This article addresses a fundamental question of multiagent knowledge distribution: what information should be sent to whom and when with the limited resources available to each agent? Communication requirements for multiagent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multiagent coordination on networked systems, for example, power and bandwidth, this article introduces two concepts for the partially observable Markov decision processes (POMDPs): 1) action-based constraints that yield constrained-action POMDPs (CA-POMDPs) and 2) soft probabilistic constraint satisfaction for the resulting infinite-horizon controllers. To enable constraint analysis over an infinite horizon, an unconstrained policy is first represented as a finite-state controller (FSC) and optimized with policy iteration. The FSC representation then allows for a combination of the Markov chain Monte Carlo and discrete optimization to improve the probabilistic constraint satisfaction of the controller while minimizing the impact on the value function. Within the CA-POMDP framework, we then propose intelligent knowledge distribution (IKD) which yields per-agent policies for distributing knowledge between agents subject to interaction constraints. Finally, the CA-POMDP and IKD concepts are validated using an asset tracking problem where multiple unmanned aerial vehicles (UAVs) with heterogeneous sensors collaborate to localize a ground asset to assist in avoiding unseen obstacles in a disaster area. The IKD model was able to maintain asset tracking through multiagent communications while only violating soft power and bandwidth constraints 3% of the time, while greedy and naive approaches violated constraints more than 60% of the time.

In this paper, we introduce an efficient application-aware resource allocation (RA) approach for wireless networks with hybrid traffic. In our model, users are running elastic or inelastic traffic. We use logarithmic and sigmoidal-like utility functions to represent delay tolerant applications and real time applications, respectively, running on the user equipment (UE). Our approach provides an efficient assignment of the spectral resources based on the quality of service (QoS) requirements of users' applications as well as the UEs' channel conditions. We proved that the proposed resource allocation optimization problem is convex and therefore can ensure optimal allocated resources while considering the QoS requirements of the UEs' applications and channels' conditions.

Spectrum auction has been considered a promising solution to release the under-utilized spectrum from primary spectrum licensee to potential secondary users. In this paper, we introduce an auction-based resource allocation scheme between a spectrum broker and base stations (BS)s that belong to a wireless service provider (WSP). Each BS submits the proportion of resources it wishes to lease from each auctioned spectrum band based on its true demand and the auctioneer's offered price. The spectrum broker leases the resource blocks (RB)s of its under-utilized spectrum bands to the WSP base stations using an iterative resource allocation algorithm. The proposed algorithm is based on a fair spectrum auction. The spectrum broker who manages the under-utilized spectrum resources plays the role of an auctioneer. First, we establish utility functions for the auctioneer and each participating BS. Then we maximize these utilities in iterations in order to achieve optimal price and allocated resources. Furthermore, we introduce a cheat-prevent auction mechanism based on ascending-bid auction to prevent greedy BSs from cheating activities to obtain lower prices for the auctioned resources. Finally we present simulation results on the performance of the proposed resource leasing mechanism.

The Fifth Generation (5G) mobile broadband standards make a fundamental shift in cryptography. Prior generations based their security and privacy principally on symmetric key cryptography. The Subscriber Identity Module (SIM) and its successors contain a shared key used to authenticate the User Equipment (UE) to the network, and vice versa.

Machine-to-Machine (M2M) networks being connected to the internet at large, inherit all the cyber-vulnerabilities of the standard Information Technology (IT) systems. Since perfect cyber-security and robustness is an idealistic construct, it is worthwhile to design intrusion detection schemes to quickly detect and mitigate the harmful consequences of cyber-attacks. Volumetric anomaly detection have been popularized due to their low-complexity, but they cannot detect low-volume sophisticated attacks and also suffer from high false-alarm rate. To overcome these limitations, feature-based detection schemes have been studied for IT networks. However these schemes cannot be easily adapted to M2M systems due to the fundamental architectural and functional differences between the M2M and IT systems. In this paper, we propose novel feature-based detection schemes for a general M2M uplink to detect Distributed Denial-of-Service (DDoS) attacks, emergency scenarios and terminal device failures. The detection for DDoS attack and emergency scenarios involves building up a database of legitimate M2M connections during a training phase and then flagging the new M2M connections as anomalies during the evaluation phase. To distinguish between DDoS attack and emergency scenarios that yield similar signatures for anomaly detection schemes, we propose a modified Canberra distance metric. It basically measures the similarity or differences in the characteristics of inter-arrival time epochs for any two anomalous streams. We detect device failures by inspecting for the decrease in active M2M connections over a reasonably large time interval. Lastly using Monte-Carlo simulations, we show that the proposed anomaly detection schemes have high detection performance and low-false alarm rate.

Novel, computationally simple and deterministic blind channel estimation schemes for PSK-OFDM and QAM-OFDM are proposed from legitimate transceivers as well as pilot-jammer's perspective. Their effectiveness in formulating the pilot-nulling attacks against OFDM is investigated. The estimators are based on the Least Squares (LS) principle and capitalize on the finite alphabet property of the information symbols. Phase ambiguity resolution requires no reference symbols while scalar ambiguity is resolved at the expense of a single pilot tone. The multipath characteristics of the channel between the target and the jammer are assumed to be Time Division Duplex (TDD) reciprocal. Simulation results are provided to demonstrate the performance of the proposed methods in channel estimation at the pilot locations as well as designing effective pilot-nulling attacks. The proposed methods estimate the jammer channel to the target perfectly to launch precise pilot-nulling attacks on OFDM in very low noise environment. The performance degrades with increasing noise.

This paper presents a novel physical layer scheme for multiple-input multiple-output (MIMO) communication systems based on unsupervised deep learning (DL) using an autoencoder in an interference channel (IC) environment. Moreover, it extends the single-input single-output (SISO) channel autoencoder to consider fading channel conditions. In both schemes, two physical layer communication system encoders and decoders are jointly optimized in the presence of interference to minimize their symbol error rate (SER). We analyze resulting SER performance for varying signal-to-interference-plus-noise-ratio (SINR) levels. Realistic channel effects; i.e. Rayleigh fading, are used while training the autoencoder system. For SISO systems, the autoencoder system in IC demonstrates significant performance improvement compared to the conventional single-user systems by eliminating interference when there is channel state information (CSI) at the transmitter. The MIMO autoencoder system also shows significant performance improvements compared to the conventional single-user MIMO systems at SINR levels higher than 16dB. MIMO systems with different number of antennas are simulated to analyze the change in the system complexity and scalability. The information required at the transmitter; i.e. CSI from both the intended and interference links, and the autoencoder training time increases with increasing number of antennas for the autoencoder-based MIMO systems.

This paper addresses a fundamental question of multi-agent knowledge distribution: what information should be sent to whom and when, with the limited resources available to each agent? Intelligent Knowledge Distribution is a framework that answers these questions. Communication requirements for multi-agent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multi-agent coordination on systems, including communications, this paper introduces the concept of action-based constraints on partially observable Markov decision processes, rewards based upon the value of information driven by Kullback-Leibler Divergence, and probabilistic constraint satisfaction through discrete optimization and Markov chain Monte Carlo analysis. Intelligent Knowledge Distribution is driven by determining the information content an agent believes another agent will obtain by receiving certain information, along with the importance or relevance of that information to the system objective. To perform constraint analysis on an infinite-horizon policy, policies are represented as a Finite State Controller allowing Markov chain Monte Carlo analysis to determine a probabilistic level of guarantee that the constraints will be satisfied. The analysis of performance for an example mission presented in this paper shows the constrained controllers, during the highest constraint seen in simulations, can be constructed to meet minimal constraint guarantees (80%) while impacting the optimal value less than 50%, where the unconstrained optimal controller only satisfied the constraint 10% of the time.

In this paper, we introduce a secure energy trading auction approach to schedule the power plant limited resources during peak hours time slots. In the proposed auction model, the power plant serving a power grid shares with the smart meters its available amount of resources that is expected during the next future peak time slot; smart meters expecting a demand for additional power participate in the power auction by submitting bids of their offered price for their requested amount of power. In order to secure the power auction and protect smart meters' privacy, homomorphic encryption through Paillier cryptosystem is used to secure the bidding values and ensure avoiding possible insincere behaviors of smart meters or the grid operator (i.e. the auctioneer) to manipulate the auction for their own benefits. In addition, we use a payment rule that maximizes the power plant's revenue. We propose an efficient power scheduling mechanism to distribute the operator's limited resources among smart meters participating in the power auction. Finally, we present simulation results for the performance of our secure power scheduling auction mechanism.

The increase in data storage and power consumption at data-centers has made it imperative to design energy efficient distributed storage systems (DSS). The energy efficiency of DSS is strongly influenced not only by the volume of data, frequency of data access and redundancy in data storage, but also by the heterogeneity exhibited by the DSS in these dimensions. To this end, we propose and analyze the energy efficiency of a heterogeneous distributed storage system in which$n$storage servers (disks) store the data of$R$distinct classes. Data of class$i$is encoded using a$(n,k_{i})$erasure code and the (random) data retrieval requests can also vary across classes. We show that the energy efficiency of such systems is closely related to the average latency and hence motivates us to study the energy efficiency via the lens of average latency. Through this connection, we show that erasure coding serves the dual purpose of reducing latency and increasing energy efficiency. We present a queuing theoretic analysis of the proposed model and establish upper and lower bounds on the average latency for each data class under various scheduling policies. Through extensive simulations, we present qualitative insights which reveal the impact of coding rate, number of servers, service distribution and number of redundant requests on the average latency and energy efficiency of the DSS.

The proliferation of cloud computing has forced data centers to support a large number of dynamic resources such as virtual machines and storage volumes from any physical location within the data center. This has led to highly volatile and unpredictable traffic patterns. The emergence of cloud applications that exchange large volumes of information have created large persistent flows that need to coexist with other types of traffic. As a result, a number of data center topologies have been proposed to support cloud applications. One of the topologies that has gained prominence is the Clos network. On one hand, Clos networks provide many nice properties, such as equidistant hops, bandwidth, and latency. On the other hand the routing of diverse traffic flows still remains an issue. Current routing methods such as equal-cost multi-path (ECMP) routing that rely on flow hashing over equal cost paths do not take into consideration traffic volume nor network state. In this paper, we investigate routing in Clos topologies. We formulate optimal flow routing in Clos networks as a binary multicommodity flow problem and present optimized flow re-routing, a modified version of the binary multicommodity flow problem that uses a heuristics approach to minimize hash collisions. We also propose FlowFit, a practical optimization method that uses network state to optimally re-assign flows to links. Our experimental and simulation results show a significant improvement in bisection bandwidth and flow completion time over ECMP.

Distributed storage systems (DSSs) in the presence of an external wiretapper are considered. A DSS is parameterized by (n, k, d), in which the data are stored across n nodes (each with storage capacity α), and must be recoverable by accessing the contents stored on any k out of n nodes. If a node fails, any d ≥ k out of (n - 1) nodes help in the repair (regeneration) of the failed node (by sending dβ units of repair data, where β ≤ α), so that the data can still be recovered from the DSS. For such a (n, k, d)-DSS, security from the two types of wiretappers is investigated: 1) Type-I (node data) wiretapper, which can read the data stored on any ℓ <; k nodes and 2) Type-II (repair data) wiretapper, which can read the data that is used to repair a set of ℓ failed nodes. The focus of this paper is on the optimal tradeoff between the storage (α) and the repair bandwidth (dβ) in presence of a Type-I/Type-II wiretapper and the practically relevant constraint of exact repair in which a failed node must be replaced by its exact replica. In this paper, several new results and outer bounds for the storage-versus-exact-repair-bandwidth tradeoff(s) are obtained for the Type-I and Type-II security problems. Furthermore, new outer bounds are presented for the Type-II problem, which hold for general (n, k, d, ℓ) parameters. It is shown that these outer bounds strictly improve upon the existing cutset-based outer bounds. The key technical contribution of this paper is in developing novel information theoretic converse proofs for these problems. From our optimal characterization results, we show that in a Type-II setting, the only efficient point in the storage-versus-exact-repair-bandwidth tradeoff is the minimum bandwidth regenerating (MBR) point corresponding to α = dβ. This is in sharp contrast to the Type-I setting in which the optimal tradeoff allows a spectrum of operating points beyond the MBR point.

Abstract In this paper, the authors derived the analytical bit error rate expressions for orthogonal frequency division multiple access and single‐carrier frequency‐division multiple access (SC‐FDMA) in Rayleigh slow‐fading channel for binary phase‐shift keying/quadrature phase‐shift keying/16‐quadrature amplitude modulations under pilot jamming and pilot symbol‐assisted channel estimation. Beginning with the bit error rate analysis from the general case of pilot symbol‐assisted channel estimation technique in Rayleigh slow‐fading channel, the expressions are first modified for different modulations and then further customized to account for the Zero‐Forcing equalization in frequency or time direction with application respectively to orthogonal frequency division multiple access or SC‐FDMA without and with pilot jamming. Piecewise‐linear interpolation is used for its simplicity. The simulation results match perfectly with the theoretical predictions except for some discrepancies with SC‐FDMA, which imply that the generalized equations developed here have to be further modified to account for system‐specific features like discrete Fourier transform precoding for SC‐FDMA. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Orthogonal frequency‐division multiplexing (OFDM) has become the manifest modulation choice for 4G standards. Timing acquisition and carrier frequency offset synchronization are prerequisite to OFDM demodulation and must be performed often. Most of the OFDM methods for synchronization were not designed with security in mind. In particular, we analyze the performance of a maximum likelihood synchronization estimator against highly correlated jamming attacks. We present a series of attacks against OFDM timing acquisition: preamble whitening, the false preamble attack, preamble warping, and preamble nulling.The performance of OFDM synchronization turns out to be very poor against these attacks, and a number of mitigation strategies and security improvements are discussed. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract This paper introduces, investigates, and proposes power‐efficient equalization jamming attacks against the orthogonal frequency division multiplexing (OFDM) and possible countermeasures. Signals known a priori, called pilot tones, are employed in the conventional OFDM‐based systems to estimate the channel response and perform equalization. Attacks against these pilot tones can hamper equalization and degrade target's performance. This paper begins with noise‐based pilot tone jamming and moves onto pilot nulling attack. The paper embraces two mitigation strategies of randomizing the pilot tones. The paper presents mathematical model of effective noise per symbol and simulation results for OFDM systems with conventional and randomized pilot‐tones under such attacks. The paper concludes that the noise‐based pilot tone attacks are power efficient than conventional attacks, and the pilot nulling is capable of doing the most damage, but in the expense of additional channel state information. The paper also claims that the mitigation strategies offer a remedy, but works relatively poorly when no jammer is present. Copyright © 2015 John Wiley & Sons, Ltd.

Caching is a viable solution for alleviating the severe capacity crunch in modern content centric wireless networks. Parts of popular files are pre-stored in users' cache memories such that at times of heavy demand, users can be served locally from their cache content thereby reducing the peak network load. In this work, we consider a central server assisted caching network where files are jointly delivered to users through multicast transmissions. For such a network, we develop a new information theoretic lower bound on the fundamental cache storage vs. transmission rate tradeoff, which strictly improves upon the best known existing bounds. The new bounds are used to establish the approximate storage vs. rate tradeoff of centralized caching to within a constant multiplicative factor of 8.

Data centers need to support a large number of dynamic resources such as virtual machines and storage volumes from any physical location within the data center. This has led to highly volatile and unpredictable traffic patterns. The emergence of applications that exchange large volumes of information have created large persistent flows that need to coexist with other traffic flows. Current load balancing protocols that rely on static hashing over equal cost paths do not take into consideration traffic volume nor network state. These protocols are no longer sufficient for achieving high bandwidth utilization. In this work we investigate load balancing in data center networks with folded-Clos architectures. We propose two load-balancing methods; “Selective Randomized Load Balancing” and “FlowFit”, the first method is an enhanced form of randomized load balancing, while the second is a form of dynamic load balancing that relies on a global view of the network state, for assigning flows to links. We build our topology using a centralized OpenFlow controller and open virtual switches. Our experimental and simulation results show a significant increase in bisection bandwidth over static hashing. These gains are achieved with minimal changes to the control and data planes.

In this paper, we present our spectrum sharing algorithm between a multi-input multi-output (MIMO) radar and Long Term Evolution (LTE) cellular system with multiple base stations (BS)s. We analyze the performance of MIMO radars in detecting the angle of arrival, propagation delay and Doppler angular frequency by projecting orthogonal waveforms onto the null-space of interference channel matrix. We compare and analyze the radar's detectable target parameters in the case of the original radar waveform and the case of null-projected radar waveform. Our proposed spectrum-sharing algorithm causes minimum loss in radar performance by selecting the best interference channel that does not cause interference to the ithLTE base station due to the radar signal. We show through our analytical and simulation results that the loss in the radar performance in detecting the target parameters is minimal when our proposed spectrum sharing algorithm is used to select the best channel onto which radar signals are projected.

National Telecommunications and Information Administration (NTIA) has proposed vast exclusions zones between radar and Worldwide Interoperability for Microwave Access (WiMAX) systems which are also being considered as geographic separations between radars and 3.5 GHz Long Term Evolution (LTE) systems without investigating the changes induced by the distinct nature of the LTE systems as opposed to WiMAX. This paper performs a detailed system-level analysis of the interference effects from shipborne radar systems into LTE macro cells and outdoor small cells. Even though the results reveal impacts of radar interference on LTE systems performance, they provide clear indications of conspicuously narrower exclusion zones for LTE vis-à-vis those proposed for WiMAX and pave the way toward deploying 3.5 GHz LTE within the exclusion zones.

This paper explores a collocated overlapped-multiple-input multiple-output (MIMO) antenna architecture and a spectrum sharing algorithm via null space projection (NSP) for radar-communications coexistence. In the overlapped-MIMO architecture, the transmit array of a collocated MIMO radar is partitioned into a number of subarrays that are allowed to overlap. Each of the antenna elements has signals orthogonal to others in the same subarray and to the other subarrays. The proposed architecture not only improves sidelobe suppression to reduce interference to communications system, but also enjoys the advantages of MIMO radar without sacrificing the desirable characteristics such as beampattern and SNR gain. The radar-centric spectrum sharing then projects the radar signal onto the null space of the communications system's interference channel to avoid interference from the radar. Numerical results are presented that show the performance of the proposed design in terms of overall beampattern and sidelobe levels of the radar waveform and finally shows a comparison of the proposed system with existing collocated MIMO radar architectures.

In this paper, we consider a resource allocation with carrier aggregation optimization problem in long term evolution (LTE) cellular networks. In our proposed model, users are running elastic or inelastic traffic. Each user equipment (UE) is assigned an application utility function based on the type of its application. Our objective is to allocate multiple carriers resources optimally among users in their coverage area while giving the user the ability to select one of the carriers to be its primary carrier and the others to be its secondary carriers. The UE's decision is based on the carrier price per unit bandwidth. We present a price selective centralized resource allocation with carrier aggregation algorithm to allocate multiple carriers resources optimally among users while providing a minimum price for the allocated resources. In addition, we analyze the convergence of the algorithm with different carriers rates. Finally, we present simulation results for the performance of the proposed algorithm.

Caching is emerging as a vital tool for alleviating the severe capacity crunch in modern content-centric wireless networks. The main idea behind caching is to store parts of the popular content in end-users' memory and leverage the locally stored content to reduce peak data rates. By jointly designing content placement and delivery mechanisms, recent works have shown order-wise reduction in transmission rates in contrast to traditional methods. In this paper, we consider the secure caching problem with the additional goal of minimizing information leakage to an external wiretapper. The fundamental cache memory versus transmission rate tradeoff for the secure caching problem is characterized. Rather surprisingly, these results show that security can be introduced at a negligible cost, particularly for large number of files and users. It is also shown that the rate achieved by the proposed caching scheme with secure delivery is within a constant multiplicative factor from the information-theoretic optimal rate for almost all parameter values of practical interest.

Multi-tenant distributed storage systems (DSS) exhibit heterogeneity in several dimensions such as fault tolerance requirements, nature of job requests etc. However the primary focus in literature has been on homogenous models for DSS. In this paper, we investigate the impact of heterogeneity on the latency performance of multi-tenant distributed storage systems. Heterogeneity across multiple tenants is modeled via potentially different fault tolerance requirements and different job arrival rates. We consider a heterogeneous multi-tenant DSS with n servers that store the data of R distinct traffic classes. The data of each traffic class i is stored in the DSS using a different (n, fc¿) Maximum-Distance-Separable (MDS) code. Depending upon the traffic class, the data may be frequently or infrequently accessed and is modeled using different job arrival rates for the traffic classes. We then present a queuing theoretic analysis of the proposed model and establish upper and lower bounds on the average latency for each traffic class for various scheduling policies. Using simulations, we verify the accuracy of the derived bounds and present qualitative insights on the impact of heterogeneity and scheduling policies on the mean latency of different classes.

Dynamic spectrum access (DSA) technologies offer solutions to the spectral crowding associated with static frequency allocation. Hierarchical DSA networks aim at allowing secondary users to efficiently utilize licensed spectrum, while still protecting primary users and ensuring them first priority to spectrum access. However, these networks are often multi-tiered and the concept of different operating policies for secondary users has arisen. In this study we consider the idea of two operating modes in a stochastically modeled DSA network. Observations from the radio frequency (RF) environment are classified using self organizing maps (SOMs). The discretized observations are then utilized to develop hidden Markov models (HMMs) of each type of radio. These models are developed for a variable number of map sizes and hidden states then sequence matched against unknown radios in order to determine identification performance. The system is shown to perform extremely well for certain combinations of SOM sizes and HMM states.

This paper details the derivation of the closed-form BER expressions of PSK modulation in Rayleigh faded AWGN channel for OFDM and SC-FDMA under jamming and channel estimation error. It has addressed the issue by first approaching the problem from general case of PSK modulation in Rayleigh faded channel with channel estimation error and then narrowing it down for the specific case of OFDM and SC-FDMA under jamming. The coherent demodulation of PSK modulated OFDM/SC-FDMA symbols is performed by Frequency Domain Zero Forcing Equalization using Pilot Symbol Assisted Channel Estimation (PSACE) technique. Piecewise-linear interpolation is used for its low computational complexity.

Distributed storage systems (DSS) in the presence of a passive eavesdropper are considered in this paper. A typical DSS is characterized by 3 parameters (n, k, d) where, a file is stored in a distributed manner across n nodes and can be recovered entirely from any k out of n nodes. Whenever a node fails, d ∈ [k, n) nodes help in repairing the failed node. The focus of this work is on the exact repair capabilities of a DSS, where a failed node is replaced with an identical node. Securing this DSS from passive eavesdropping attacks is studied in this paper. The eavesdropper is capable of wiretapping the repair process of a subset of nodes in the storage system. The main contribution of this paper is the optimal characterization of the secure storage-vs-exact-repair-bandwidth tradeoff region which prior to this work was unknown. We focus on the simplest nontrivial instances of this problem, namely (n, k, d) = (3, 2, 2) and (4, 3, 3), and present novel information-theoretic converse proofs that validate these optimal tradeoff regions.

Caching has emerged as a vital tool in modern communication systems for reducing peak data rates by allowing popular fles to be pre-fetched and stored locally at end users' devices. In this paper, the concept of information theoretic security for content delivery with decentralized caching is introduced. The proposed secure caching scheme allows users to cache content in a decentralized manner and reduces the peak rate by leveraging local and global caching gains, while securely delivering requested content to the users. The analysis of the secure decentralized caching problem is presented which shows that the proposed scheme introduces security at a negligible cost in terms of achievable rate when compared to insecure decentralized caching schemes, particularly for large number of fles and users. It is also shown that the rate of the proposed secure scheme is within a constant multiplicative factor from the information theoretic optimal rate for feasible values of problem parameters.

In this paper, we consider resource allocation optimization problem in cellular networks for different types of users running multiple applications simultaneously. In our proposed model, each user application is assigned a utility function that represents the application type running on the user equipment (UE). The network operators assign a subscription weight to each UE based on its subscription. Each UE assigns an application weight to each of its applications based on the instantaneous usage percentage of the application. Additionally, UEs with higher priority assign applications target rates to their applications. Our objective is to allocate the resources optimally among the UEs and their applications from a single evolved node B (eNodeB) based on a utility proportional fairness policy with priority to realtime application users. A minimum quality of service (QoS) is guaranteed to each UE application based on the UE subscription weight, the UE application weight and the UE application target rate. We propose a two-stage rate allocation algorithm to allocate the eNodeB resources among users and their applications. Finally, we present simulation results for the performance of our rate allocation algorithm.

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

Jamming attacks can significantly impact the performance of wireless communication systems. In addition to reducing the capacity, such attacks may lead to insurmountable overhead in terms of retransmissions and increased power consumption. In this paper, we consider the multiple-input single-output (MISO) broadcast channel (BC) in the presence of a jamming attack in which a subset of the receivers can be jammed at any given time. Further, countermeasures for mitigating the effects of such jamming attacks are presented. The effectiveness of these antijamming countermeasures is quantified in terms of the degrees-of-freedom (DoF) of the MISO BC under various assumptions regarding the availability of the channel state information (CSIT) and the jammer state information at the transmitter (JSIT). The main contribution of this paper is the characterization of the DoF region of the two user MISO BC under various assumptions on the availability of CSIT and JSIT. Partial extensions to the multiuser broadcast channels are also presented.

Orthogonal Frequency Division Multiplexing (OFDM) uses pilot tones to estimate and equalize the channel's frequency response whose locations and values are known. This makes OFDM prone to pilot tone based jamming attacks. Our previous research analyzed such jamming attacks. This paper builds on this idea and proposes approaches to mitigate the effect by randomizing pilot tone locations. It also presents OFDM channel and equalizer model, formulates channel estimation error, and then undertakes an analysis of OFDM system for randomized pilot tones, verifying through simulation. Both analytics and simulation show that randomized pilot tone schemes perform worse when a jammer is not present but perform better in the presence of pilot tone based jammer. Of the two proposed randomization schemes, the one that constrains the distance between random tones performs better in both scenarios.

Jamming attacks can significantly impact the performance of wireless communication systems, and lead to insurmountable overhead in terms of re-transmissions and increased power consumption. In this paper, we consider the multi-user multiple-input single-output (MISO) broadcast channel (BC) in the presence of jamming attacks in which a subset of the users can be selectively jammed at any given time. We present countermeasures for mitigating the effects of such jamming attacks. The effectiveness of these anti-jamming countermeasures is quantified in terms of the degrees-of-freedom (DoF) of the MISO BC under various assumptions regarding the availability of the channel state information (CSIT) and the jammer state information at the transmitter (JSIT).

Orthogonal Frequency Division Multiplexing (OFDM) is used in many modern communications systems. Timing and frequency synchronization in an OFDM system are critical to performance and must be carried out early and often. Current synchronization methods, such as those developed by Schmidl and Cox [1], were not designed to be robust to adversarial signals. A series of attacks against the preamble synchronization stage have been developed and demonstrated to debilitate OFDM receivers. Multiple attacks against the frequency offset error estimation stage are discussed, and some possible improvements to OFDM synchronization algorithms are suggested.

We study and compare modeling an end-to-end network by conventional, bivariate, and exponential observation hidden Markov processes. Furthermore, effects of μ-law, Lindle-Boyde-Gray, and uniform quantization approaches on the modeling granularity is explored. We performed experiments using synthetic representative data from a traffic-modeler autoregressive modular process and the Network Simulator software as well as over-the-Internet experiments with real data to contrast the fidelity produced from each model. Comparing statistical signatures of the model-generated data with those of the training sequence indicates that accompanying Lindle-Boyde-Gray quantization with conventional or bivariate hidden Markov processes significantly improves the modeling fidelity.

This paper presents power efficient asynchronous off-tone jamming attacks on orthogonal frequency division multiplexing (OFDM). Often wireless communication systems have to operate in environments, which are prone to unknown interference from adversaries such as jamming, which can lead to degradation of performance. This paper begins with presenting existing conventional jamming attacks, such as barrage jamming (BJ), partial band jamming (PBJ), single-tone jamming (STJ), and multi-tone jamming (MTJ) on OFDM and then proposes new power efficient jamming strategies. It is known that signal with frequency offset can affect more spectrum than the occupied bandwidth as the signal energy gets smeared into adjacent spectrum while performing FFT in the OFDM receiver, and thus create inter-channel interference (ICI). This paper builds on this idea, and presents new asynchronous single off-tone and multiple off-tone jamming attacks on OFDM. We first present our channel model, and then undertake an analysis of OFDM systems under off-tone jamming attacks, verifying the idea through simulation. Both analysis and simulation show that off-tone jamming attacks have more adverse effect than jammer aligned with the received signal.

In this paper, we consider resource allocation optimization problem in the fourth generation long-term evolution (4G-LTE) with elastic and inelastic real-time traffic. Mobile users are running either delay-tolerant or real-time applications. The users applications are approximated by logarithmic or sigmoidal-like utility functions. Our objective is to allocate resources according to the utility proportional fairness policy. Prior utility proportional fairness resource allocation algorithms fail to converge for high-traffic situations. We present a robust algorithm that solves the drawbacks in prior algorithms for the utility proportional fairness policy. Our robust optimal algorithm allocates the optimal rates for both high-traffic and low-traffic situations. It prevents fluctuation in the resource allocation process. In addition, we show that our algorithm provides traffic-dependent pricing for network providers. This pricing could be used to flatten the network traffic and decrease the cost per bandwidth for the users. Finally, numerical results are presented on the performance of the proposed algorithm.

We propose projecting radar waveform onto the null space of an interference channel matrix between the radar and a communication system as a solution for coexistence of radar and communication systems in the same band. This approach assumes that the cognitive radar has full knowledge of the interference channel and tries to modify its signal vectors in such a way that they fall in the null space of the channel matrix. We investigate the effects of null space projections on radar performance and target parameter identification both analytically and quantitatively by using maximum likelihood and Cramer-Rao bound performance bounds to estimate target direction in the two cases of no null space projection and null space projection. Through simulation we demonstrate that by optimal choice of the number of antennas, the performance and target identification capabilities of radar in our method are competitive with that of traditional radar waveforms, while simultaneously guaranteeing coexistence between radar and communication systems.

This paper explores effects of pilot based jamming attacks with synchronization mismatches on MIMO-OFDM systems. Both MIMO and OFDM based systems employ known data called pilot tones to estimate the channel frequency response and perform equalization. Jamming pilot tones can potentially disrupt the channel estimation and equalization, thus making the communication impossible. Our earlier research focused on efficient jamming algorithms, such as pilot jamming for OFDM systems and singularity attack for MIMO systems. It has been shown that pilot based jamming is more efficient than broadband attacks on entire signal. However, pilot based jamming may suffer from time and frequency synchronization mismatches that can degrade the efficacy of attacks. This paper presents channel and equalizer model, builds on various pilot based jamming attacks and further examines the cause and effect of mismatches in the jamming attack. Through simulation we demonstrate that singularity jamming achieves 3 dB gain over barrage jamming at 0.4 bit error rate. For 25% and 50% symbol time offset, singularity jamming loses about 0.25 dB and 1 dB gain, respectively, when perfect CSI is available for the jammer. At 0.2 bit error rate, 0.2 and 0.5 normalized frequency offset, singularity jamming loses about 1 dB and 3 dB over synchronized jamming, respectively, when CSI with 20% error is available at the jammer side.

This paper investigates efficient jamming attacks against MIMO-enabled systems. Previous research has focused on jamming data transmissions. We instead focus on jamming channel sounding symbols, and introduce the MIMO Singularity Attack, which attempts to reduce the rank of the channel gain matrix estimate by the receiver through transmission of specific jamming signals. All MIMO systems need a method to estimate and equalize their channel, whether through channel reciprocity or sounding, and most modern OFDM-based MIMO waveforms use sounding via OFDM pilot tones. We develop the constraints associated with jamming MIMO sounding channels, and further describe how these attacks specifically impact data symbol estimates for OFDM pilot-based sounding systems.

This paper describes an efficient and effective design of Robust Spatio-Temporal Prediction based on Student's t distribution, namely, St-RSTP, to provide estimations based on observations over spatio-temporal neighbors. The proposed St-RSTP is more resilient to outliers or other small departures from model assumptions than its ancestor, the Spatio-Temporal Random Effects (STRE) model. STRE is a state-of-the-art statistical model with linear order complexity for large scale processing. However, it assumes Gaussian observations, which has the well-known limitation of non-robustness. In our StRSTP design, the measurement error follows Student's t distribution, instead of a traditional Gaussian distribution. This design reduces the influence of outliers, improves prediction quality, and keeps the problem analytically intractable. We propose a novel approximate inference approach, which approximates the model into the form that separates the high dimensional latent variables into groups, and then estimates the posterior distributions of different groups of variables separately in the framework of Expectation Propagation. As a good property, our approximate approach degeneralizes to the standard STRE based prediction, when the degree of freedom of the Student's t distribution is set to infinite. Extensive experimental evaluations based on both simulation and real-life data sets demonstrated the robustness and the efficiency of our Student-t prediction model. The proposed approach provides critical functionality for stochastic processes on spatio-temporal data.

Crucial to the successful deployment of cognitive radio networks, security issues have begun to receive research interests recently. In this paper, we focus on defending against the jamming attack, one of the major threats to cognitive radio networks. Secondary users can exploit the flexible access to multiple channels as the means of anti-jamming defense. We first investigate the situation where a secondary user can access only one channel at a time and hop among different channels, and model it as an anti-jamming game. Analyzing the interaction between the secondary user and attackers, we derive a channel hopping defense strategy using the Markov decision process approach with the assumption of perfect knowledge, and then propose two learning schemes for secondary users to gain knowledge of adversaries to handle cases without perfect knowledge. In addition, we extend to the scenario where secondary users can access all available channels simultaneously, and redefine the anti-jamming game with randomized power allocation as the defense strategy. We derive the Nash equilibrium for this Colonel Blotto game which minimizes the worst-case damage. Finally, simulation results are presented to verify the performance.

Orthogonal Frequency Division Multiplexing (OFDM) uses pilot tones to estimate the channel's frequency response and perform equalization. It is commonly known that jamming pilot tones is more efficient than broadband attacks against an entire OFDM signal. This paper builds on this idea and introduces the pilot nulling attack, which is considerably more efficient than simple pilot jamming, by driving received pilot energy as close to zero as possible. This paper presents our channel and equalizer model, and then undertakes an analysis of OFDM under these attacks, verifying the assessment through simulation. For a target bit error rate of 0.4, QPSK underlying modulation, and pilot tone density of 1/8, we discover that pilot jamming is roughly 2 dB more efficient than barrage jamming, and pilot nulling is roughly 7.5 dB more efficient than barrage jamming. In all cases, pilot nulling is capable of fully denying the target signal at 4 dB of signal-to-jamming ratio by driving the QPSK bit error rate to 0.5.

In this paper we study how key dynamic spectrum access operating parameters impact radios' ability to fairly access wireless spectrum. Additionally we investigate intentional spectrum access policies that allow priority radios (e.g. public safety or defense) to preempt access to the spectrum by lower priority (e.g. commercial) radio networks.

Various spectrum management schemes have been proposed in recent years to improve the spectrum utilization in cognitive radio networks. However, few of them have considered the existence of cognitive attackers who can adapt their attacking strategy to the time-varying spectrum environment and the secondary users' strategy. In this paper, we investigate the security mechanism when secondary users are facing the jamming attack, and propose a stochastic game framework for anti-jamming defense. At each stage of the game, secondary users observe the spectrum availability, the channel quality, and the attackers' strategy from the status of jammed channels. According to this observation, they will decide how many channels they should reserve for transmitting control and data messages and how to switch between the different channels. Using the minimax-Q learning, secondary users can gradually learn the optimal policy, which maximizes the expected sum of discounted payoffs defined as the spectrum-efficient throughput. The proposed stationary policy in the anti-jamming game is shown to achieve much better performance than the policy obtained from myopic learning, which only maximizes each stage's payoff, and a random defense strategy, since it successfully accommodates the environment dynamics and the strategic behavior of the cognitive attackers.

We present a physical-layer fingerprint-embedding scheme for orthogonal frequency-division-multiplexing (OFDM) transmissions, where the fingerprint signal conveys a low capacity communication suitable for authenticating the transmission and further facilitating secure communications. Our system strives to embed the fingerprint message into the noise subspace of the channel estimates obtained by the receiver, using a number of signal spreading techniques. When side information of the channel state is known and leveraged by the transmitter, the performance of the fingerprint embedding can be improved. When channel state information is not known, blind spreading techniques are applied. The fingerprint message is only visible to aware receivers who explicitly preform detection of the signal, but is invisible to receivers employing typical channel equalization. A taxonomy of overlay designs is discussed and these designs are explored through experiment using time-varying channel-state information (CSI) recorded from IEEE802.16e Mobile WiMax base stations. The performance of the fingerprint signal as received by a WiMax subscriber is demonstrated using CSI measurements derived from the downlink signal. Detection performance for the digital fingerprint message in time-varying channel conditions is also presented via simulation.

Spectrum sensing is required for many cognitive radio applications, including spectral awareness, interoperability, and dynamic spectrum access. Any approach is highly dependent on the signal environment in which it operates. Noise, multipath channel distortion, sample-rate offsets, and center-frequency mis-estimations can make feature-based signal detection difficult. Additionally, as these values change in a dynamic environment, statically-trained classifiers cannot track the evolving class statistics. Unsupervised learning allows a cognitive radio system to evolve its classifier as the radio environment evolves, without the need for expert-annotated signal samples. While this allows improved classifier performance, previous work has demonstrated that unsupervised techniques developed without security in mind suffer from classifier manipulation through transmission of carefully crafted signals that shift decision boundaries. This paper develops countermeasures to the class manipulation attacks that mitigates their efficacy, and shows the robustness of unsupervised learning for signal classification in an evolving RF environment.

Traditional cooperative communications consider dedicated-relays, while often such relays may not be available. In this paper, we consider wireless transceivers that relay signals in addition to their own primary communication mission. We consider a best-effort delivery policy, where a node is not obligated to devote energy to cooperatively relay signals, nor does it provide a guarantee of signal quality on retransmissions. Instead the relay sacrifices energy at its own discretion, with priority given to the primary communication mission. We consider one best-effort delivery problem: a system that transmits an additional relay signal within its original transmission energy budget while inducing minimal degradation to the primary-user's signal. To maintain this constraint, we consider the feasibility of reallocating energy from pilot signals used for channel estimation toward the relaying service, when channel conditions are stationary. We demonstrate that transmitter energy may be dynamically allocated between a relay component and a pilot component of the transmission using best-effort delivery. This power allocation is critical to system performance, since both the primary-user and the secondary-user may require pilot energy to correctly decode transmitted signals. Sub-optimal power allocation rules with respect to primary-user channel estimate mean-square error and pairwise error probability are derived.

A framework for introducing an extrinsic fingerprint signal to space-time coded transmissions at the physical layer is presented, where the fingerprint signal conveys a low capacity cryptographically secure authentication message of arbitrary length. The multi-bit digital fingerprint message conveyed by the fingerprint signal is available to all users within reception range and is used to authenticate the fingerprinted transmission. A novel approach is discussed where the fingerprint signaling mechanism mimics distortions similar to time-varying channel effects. Specifically, the fingerprint is detectable to receivers considering previous channel state information, but will be ignored by receivers equalizing according to current channel state information. Two example fingerprint signaling mechanisms and detection rules are presented based on pulse-amplitude keying and phase-shift keying approaches. The methods for obtaining the real (intrinsic) channel estimate, the extrinsic fingerprint message, and the primary transmission are analytically demonstrated using general pilot embedding schemes. The worst-case distortions caused by non-ideal equalization of a fingerprinted message are derived using the 2×2 Alamouti code. Simulation results including bit error rate (BER) and model mismatch error using a maximum-likelihood (ML) receiver are presented for both the primary and fingerprint signal, while authentication signal BERs lower than the primary signal are demonstrated.

A framework for introducing an extrinsic fingerprint signal to space-time coded transmissions at the physical-layer is presented, whereby the fingerprint signal conveys a low capacity digital communication suitable for authenticating the transmission and further facilitating secure communications. A novel approach is discussed where the fingerprint signal mimics distortions similar to time-varying channel effects. Specifically, the fingerprint signal is only visible to aware receivers considering previous channel state information (CSI) and is otherwise invisible to a receiver equalizing according to current CSI. An augmented signal is created consisting of the original transmission, or primary message, and the fingerprint message. An example fingerprint signal and detection rule are presented based on a phase-shift keying approach. Simulation results including bit error rate (BER) are presented for both the primary and fingerprint signals using the 2×2 Alamouti code, and authentication signal BERs lower than the primary signal are demonstrated.

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

Traditional decode-and-forward cooperation systems consider dedicated relays, while instead we consider wireless transceivers that cooperatively relay signals in addition to primary communication missions. A system that transmits the additional signal using a best-effort transmission policy within the original transmission energy constraint is considered. To maintain the original energy budget we consider the feasibility of reallocating energy from pilot signals toward the relaying service when channel conditions are stationary. Under the best-effort delivery policy, the node is not obligated to devote energy for relaying signals, nor does it provide a guarantee of signal quality to retransmissions. Instead the relay sacrifices energy at its own discretion, prioritizing the primary communication mission. Using the best-effort delivery policy, we derive an optimal power allocation rule that maintains a fixed symbol error rate for the relay's primary transmission, and further demonstrate cooperative communication gains using the proposed delivery method.

Cooperative spectrum sensing has been shown to be able to greatly improve the sensing performance in cognitive radio networks. However, if cognitive users belong to different service providers, they tend to contribute less in sensing in order to increase their own throughput. In this paper, we propose an evolutionary game framework to answer the question of "how to collaborate" in multiuser de-centralized cooperative spectrum sensing, because evolutionary game theory provides an excellent means to address the strategic uncertainty that a user/player may face by exploring different actions, adaptively learning during the strategic interactions, and approaching the best response strategy under changing conditions and environments using replicator dynamics. We derive the behavior dynamics and the evolutionarily stable strategy (ESS) of the secondary users. We then prove that the dynamics converge to the ESS, which renders the possibility of a de-centralized implementation of the proposed sensing game. According to the dynamics, we further develop a distributed learning algorithm so that the secondary users approach the ESS solely based on their own payoff observations. Simulation results show that the average throughput achieved in the proposed cooperative sensing game is higher than the case where secondary users sense the primary user individually without cooperation. The proposed game is demonstrated to converge to the ESS, and achieve a higher system throughput than the fully cooperative scenario, where all users contribute to sensing in every time slot.

Dynamic spectrum access (DSA), enabled by cognitive radio technologies, has become a promising approach to improve efficiency in spectrum utilization, and the spectrum auction is one important DSA approach, in which secondary users lease some unused bands from primary users. However, spectrum auctions are different from existing auctions studied by economists, because spectrum resources are interference-limited rather than quantity-limited, and it is possible to award one band to multiple secondary users with negligible mutual interference. To accommodate this special feature in wireless communications, in this paper, we present a novel multi-winner spectrum auction game not existing in auction literature. As secondary users may be selfish in nature and tend to be dishonest in pursuit of higher profits, we develop effective mechanisms to suppress their dishonest/collusive behaviors when secondary users distort their valuations about spectrum resources and interference relationships. Moreover, in order to make the proposed game scalable when the size of problem grows, the semi-definite programming (SDP) relaxation is applied to reduce the complexity significantly. Finally, simulation results are presented to evaluate the proposed auction mechanisms, and demonstrate the complexity reduction as well.

Dynamic spectrum access has become a promising approach to fully utilize the scarce spectrum resources. In a dynamically changing spectrum environment, it is very important to consider the statistics of different users' spectrum access so as to achieve more efficient spectrum allocation. In this paper, we propose a primary-prioritized Markov approach for dynamic spectrum access through modeling the interactions between the primary and the secondary users as continuous-time Markov chains (CTMC). Based on the CTMC models, to compensate the throughput degradation due to the interference among secondary users, we derive the optimal access probabilities for the secondary users, by which the spectrum access of the secondary users is optimally coordinated, and the spectrum dynamics are clearly captured. Therefore, a good tradeoff can be achieved between the spectrum efficiency and fairness. The simulation results show that the proposed primary-prioritized dynamic spectrum access approach under proportional fairness criterion achieves much higher throughput than the CSMA-based random access approaches and the approach achieving max-min fairness. Moreover, it provides fair spectrum sharing among secondary users with only small performance degradation compared to the approach maximizing the overall average throughput.

Dynamic spectrum access has become a promising approach to improve spectrum efficiency by adaptively coordinating different users' access according to spectrum dynamics. However, users who are competing with each other for spectrum may have no incentive to cooperate, and they may even exchange false private information about their channel conditions in order to get more access to the spectrum. In this paper, we propose a repeated spectrum sharing game with cheat-proof strategies. By using the punishment-based repeated game, users get the incentive to share the spectrum in a cooperative way; and through mechanism-design-based and statistics-based approaches, user honesty is further enforced. Specific cooperation rules have been proposed based on the maximum total throughput and proportional fairness criteria. Simulation results show that the proposed scheme can greatly improve the spectrum efficiency by alleviating mutual interference.

The ability to adapt to a changing RF environment is a major feature of cognitive radios. As the interference and noise characteristics change, the radio should be able to select a waveform optimal for a particular scenario. However, more frequent adaptation requires more frequent distribution of radio parameters, increasing the overhead and decreasing available proportion of capacity for transmitting data. This paper presents a basic model for analyzing modulation adaption overhead in a point-to-point cognitive radio link, and simulates a few specific cases of interest. We examine how quickly a radio can adapt without that overhead actually causing worse overall performance. Our major results are as follows: to decrease overhead cognitive engines should be placed at receivers, not transmitters; modulation adaptation in Rayleigh fading environments is only beneficial under high-SNR conditions; the higher the average SNR, the more frequently we can adapt to fading channels and see rate gains; and adaptation to interference channels offers a significant performance gain.

Cooperative spectrum sensing has been shown to greatly improve the sensing performance in cognitive radio networks. However, if the cognitive users belong to different service providers, they tend to contribute less in sensing in order to achieve a higher throughput. In this paper, we propose an evolutionary game framework to study the interactions between selfish users in cooperative sensing. We derive the behavior dynamics and the stationary strategy of the secondary users, and further propose a distributed learning algorithm that helps the secondary users approach the Nash equilibrium with only local payoff observation. Simulation results show that the average throughput achieved in the cooperative sensing game with more than two secondary users is higher than that when the secondary users sense the primary user individually without cooperation.

In order to fully utilize spectrum, auction-based dynamic spectrum allocation has become a promising approach which allows unlicensed wireless users to lease unused bands from spectrum license holders. Because spectrum resources are reusable by users far apart, in some scenarios, spectrum is more efficiently utilized by awarding one band to multiple secondary users simultaneously, which distinguishes it from traditional auctions where only one user can be the winner. However, the multi-winner auction is a new concept posing new challenges in the traditional auction mechanisms, because such mechanisms may yield low revenue and are not robust to some newly-emerging collusion. Therefore, in this paper, we propose an efficient mechanism for the multi-winner spectrum auction with collusion- resistant pricing strategies, in which the optimal spectrum allocation can be solved by binary linear programming and the pricing is formulated as a convex optimization problem. Furthermore, a greedy algorithm is proposed to reduce complexity for multi- band auctions. Simulation results are presented to evaluate our proposed auction mechanisms.

Cognitive radios have the ability to sense their RF environment and adapt their transmission parameters to perform optimally in any situation. Part of this involves selecting the best modulation type for a particular channel. In this paper we consider a variable-rate, variable-power, adaptive, m-ary quadrature amplitude modulation (M-QAM) scheme in a single-user communication scenario. The channel between the transmitter and receiver is assumed to be a Rayleigh block-fading channel. Each block is divided into training and data phases. During the training phase, the receiver estimates the channel and feeds the estimate back to the transmitter. During the data phase, the transmitter sends its message by adapting the size of the M- QAM constellation. We first find a closed-form expression that relates the bit error rate (BER) to the constellation size of the M-QAM, and therefore to the data rate of our system. Then, for a given target BER, we maximize the data rate over the training parameters, which are the training signal, the training duration, and the training power. When these optimum parameters are used in a MATLAB implementation, we find that the target BER is matched to within an order of magnitude, and the resulting data rate is close to the theoretical limit.

This paper studies the secrecy capacity of unicast communication in a wireless erasure network setting in the presence of a wire-tapper. From an information-theoretic setting of perfect secrecy, both upper bounds and achievable secrecy rates are presented. Secrecy capacity is determined in closed form for a class of broadcast constrained erasure networks.

The interference temperature model was proposed by the FCC in 2003 as a way to dynamically manage and allocate spectrum resources. It would allow unlicensed radios to sense their current RF environment and transmit in licensed bands, provided their transmission does not raise the interference temperature for that frequency band over the interference temperature limit. It never received much interest because nobody was sure exactly how to use it or how if it would work. This research focuses on a mathematical analysis of the interference temperature model in an effort to examine the relationships between the capacity achieved by the unlicensed network and the interference caused to the licensed network. We develop a model for the RF environment and determine probability distributions governing interference temperature as a function of various elements in the model. We then determine bounds on the amount of interference caused by implementing such a system. We examine model environments for a wireless WAN and a wireless LAN, each coexisting with a licensed carrier. For each, we quantify both the impact on the licensed signal and also the capacity achieved by our underlay network. By substituting numeric values for RF environments in which the interference temperature model might be applied, we show that achievable capacity is very small, while the impact the licensee can be very large. Based on this, we propose alternate usages for interference temperature and ways to boost capacity.

Abstract To combat recent spectral overcrowding in unlicensed bands, the FCC has been investigating new ways to manage RF resources. The idea is to let people use licensed frequencies, provided they can guarantee interference perceived by the primary license holders will be minimal. With advances in software and cognitive radio, practical ways of doing this are on the horizon. In 2003, the FCC released a memorandum seeking comment on the interference temperature model (ITM) for controlling spectrum use. Formally defining and analyzing techniques for implementing this model are the primary goals of this paper. Two interpretations of the ITM are developed and for each, we examine tradeoffs between power, bandwidth, and capacity. From these relationships, algorithms for computing RF transmission parameters are developed. These algorithms seek to maximize both capacity and spectral efficiency for a given RF environment. Additionally, we describe ways to choose a center frequency that will optimize future performance, subject to the constraints of the ITM. Copyright © 2007 John Wiley & Sons, Ltd.