting a scheme based on level crossings of the fading process, which is well-suited for the Rayleigh and Rician fading models associated with a richly scattering environment. Our level crossing algorithm is simple, and incorporates a self-authenticating mechanism to prevent adversarial manipulation of message exchanges during the protocol. Since the level crossing algorithm is best suited for fading processes that exhibit symmetry in their underlying distribution, we present a second and more powerful approach that is suited for more general channel state distributions. This second approach is motivated by observations from quantizing jointly Gaussian processes, but exploits empirical measurements to set quantization boundaries and a heuristic log likelihood ratio estimate to achieve an improved secret key generation rate. We validate both proposed protocols through experimentations using a customized 802.11a platform, and show for the typical WiFi channel that reliable secret key establishment can be accomplished at rates on the order of 10 b/s.

Cooperative forwarding in wireless networks has shown to yield rate and diversity gains, but it incurs energy costs borne by the cooperating nodes. In this paper we consider an incentive mechanism called Bandwidth Exchange (BE) where the nodes flexibly exchange the transmission bandwidth as a means of providing incentive for forwarding data, without increasing either the total bandwidth required or the total transmit power. The advent of cognitive radios and multicarrier systems such as Orthogonal Frequency Division Multiple Access (OFDMA) with the ability to flexibly delegate and employ a number of subcarriers makes this approach particularly appealing compared to other incentive mechanisms that are often based on abstract notions of credit and shared understanding of worth.We consider a N-node wireless network over a fading channel and use a Nash Bargaining Solution (NBS) mechanism to study the benefits of BE in terms of rate and coverage gains.We also propose two heuristic algorithms based on simple probabilistic rules for forwarding and study the tradeoffs in terms of performance among these approaches. Our results reveal that bandwidth exchange based forwarding can provide transmit power savings in OFDMA networks of at least 3dB compared to noncooperation.

Cooperative forwarding in wireless networks has shown to yield benefits of rate and diversity gains, but it needs to be incentivized due to the energy and delay costs incurred by individual nodes in such cooperation. In this paper we consider an incentive mechanism called Bandwidth Exchange (BE) where the cooperating nodes flexibly exchange the transmission bandwidth (spectrum) as a means of providing incentive for forwarding data. The advent of cognitive radios with the ability to flexibly change their carrier frequency as well as their transmission bandwidth makes this form of incentive particularly attractive compared to other incentive mechanisms that are often based on abstract notions of credit and shared understanding of worth. Specifically, we consider a N-node wireless network and use a Nash Bargaining Solution (NBS) mechanism to study the benefits of BE in terms of rate and coverage gains.

In this paper, an auctioning strategy is proposed for cellular networks that ensures net energy savings. The pricing scheme, in conjunction with the two dimensional bid structure, incentivizes cooperation at the terminal nodes for better interference management at receivers and for cooperative relaying. It is shown that, for the proposed auctioning strategy, network operators are guaranteed revenue gains, mobile nodes' dominant strategy is to bid their true valuation of their energy resources, and overall effective energy gains occur under the assumption of a reserve price for bidding.

Due to the broadcast nature of the wireless medium, wireless networks are especially vulnerable to Sybil attacks, where a malicious node illegitimately claims a large number of identities and thus depletes system resources. We propose an enhanced physical-layer authentication scheme to detect Sybil attacks, exploiting the spatial variability of radio channels in environments with rich scattering, as is typical in indoor and urban environments. We build a hypothesis test to detect Sybil clients for both wideband and narrowband wireless systems, such as WiFi and WiMax systems. Based on the existing channel estimation mechanisms, our method can be easily implemented with low overhead, either independently or combined with other physical-layer security methods, e.g.,spoofingattack detection. The performance of our Sybil detector is verified, via both a propagation modeling software and field measurements using a vector network analyzer, for typical indoor environments. Our evaluation examines numerous combinations of system parameters, including bandwidth, signal power, number of channel estimates, number of total clients, number of Sybil clients, and number of access points. For instance, both the false alarm rate and the miss rate of Sybil attacks are usually below 0.01, with three tones, pilot power of 10 mW, and a system bandwidth of 20 MHz.

The sum-capacity is studied for a$K$-user physically degraded Gaussian multiple-access relay channel (MARC). Decode-and-forward (DF) is shown to achieve the sum-capacity and capacity region for a subclass of degraded Gaussian MARCs in which the multiple-access link from the sources to the relay is the bottleneck link. For the remaining subclass, DF is shown to achieve the$K$-user sum-capacity when the sources are symmetric, i.e., they transmit with the same transmit power. The optimality of DF is conjectured for the case of asymmetric sources.

In this paper, we show that for STC (Space-Time Coded) Hybrid-ARQ (Automatic Repeat reQuest) schemes with (re)transmission power control over independent Rayleigh block fading channels, the problem of optimizing energy efficiency with a PER (Packet Error Rate) constraint can be solved as a geometric programming problem. The optimum transmit power increases super-linearly with each requested retransmission and the fraction of the average power optimally allocated to each ARQ round only depends on the incremental diversity gain. The energy savings increases with a decrease in the PER targets and decreases with an increase in the diversity gain.

The radio channel response decorrelates rapidly as the transmitter changes location in an environment with rich scatterers and reflectors. Based on this fact, a channel-based authentication scheme was previously proposed to discriminate between transmitters at different locations, and thus to detect spoofing attacks in wireless networks. In this paper, we study its application in frequency-selective Rayleigh channels, considering channel time variations due to environmental changes and terminal mobility, as well as the channel estimation errors due to the interference from other radios. We propose a generalized likelihood ratio test (GLRT) that is optimal but computationally cumbersome, and a simplified version that requires no a priori knowledge of channel parameters and is therefore more practical. We verify the efficacy of the channel-based spoofing detectors via numerical analysis, showing how performance is improved by using multiple antennas, higher transmit power, and wider system bandwidth. We show that, under a wide variety of practical conditions, spoofing can be detected with better than 90% probability while keeping the probability of falsely rejecting valid transmissions below 10%.

We propose an enhanced physical-layer authentication scheme for multi-carrier wireless systems, where transmission bursts consist of multiple frames. More specifically, it is based on the spatial variability characteristic of wireless channels, and able to work with moderate terminal mobility. For the authentication of the first frame in each data burst, the legal transmitter uses the saved channel response from the previous burst as the key for authentication of the first frame in the next burst. The key is obtained either via feedback from the receiver, or using the symmetric channel property of a TDD system. Then the authentication of the following frames in the burst is performed either by a Neyman-Pearson hypothesis test, or a least-squares adaptive channel estimator. Simulations in a typical indoor building show that the scheme based on the Neyman-Pearson test is more robust against terminal mobility, and is able to detect spoofing attacks efficiently with small system overhead when the terminal moves with a typical pedestrian speed.

We study the formation of ad-hoc networks among selfish energy-constrained wireless devices that are primarily interested in beingconnectedwith other devices. We use a non-cooperative bilateral connection game (BCG) framework to study network formation. For a BCG in which devices choose their individual strategies to remain connected by minimizing only their direct transmission power costs, we show that the price-of-anarchy is unbounded in the network size. We propose a BCG with an alternate cost structure in which each device additionallypaysthe transmission power costs incurred by other devices for its own traffic. We show that a unique network structure emerges in this game that is stable as well as socially efficient. We then study the achievable throughput for random point-to-point traffic in this stable energy-efficient network. When the nodes of a network are located in a bounded planar region the distribution of point- to-point flows through the nodes exhibits a scale-free behavior.

Securing communications requires the establishment of cryptographic keys, which is challenging in mobile scenarios where a key management infrastructure is not always present. In this paper, we present a protocol that allows two users to establish a common cryptographic key by exploiting special properties of the wireless channel: the underlying channel response between any two parties is unique and decorrelates rapidly in space. The established key can then be used to support security services (such as encryption) between two users. Our algorithm uses level-crossings and quantization to extract bits from correlated stochastic processes. The resulting protocol resists cryptanalysis by an eavesdropping adversary and a spoofing attack by an active adversary without requiring an authenticated channel, as is typically assumed in prior information-theoretic key establishment schemes. We evaluate our algorithm through theoretical and numerical studies, and provide validation through two complementary experimental studies. First, we use an 802.11 development platform with customized logic that extracts raw channel impulse response data from the preamble of a format-compliant 802.11a packet. We show that it is possible to practically achieve key establishment rates of ~ 1 bit/sec in a real, indoor wireless environment. To illustrate the generality of our method, we show that our approach is equally applicable to per-packet coarse signal strength measurements using off-the-shelf 802.11 hardware.

The 26 papers in this special issue focus on game theory in communication systems. The papers are grouped in four clusters according to their topics: (1) Physical layer models in wireless communications, (2) higher layer and cross-layer issues in wireless communications, (3) wire-line communication networks, and (4) specific topics including peer-to-peer networking, network coding, and network security.

Cooperation between rational users in wireless networks is studied using coalitional game theory. Using the rate achieved by a user as its utility, it is shown that the stable coalition structure, i.e., set of coalitions from which users have no incentives to defect, depends on the manner in which the rate gains are apportioned among the cooperating users. Specifically, the stability of the grand coalition (GC), i.e., the coalition of all users, is studied. Transmitter and receiver cooperation in an interference channel (IC) are studied as illustrative cooperative models to determine the stable coalitions for both flexible (transferable) and fixed (non-transferable) apportioning schemes. It is shown that the stable sum-rate optimal coalition when only receivers cooperate by jointly decoding (transferable) is the GC. The stability of the GC depends on the detector when receivers cooperate using linear multiuser detectors (non-transferable). Transmitter cooperation is studied assuming that all receivers cooperate perfectly and that users outside a coalition act as jammers. The stability of the GC is studied for both the case of perfectly cooperating transmitters (transferrable) and under a partial decode-and-forward strategy (non-transferable). In both cases, the stability is shown to depend on the channel gains and the transmitter jamming strengths.

We investigate the use of distributed measurements for estimating and updating the performance of a cellular system. Specifically, we discuss the number and placement of sensors in a given cell for estimating its signal coverage. Here, an "outage" is said to occur at a location if a mobile receiver there has inadequate signal-to-noise ratio (SNR -based outage) or, using another criterion, inadequate signal-to-interference ratio (SIR- based outage); and the "outage probability" is the fraction of the cell area over which outage occurs. A design goal is to improve measurement efficiency (i.e., minimizing the required number of measurement sensors) while accurately estimating the outage probability and mapping the coverage holes. The investigation uses a generic path loss model incorporating distance effects and spatially correlated shadow fading. Our emphasis is on the performance prediction accuracy of the sensor network, rather than on cellular system analysis per se. Through analysis and simulation, we assess several approaches to estimating the outage probability. Applying the principle of importance sampling to the sensor placement, we show that a cell outage probability of Pocan be accurately estimated using ~ 10/Popower-measuring sensors distributed in a random uniform way over the area with base-sensor distances from 50% to 100% of the cell radius. This result applies to both SNR-based and SIR-based outage estimation for both indoor and outdoor environments.

The wireless medium contains domain-specific information that can be used to complement and enhance traditional security mechanisms. In this paper we propose ways to exploit the spatial variability of the radio channel response in a rich scattering environment, as is typical of indoor environments. Specifically, we describe a physical-layer authentication algorithm that utilizes channel probing and hypothesis testing to determine whether current and prior communication attempts are made by the same transmit terminal. In this way, legitimate users can be reliably authenticated and false users can be reliably detected. We analyze the ability of a receiver to discriminate between transmitters (users) according to their channel frequency responses. This work is based on a generalized channel response with both spatial and temporal variability, and considers correlations among the time, frequency and spatial domains. Simulation results, using the ray-tracing tool WiSE to generate the time-averaged response, verify the efficacy of the approach under realistic channel conditions, as well as its capability to work under unknown channel variations.

The wireless medium contains domain-specific information that can be used to complement and enhance traditional security mechanisms. In this paper we propose ways to exploit the fact that, in a typically rich scattering environment, the radio channel response decorrelates quite rapidly in space. Specifically, we describe a physical-layer algorithm that combines channel probing (M complex frequency response samples over a bandwidth W) with hypothesis testing to determine whether current and prior communication attempts are made by the same user (same channel response). In this way, legitimate users can be reliably authenticated and false users can be reliably detected. To evaluate the feasibility of our algorithm, we simulate spatially variable channel responses in real environments using the WiSE ray-tracing tool; and we analyze the ability of a receiver to discriminate between transmitters (users) based on their channel frequency responses in a given office environment. For several rooms in the extremities of the building we considered, we have confirmed the efficacy of our approach under static channel conditions. For example, measuring five frequency response samples over a bandwidth of 100 MHz and using a transmit power of 100 mW, valid users can be verified with 99% confidence while rejecting false users with greater than 95% confidence.

The problem of resource allocation is studied for a two-user fading orthogonal multiaccess relay channel (MARC) where both users (sources) communicate with a destination in the presence of a relay. A half-duplex relay is considered that transmits on a channel orthogonal to that used by the sources. The instantaneous fading state between every transmit-receive pair in this network is assumed to be known at both the transmitter and receiver. Under an average power constraint at each source and the relay, the sum-rate for the achievable strategy of decode-and-forward (DF) is maximized over all power allocations (policies) at the sources and relay. It is shown that the sum-rate maximizing policy exploits the multiuser fading diversity to reveal the optimality of opportunistic channel use by each user. A geometric interpretation of the optimal power policy is also presented.

The 11 papers in this special issue focus on adaptive, spectrum agile and cognitive wireless networks. Some of the topics covered include: fundamental performance aspects of spectrum sharing; spectrum sensing capabilities in cognitive radios; detection and estimation approaches to spectrum sharing; MAC design in opportunistic spectrum access; and competitive spectrum sharing and resource allocation using concepts from game theory and pricing.

In this paper we study the effects of practical pilot- assisted channel state estimation on the achievable information theoretic data rates (uplink and downlink) in a multiple antenna multiuser TDD system. Specifically, we consider a wireless system with multiple antennas at the base station and a number of mobile terminals each with a single antenna. We analyze the performance of uplink multiuser detection and downlink transmitter optimization that are based on linear spatial filtering. Using a discrete version of the continuously time-varying wireless channel we analyze how a lower bound on the achievable data rates depends on (1) the arrangement of pilot symbols (preamble or postamble); (2) the duration of the uplink and downlink transmissions; and (3) the percentage power allocated to the pilot. Furthermore, we also present the effects of the terminal speeds on the achievable data rates, thereby providing prescriptions for the practical design of the pilot. Specifically, our results point to an uplink postamble with flexible percentage (about 20 - 25 %) pilot power allocation, and transmission durations tailored for specific ranges of terminal speeds.

An offset encoding technique is presented that improves sliding-window decoding with decode-and-forward for K-user multiple-access relay channels. The technique offsets user transmissions by one block per user and achieves the corner points of the destination's backward decoding rate regions but with a smaller delay. As a result, one achieves boundary points of the best known decode-and-forward rate regions with a smaller delay than with backward decoding.

We investigate the use of an auxiliary network of sensors to locate mobiles in a cellular system, based on the received signal strength at the sensor receivers from a mobile's transmission. The investigation uses a generic path loss model incorporating distance effects and spatially correlated shadow fading. We describe four simple localization schemes and show that they all meet E-911 requirements in most environments. Performance can be further improved by implementing the MMSE algorithm, which ideally reaches the Cramer-Rao bound. We compare the MMSE algorithm and the four simple schemes when the model parameters are estimated via inter-sensor measurements.

The performance of wireless sensor network applications is typically a function of the amount of data collected by the individual sensors and delivered to a set of sinks through multihop routing within the network. However, the energy-constrained nature of the nodes limits the operational lifetime of the network since energy is dissipated both in sensing and in communicating data across the network. There is thus an inherent trade-off in simultaneously maximizing the network lifetime and the application performance (characterized here by a network utility function). In this paper, we characterize this trade-off by considering a cross-layer design problem in a wireless sensor network with orthogonal link transmissions. We compute an optimal set of source rates, network flows, and radio resources at the transport, network, and radio resource layers respectively, while jointly maximizing the network utility and lifetime. Using dual decomposition techniques, we show that the cross-layer optimization problem decomposes vertically into three subproblems - a joint transport and routing problem, a radio resource allocation problem, and a network lifetime maximization problem, all of which interact through the dual prices for capacities of links and battery capacities of nodes.

We investigate the use of an auxiliary network of sensors to assist radio resource management in a cellular system. Specifically, we discuss the number and placement of sensors in a given cell for estimating its signal coverage. Here, an "outage" is said to occur at a location if the mobile receiver there has inadequate signal-to-noise ratio (SNR-based outage) or, using another criterion, inadequate signal-to-interference ratio (SIR-based outage); and the "outage probability" is the fraction of the cell area over which outage occurs. A design goal is to confine the number of sensors per cell to an acceptable level while accurately estimating the outage probability. The investigation uses a generic path loss model incorporating distance effects and spatially correlated shadow fading. Our emphasis is the performance prediction accuracy of the sensor network, rather than cellular system analysis per se. Through analysis and simulation, we assess several approaches to estimating the outage probability. Applying the principle of importance sampling to the sensor placement, we show that a cell outage probability of ~ Po can be accurately estimated using ~ 10/Po power-measuring sensors distributed in a random uniform way over base-mobile distances from 50% to 100% of the cell radius. This result applies to both SNR-based and SIR-based cases, in both indoor and outdoor environments.

We consider a centralized Spectrum Server that coordinates the transmissions of a group of links sharing a common spectrum. Links employ on-off modulation with fixed transmit power when active. In the on state, a link obtains a data rate determined by the signal-to-interference ratio on the link. With knowledge of the link gains in the network, the spectrum server schedules the on/off periods of the links so as to satisfy constraints on link fairness and efficiency. We express fairness constraints as lower bounds on the average minimum rate for each link. Efficiency constraints are expressed as lower bounds on the ratio of the average rate to the average transmit power for each link. Subject to fairness and efficiency constraints, the spectrum server finds a schedule that maximizes the average sum rate. Using a graph theoretic model for the network and a linear programming formulation, the resulting schedules are a collection of time shared transmission modes (sets of active links). In the special case when there is no minimum rate constraint, varying the efficiency constraint can cause the optimal policy to vary from a fixed dominant mode with highest sum rate being operated all the time to time sharing among singleton modes in which just one link is active. We also address the case of maximum common rate scheduling under efficiency constraints. Simulation results are presented to substantiate our findings.

The formation of coalitions in a Gaussian interference channel where the receivers are allowed to cooperate is studied under the framework of coalitional game theory. Allowing any arbitrary sharing of the total rate achieved by a coalition between its member links, it is shown that the grand coalition (coalition of all links) maximizes spectrum utilization and is also stable, that is, the links in this coalition have no incentives to leave and form other coalitions. The issue of fairness in allocating rates to members of a grand coalition is addressed via a Nash bargaining solution where each link utility is modeled as the rate gained by being in a coalition relative to the rate achieved in the interference channel. Further, a rate allocation solution using proportional fairness is also presented and the results are illustrated with examples

We propose a channel state information (CSI) feedback scheme based on unquantized and uncoded (UQ-UC) transmission. We consider a system where a mobile terminal obtains the downlink CSI and feeds it back to the base station using an uplink feedback channel. If the downlink channel is an independent Rayleigh fading channel, then the CSI may be viewed as an output of a complex independent identically distributed Gaussian source. Further, if the uplink feedback channel is an additive white Gaussian noise channel, and the downlink CSI is perfectly known at the mobile terminal, it can be shown that UQ-UC CSI transmission (that incurs zero delay) is optimal in that it achieves the same minimum mean-squared error distortion as a scheme that optimally (in the Shannon sense) quantizes and encodes the CSI, while theoretically incurring infinite delay. Since the UQ-UC transmission is suboptimal on correlated wireless channels, we propose a simple linear CSI feedback receiver that can be used to improve the performance of UQ-UC transmission while still retaining the attractive zero-delay feature. We provide bounds on the performance of such UQ-UC CSI feedback and study its impact on the achievable information rates. Furthermore, we explore its application and performance in multiple-antenna multiuser wireless systems, and also propose a corresponding pilot-assisted channel-state estimation scheme

The performance of source-cooperation in a multi-access network is compared to that of using a wireless relay. The former network is modeled as a multi-access channel with generalized feedback and the latter as a multi-access relay channel. Using power as the cost metric, achievable rates and outage probabilities for the two networks are compared under a total transmit power constraint and a specified geometry. The use of a relay is shown to be advantageous for a variety of wireless fading channels

In the power control game proposed for MC-CDMA systems, each user needs to decide how much power to transmit over each carrier to maximize its overall utility. The utility function considered measures the number of reliable bits transmitted per joule of energy consumed. It is shown that the user's utility is maximized when the user transmits only on the carrier with the best "effective channel". The existence and uniqueness of Nash equilibrium for the proposed game are investigated and the properties of equilibrium are studied. Also, an iterative and distributed algorithm for reaching equilibrium (if it exists) is presented. It is shown that the proposed approach results in a significant improvement in the total utility achieved at equilibrium compared to the case in which each user maximizes its utility over each carrier independently.

Wireless sensor networks are typically deployed over an information field to sense and gather information from a distributed physical process. Resource allocation problems considered in the literature often ignore the underlying information field and rather consider a uniform distribution of information. We propose an information field model that partitions the observation space into a grid, with independent information being generated at each point in the grid. Given this model, we find the optimal node distribution over the field that maximizes the network information capacity or the total information gathered over the lifetime of the network. The optimal node distribution is obtained by considering the equivalent problem of optimal energy distribution and flow over the information field that maximizes the information capacity.

We propose a channel state information (CSI) feedback scheme based on unquantized and uncoded (UQ-UC) transmission. We consider a system where a mobile terminal obtains the downlink CSI and feeds it back to the base station using an uplink feedback channel. If the downlink channel is an independent Rayleigh fading channel, then the CSI may be viewed as an output of a complex independent identically distributed Gaussian source. Further, if the uplink feedback channel is AWGN, it can be shown that that UQ-UC CSI transmission (that incurs zero delay) is optimal in that it achieves the same MMSE distortion as a scheme that optimally (in the Shannon sense) quantizes and encodes the CSI while theoretically incurring infinite delay. Since the UQ-UC transmission is suboptimal on correlated wireless channels, we propose a simple linear CSI feedback receiver that can be used to improve the performance while still retaining the attractive zero-delay feature. We provide bounds on the performance of the UQ-UC CSI feedback. Furthermore, we explore its application and performance in multiple antenna multiuser wireless systems.

The assumption that all nodes cooperate to relay packets for each other may not be realistic for commercial wireless ad hoc networks. An autonomous (selfish) node in a wireless network has two disincentives for forwarding for others: energy expenditure (real cost) and possible delays for its own data (opportunity cost). We introduce a mechanism that "fosters cooperation through bribery" in the context of forwarding in ad hoc networks. Using a microeconomic framework based on game theory, we design and analyze a pricing algorithm that encourages forwarding among autonomous nodes by reimbursing forwarding. Taking a joint network-centric and user-centric approach, the revenue maximizing network and utility (measured in bits-per-Joule) maximizing nodes interact through prices for channel use, reimbursements for forwarding, transmitter power control, as well as forwarding and destination preferences. In a three-node (two-sources, one-access-point) network, the network converges to an architecture that induces forwarding only when the network geometries are such that forwarding is likely to increase individual benefits (network revenue and node utilities). For other geometries, the network converges to architectures that do not favor forwarding. We then generalize to a multinode network, where it is seen that the nodes' willingness to forward decrease for large ratios of the average internodal distance to the smallest distance between the access point and any source node. Pricing with reimbursement generally improves the network aggregate utility (or aggregate bits-per-Joule), as well as utilities and revenue compared with the corresponding pricing algorithm without reimbursement.

We consider wireless data services characterized by short distances, no shadowing, low power, and low antenna heights, deployed in places where a high frequency of potential users is expected, e.g., toll booths, parking lots, intersections, etc. Within such a system, we expect to see a well-defined geometry of base-to-user radio paths, as well as a predictable user trajectory, neither of which can be assumed for the wide-area cellular case. This offers the promise of a strong deterministic component of the channel response, in addition to a weaker stochastic component. Here, we combine analysis of the former with measurements and modeling of the latter for three typical outdoor scenarios. Comparisons between predicted and measured behavior show excellent agreement.

A pricing mechanism to mediate (and allocate resources) between conflicting user and network objectives has been recently proposed by the authors in a single-cell system. Here, we extend the results to a multicell system, where the autonomous base station assignment and power control are formulated as a noncooperative game among users. The network prices the resources using two strategies: global pricing that maximizes the revenue, and minimax pricing that trades off the revenue for a more even resource allocation.

In this paper, the cross-layer design problem of joint multiuser detection and power control is studied, using a game-theoretic approach that focuses on energy efficiency. The uplink of a direct-sequence code-division multiple-access data network is considered, and a noncooperative game is proposed in which users in the network are allowed to choose their uplink receivers as well as their transmit powers to maximize their own utilities. The utility function measures the number of reliable bits transmitted by the user per joule of energy consumed. Focusing on linear receivers, the Nash equilibrium for the proposed game is derived. It is shown that the equilibrium is one where the powers are signal-to-interference-plus-noise ratio-balanced with the minimum mean-square error (MMSE) detector as the receiver. In addition, this framework is used to study power-control games for the matched filter, the decorrelator, and the MMSE detector; and the receivers' performance is compared in terms of the utilities achieved at equilibrium (in bits/joule). The optimal cooperative solution is also discussed and compared with the noncooperative approach. Extensions of the results to the case of multiple receive antennas are also presented. In addition, an admission-control scheme based on maximizing the total utility in the network is proposed.

We consider transmission control (rate and power) strategies for transferring a fixed-size file (finite number of bits) over fading channels under constraints on both transmit energy and transmission delay. The goal is to maximize the probability of successfully transferring the entire file over a time-varying wireless channel modeled as a finite-state Markov process. We study two implementations regarding the delay constraints: an average delay constraint and a strict delay constraint. We also investigate the performance degradation caused by the imperfect (delayed or erroneous) channel knowledge. The resulting optimal policies are shown to be a function of the channel-state information (CSI), the residual battery energy, and the number of residual information bits in the transmit buffer. It is observed that the probability of successful file transfer increases significantly when the CSI is exploited opportunistically. When the perfect instantaneous CSI is available at the transmitter, the faster channel variations increase the success probability under delay constraints. In addition, when considering the power expenditure in the pilot for channel estimation, the optimal policy shows that the transmitter should use the pilot only if there is sufficient energy left for packet transfer; otherwise, a channel-independent policy should be used.

In future wireless systems, increasing the downlink capacity becomes more important due to potential high data rate applications such as wireless internet access. Turbo codes and downlink transmit diversity are two of the most explored techniques to improve performance and capacity. In this paper, we evaluate the performance of turbo coded wideband code-division multiple-access (WCDMA) systems with downlink transmit diversity employing a space-time block code on correlated fading channels. The performance improvement using downlink space-time transmit diversity (STTD) is found to be more significant in the case of slow fading when the temporal correlation is high. By considering the spatial correlation between the transmit antennas at the base station, we quantify the tolerable correlation between transmit antennas by means of simulation for which the turbo coded WCDMA systems with transmit diversity still attain performance gains compared to a single transmit antenna system.

A three-tier hierarchical wireless sensor network is considered that consists of a cluster of sensors, an intermediate relay with better computing and communication capabilities than the sensors, and a central server or access point. Such a network can be modeled as a multiple-access relay channel (MARC) with additive white Gaussian noise and fading. Capacity bounds for this network are presented with and without constraints on simultaneous reception and transmission by the relay. The results identify cooperative strategies between the relay and sensors for increasing network capacity. These strategies also preserve limited battery resources by eliminating the need for cooperation between sensors.

Using a microeconomic framework based on game theory, we design and analyze a pricing algorithm that encourages forwarding among autonomous (selfish) nodes by reimbursing forwarding. Taking a joint network-centric and user-centric approach, the revenue maximizing network and utility maximizing nodes interact through prices for channel use, reimbursements for forwarding, transmitter power control as well as forwarding and destination preferences. In a three-node (two sources, one access point) network, the network converges to an architecture that induces forwarding only when the network geometries are such that forwarding is likely to bring higher network efficiency. For other geometries, the network converges to architectures that do not favor forwarding. We characterize the Nash equilibria for each geometry and design specific game implementations to achieve the most Pareto superior Nash equilibrium. In a general multi-node network, our results indicate that the nodes' willingness to forward decrease for large values of the ratio of the average internodal distance to the average distance between the access point and nodes with direct connections to it.

Objectives of most radio resource-management schemes can be classified as either user centric or network centric. User-centric schemes try to maximize the interests of individual users, while network-centric schemes optimize collective metrics for all users. These two types of resource management tend to result in qualitatively different resource allocations (with, sometimes, very different degrees of fairness). In this paper, we consider the joint optimization of both user-centric and network-centric metrics. Specifically, we use a utility function (measured in units of bits per Joule) as the user-centric metric, and for the network-centric counterpart, we consider a function of the sum of the throughputs of users in the network. The user-centric measure reflects the individual user's throughput, as well as the battery energy (transmit power) consumed to achieve it. The network-centric measure reflects the total revenue derived by the usage of network resources. We introduce an explicit pricing mechanism to mediate between the user-centric and network-centric resource-management problems. Users adjust their power in a distributed fashion to maximize the difference between their utilities and their payments (measured as a product of the unit price and throughput). The network adjusts the unit price in order to maximize its revenue (measured as the sum of the individual payments). We show that the distributed user-centric power control results in a unique Nash equilibrium. Our numerical results indicate that there exists a unique unit price that maximizes the revenue of the network. We also derive a semianalytical, computationally simple, and highly accurate approximation to the optimal solution. Our results show that while users with better channels receive better qualities of service, as usual (e.g., as in waterfilling), they also make proportionally higher contributions to the network revenue.

In this letter, we present a simplified scheduling scheme for packet transmission over a fading channel which is modeled as a finite-state block channel. We first address the optimal minimum power-transmission policy with constraints on both average delay and packet loss. The optimal policy requires a sophisticated table lookup for implementation. In order to alleviate this problem, we design a simplified transmission policy that is based on checking for three control parameters: a transmission-rate threshold; a channel-state threshold; and the transmission buffer size. Our results show that the minimum average power with the simplified scheme is very close to that achieved by the optimal policy. Further, modeling the simplified scheme via a single-server bulk-service queue, we derive approximations to the quality of service metrics, such as average power and delay, that allow for analytical insights into the policy design.

Combined spatial and temporal processing has been shown to increase the potential link capacity enormously for wireless communication systems, especially when the channels between different transmit and receive antenna pairs are uncorrelated. We consider both spatial and temporal channel correlations that may be encountered in space-time processing and present the performance analysis of convolutionally coded direct-sequence code-division multiple-access systems. An upper bound for the average bit-error probability (P~/sub b/) is derived for the case of perfect channel estimation, and an analytical approximation for P~/sub b/ is derived in the case of erroneous channel estimates. The analytical approach is general enough to be applicable to various space- and time-diversity situations, such as wideband multipath channels and antenna arrays.

We propose a wireless access mechanism for web traffic packets in an integrated wireless code-division multiple-access system that has both voice and Web traffic. The proposed scheme is a medium-access control layer/link layer (MAC/LL) scheduling algorithm that consists of a two level control: admission control and packet scheduling. The admission control restricts the number of users in the system such that quality-of-service requirements [target signal-to-interference ratio (SIR) and delay] for both voice and Web traffic can be met. The packet scheduling balances the system interference on a slot-by-slot basis such that the target SIRs can be achieved for all users (voice and Web browsing sessions) with a higher scheduling priority for voice. Designing admission control for Web users based on the average offered rate per session is difficult due to the high variations in the offered load generated by heavy tailed distributions for Web traffic. To overcome this problem, we propose an admission control algorithm that adaptively estimates the aggregate average load based on load measurements using a sliding observation window.

We propose a blind successive interference cancellation receiver for asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems using a maximum mean energy (MME) optimization criterion. The covariance matrix of the received vector is used in conjunction with the MME criterion to realize a blind successive interference canceler that is referred to as the BIC-MME receiver. The receiver executes interference cancellation in a successive manner, starting with the most dominant interference component and successively cancelling the weaker ones. The receiver is compared against various centralized and decentralized receivers, and it is shown to perform well in the presence of estimation errors of the covariance matrix, making it suitable for application in time-varying channels. We also analyze properties of the covariance matrix estimates which are relevant to the performance of the BIC-MME receiver. Further, the BIC-MME receiver is particularly efficient in the presence of a few strong interferers as may be the case in the downlink of DS-CDMA systems where intracell user transmissions are orthogonal. An iterative implementation that results in reduced complexity is also studied.

A major challenge in the operation of wireless communications systems is the efficient use of radio resources. One important component of radio resource management is power control, which has been studied extensively in the context of voice communications. With the increasing demand for wireless data services, it is necessary to establish power control algorithms for information sources other than voice. We present a power control solution for wireless data in the analytical setting of a game theoretic framework. In this context, the quality of service (QoS) a wireless terminal receives is referred to as the utility and distributed power control is a noncooperative power control game where users maximize their utility. The outcome of the game results in a Nash (1951) equilibrium that is inefficient. We introduce pricing of transmit powers in order to obtain Pareto improvement of the noncooperative power control game, i.e., to obtain improvements in user utilities relative to the case with no pricing. Specifically, we consider a pricing function that is a linear function of the transmit power. The simplicity of the pricing function allows a distributed implementation where the price can be broadcast by the base station to all the terminals. We see that pricing is especially helpful in a heavily loaded system.

In this paper, we introduce total energy constraints on transmission and study dynamic power control on a time varying channel under delay constraints. We consider a block fading channel modeled as a finite state Markov chain and we study binary (on/off) power control schemes for variable rate systems. We search for the optimal transmission policy that maximizes the minimum value of the expected sum of rates over the total communication window. The optimal policies are shown to be threshold rules on the received signal strength and the residual battery energy.

We consider distributed power control in a multicell wireless data system and study the effect of pricing transmit power. Drawing on the earlier work of Goodman and Mandayam (see IEEE Personal Commun. Mag., vol.7, p.48-54, 2000), we formulate the QoS of a data user via a utility function measured in bits per Joule. We consider distributed power control, modeled as a noncooperative game, where users maximize their utilities in a multicell system. Base station assignment based on received signal strength as well as received signal-to-interference ratio (SIR) are considered jointly with power control. Our results indicate that for both assignment schemes, such a procedure results in an inefficient operating point (Nash equilibrium) for the entire system. We introduce pricing of transmit power as a mechanism for influencing data user behavior and our results show that the distributed power control based on maximizing the net utility (utility minus the price) results in improving the Pareto efficiency of the resulting operating point. Variations of pricing based on global and local loading in cells are considered as a means of improving the efficiency of wireless data networks. Finally, we discuss the improvement in utilities through a centralized scheme where each base station (BS) calculates the best SIR to be targeted by the terminals it is assigned.

We study the signal-to-interference ratio (SIR) and rate control for code division multiple access (CDMA) data users on the forward link under average or peak power constraints. The quality of service (QoS) for data users is specified by delay and error rate constraints as well as a family of utility functions representing the throughput and fairness among the data users. It is found that the optimal SIR and rate control algorithm has a hierarchical structure which can be easily implemented in a distributed manner. The SIR targets can be adjusted independently by the mobiles using information specific to the individual users. The data rates can be adjusted jointly by the base station based on limited feedback from the mobiles. We also propose a two-level iteration algorithm for both the mobile and the base station to efficiently compute the SIR and data rates. Our results show that a flexible tradeoff between total system throughput (sum of rates achieved) and fairness (similarity in data rates) can be achieved by choosing appropriate utility functions used in this scheme.

We analyze the performance of a code division multiple access (CDMA) reverse link a with an up/down power control algorithm in the presence of fading. We derive a stochastic nonlinear feedback control system model for the power controlled reverse link, and study the power control performance based on the nonlinear model using the technique of statistical linearization. We provide a general analysis framework that allows us to study the effect of mobile speed, power control step size, and fading channel parameters such as correlation coefficient and rate of fading on power control errors. Numerical results show excellent accuracy of our analysis, which can be used to design and optimize the system parameters without going through lengthy simulations. For example, in the presence of shadow fading, it is seen that a power control step size in the range of 0.5-1.3 dB is sufficient to keep power control errors near a minimum. In the case of Rayleigh fading, the standard deviation of power control errors grows quickly even at moderate mobile speeds.

Minimum duration outages have been introduced for lognormal shadow fading where the durations of signal fades were considered in evaluating outages. We develop and analyze such minimum duration outages for channels susceptible to Rayleigh fading. The results show that under typical Doppler frequencies the outages due to Rayleigh fading are more likely to cause frame or packet errors rather than call dropping due to the short time scales in effect.

We consider SIR and rate control algorithms for data users on the CDMA forward link under constraints on delay (i.e., limited allowable retransmissions), data error rate and total power. The QoS for data users is mapped onto a family of utility functions that reflect the tradeoff between total system throughput and fairness (similarity in data rates of users). The proposed algorithm uses an hierarchical control structure that is simple to implement in a distributed manner. Specifically, the data rates are adjusted jointly by the base station while the SIR targets are adjusted by the mobiles using information specific to the individual users.

In this paper, we propose access control protocols for integrated voice/data code division multiple access (CDMA) systems that are based on estimating the residual capacity available for data users. The data model considered in the analysis is suitable for non-transparent services (e.g., e-mail, file transfer, store-and-forward facsimile, etc.). The residual capacity is derived from the feasibility condition for power control using an adaptive prediction technique based on delta modulation. The analysis is extended for the case of imperfect power control. Two access control protocols are considered: modified delta modulation with scheduled access (MDM-S) and modified delta modulation with random access (MDM-R). Comparisons with earlier proposed protocols reveal a better performance in terms of average throughput for data for a given system capacity and outage probability.

We present a new method to analyze the performance of Reed-Solomon codes for hybrid automatic-repeat-request (ARQ) over Rayleigh fading channels with imperfect interleaving. Using the asymptotic results of level crossings of the faded envelope, we characterize the packet-error probability as a function of the Doppler frequency and mean signal-to-noise ratio. We then use the notion of equivalent Doppler frequency to include the effect of imperfect interleaving on performance. This approach allows us to directly evaluate the throughput of hybrid-ARQ schemes and also specify minimum interleaving depths for achieving optimum throughput on correlated Rayleigh channels.

We propose access control protocols for integrated voice/data CDMA systems that are based on estimating the residual capacity available for data users. The residual capacity is derived from the feasibility condition for power control using an adaptive prediction technique based on delta modulation. The analysis is extended for the case of imperfect power control. Two access control protocols are considered: modified delta modulation with scheduled access (MDM-S), and modified delta modulation with random access (MDM-R). Comparisons with earlier proposed protocols reveal a better performance in terms of data mean access delay for a given system capacity and outage probability.

We consider an image transmission system consisting of set partitioning in hierarchical trees (SPIHT) wavelet-based source coding, and list Viterbi algorithm (LVA) channel coding using rate compatible punctured convolutional (RCPC) codes. Our goal is to minimize bandwidth usage while maintaining received image quality. We introduce an outage criterion for image transmission. A received image is considered to be in outage if its pixel values are not identical to the image transmitted. In order to reduce correlation in fading, an enhanced block interleaving scheme employing a subblock structure is proposed. Using RCPC codes, we devise a variable rate channel coding system based on the current channel state information. Combining enhanced interleaving and variable rate channel coding, we construct a method to select the highest rate RCPC code that satisfies the image outage criterion, thus balancing the requirements of high bandwidth efficiency and high image quality.

Recent work has considered power control for wireless data networks from the framework of microeconomics. The user preferences have been mapped to utility functions measured in useful bits transmitted per unit of battery energy. We consider utility maximizing distributed power control in the presence of pricing and study the existence and efficiency of equilibria achieved. Specifically, we investigate the Pareto efficiency of a pricing policy that punishes asocial behavior in terms of the transmit power used by each user and hence the interference caused by it. The Nash equilibria achieved under pricing are characterized by using supermodularity. We propose a simple implementation of the pricing scheme where the base station broadcasts a pricing parameter that users use in their distributed power control algorithm. With the appropriate choice of the pricing parameter, all users increase their utilities significantly as compared to the case where there is no pricing. We also discuss the implication of pricing with regard to achieving a socially optimum operating point.

The integration of multimedia services over wireless channels calls for provision of variable quality of service (QoS) requirements. While radio resource management algorithms (such as power control and call admission control) can provide certain levels of variability in QoS, an alternate approach is to use reconfigurable radio architectures to provide diverse QoS guarantees. We outline a novel reconfigurable architecture for linear multiuser detection, thereby providing a wide range of bit error rate (BER) requirements amongst the constituent receivers of the reconfigurable architecture. Specifically, we focus on achieving this dynamic reconfiguration via a software radio implementation of linear multiuser receivers. Using a unified framework for achieving this reconfiguration, we partition functionality into two core technologies [field programmable gate arrays (FPGA) and digital signal processor (DSP) devices] based on processing speed requirements. We present experimental results on the performance and reconfigurability of the software radio architecture as well as the impact of fixed point arithmetic (due to hardware constraints).

We develop a framework for power control for wireless data services based on economic concepts of utility and pricing. Our approach is based on a model for the level of satisfaction (utility) a wireless data user derives from using the system. Using this model, we present a distributed power control scheme that maximizes the utility of each user. Formulating this algorithm as a non-cooperative game, we show conditions for the feasibility of such power control as well as the existence and uniqueness of the Nash equilibrium achieved by the non-cooperative game. However, the resulting equilibrium is shown to be Pareto inefficient. A detailed analysis of this equilibrium provides motivation for introducing pricing in the system. A distributed power control scheme is then proposed where users maximize the difference in their utility and price. It is shown by way of simulation that by introducing pricing in the power control algorithm, there is a Pareto improvement in terms of the utilities that the users obtain. Numerical results are presented for the special case of a code division multiple access (CDMA) system for PCS (personal communication services) band.

For optimizing the performance of optical code-division multiple-access (CDMA) systems, there is a need for determining the sensitivity of the bit-error rate (BER) of the system to various system parameters. Asymptotic approximations and bounds, used for system bit-error probabilities, seldom capture the sensitivities of the system performance. We develop single-run gradient estimation methods for such optical CDMA systems using a discrete-event dynamic systems (DEDS) approach. Specifically, computer-aided techniques such as infinitesimal perturbation analysis (IPA) and likelihood ratio (LR) methods are used for analyzing the sensitivity of the average BER to a wide class of system parameters. It is shown that the above formulation is equally applicable to time-encoded and frequency-encoded systems. Further, the estimates derived are unbiased, and also optimality of the variance of these estimates is shown via the theory of common random variates and importance sampling techniques.

For pt.I see ibid., vol.15, no.4, p.731-41 (1997). We develop infinitesimal perturbation analysis (IPA)-based stochastic gradient algorithms for deriving optimum detectors with the average probability of bit error being the objective function that is minimized. Specifically, we develop both a class of linear as well as nonlinear (threshold) detectors. In the linear scheme, the receiver despreads the received optical signal with a sequence that minimizes the average bit-error rate. In the case of the threshold detector, the detection threshold for the photoelectron count is optimized to achieved minimum average bit-error rate. These algorithms use maximum likelihood estimates of the multiple access interference based on observations of the photoelectron counts during each bit interval, and alleviate the disadvantage of previously proposed schemes that require explicit knowledge of the interference statistics. Computer-aided implementations of the detectors derived are shown to outperform the correlation detector. Sequential implementations of the adaptive detectors that require no preamble are also developed, and make them very viable detectors for systems subject to temporal variations.

In this paper, we consider a direct-sequence code-division multiple-access (DS-CDMA) system in the framework of a discrete-event dynamic system (DEDS) in order to optimize the system performance. Based on this formulation, we develop infinitesimal perturbation analysis (IPA) for estimating the sensitivity of the average probability of bit error to factors ranging from near-far effects to imperfections in power control. The above estimates are shown to be unbiased, and this technique is then further incorporated into a stochastic gradient algorithm for achieving adaptive multiuser interference rejection for such systems, which is also subject to frequency nonselective slow fading. We use an IPA-based stochastic training algorithm for developing an adaptive linear detector with the average probability of error being the minimization criterion. We also develop a practical implementation of such an adaptive detector where we use a joint estimation-detection algorithm for minimizing the average probability of bit error. A sequential implementation that does not require a stochastic training sequence or a preamble is also developed.

Analytical solutions of the performance of optical communication systems are difficult to obtain and often, Monte Carlo simulations are used to achieve realistic estimates of the performance of such systems. However, for high performance systems, this technique requires a large number of simulation trials for the estimates to be in a reasonable interval of confidence, with the number of trials increasing linearly with the performance of the system. We apply an importance sampling technique to estimate the performance of direct detection optical systems, where the "gain" of importance sampling over Monte Carlo simulations is shown to increase linearly with the system performance. Further, we use this technique to study the performance of optical communication systems employing avalanche photodetectors as well as fibre-optic code division multiple access systems (FO-CDMA). We also show that the quick simulation technique developed can be used for a wide variety of coding schemes, and for the first time, we present a comparative analysis of the performance of FO-CDMA systems employing optical orthogonal codes and prime sequences. In all cases, it is shown that importance sampling simulations require less than 50-100 trials for estimating error probabilities of 10-10 and below.