This paper presents a novel system architecture to suppress in-band artifacts (IBAs) generated from out-of-band (OOB) interferers, including reciprocal mixing by the local oscillator’s (LO) spurs and phase noise (PN), third-order intermodulation (IM3) artifacts, and harmonic down-conversion (HDC) artifacts. Theory and design procedure are explained, and measurement results from a prototype taped out in 45 nm RF SOI process are presented. The receiver was designed for the frequency range of 1.2 GHz–2.4 GHz and achieved a noise figure (NF) of 3.1 dB–6.2 dB, blocker −1 dB compression point (B1dB) of −10.3 dBm, and OOB third-order input-referred intercept point (IIP3) of 9.3 dBm on average, before artifact suppression. Measurements were performed on 16-quadrature amplitude modulated (16QAM) signals with modulated and unmodulated OOB interferers to show artifact suppression for various kinds of IBA. For each IBA, artifact suppression performance was assessed across frequency and interferer power. Interference tolerance improvement of up to 38 dB was achieved. Additionally, reconstruction of the artifacts for the cases of spur and HDC was demonstrated, showing simultaneous recovery of two signals, providing a form of carrier aggregation.

The Internet is growing rapidly, causing concerns that the address space for IPv4 may soon be used up. Taking a snapshot of the Internet size can help in planning the future evolution and capabilities of the Internet as well as planning the implementation of the next generation IPv6. We extended our previous work on measuring the number of publicly accessible web servers to make more accurate measurements of the size of the Internet. An improved importance sampling approach is introduced which achieves a significant gain over Monte Carlo methods. The growth of the Internet is mapped by periodic measurements of the number of active web servers.

Measuring the size of the Internet via Monte Carlo sampling requires probing a large portion of the Internet protocol (IP) address space to obtain an accurate estimate. However, the distribution of information servers on the Internet is highly nonuniform over the IP address space. This allows us to design probing strategies based on importance sampling for measuring the prevalence of an information service on the Internet that are significantly more effective than strategies relying on Monte Carlo sampling. We present thorough analysis of our strategies together with accurate estimates for the current size of the Internet Protocol Version 4 (IPv4) Internet as measured by the number of publicly accessible web servers and FTP servers.

The problem of distinguishing reliably between signaling formats in the presence of noise, interference, unknown dispersive channel conditions, as well as timing and frequency mismatches is addressed. Methods based on a combination of blind equalization and universal classification are presented and their performance is assessed through simulations.

The problem of implementing self-adaptive algorithms in real-time is addressed. Self-adaptive equalization determines the transmitted sequence without using a training sequence. Tree search procedures have been shown to be more effective than dynamic programming. Simulation results for tree search procedures based on the M-algorithm are presented. The focus is on the effects of channel order, sequence length and modulation format on the BER. The performance of the M-algorithm is compared with traditional approaches.

The design of single-user decorrelating receivers employing finite-precision sequences for despreading is considered. The problem is formulated as a nonlinear bounded integer optimization problem which is shown to be network performance (NP)-hard. A branch-and-bound algorithm for finding the best finite-precision decorrelating sequence is described. Numerical examples demonstrate that the loss in performance between the optimum, infinite-precision, and the best finite-precision decorrelator is small even for large channel occupancies. Some suboptimum algorithms are investigated which greatly reduce the computational complexity associated with finding good finite precision decorrelator sequences.

The problem of designing and analyzing suboptimal detectors via statistical distance measures is considered. As a preliminary result, we show that only the minimum and maximum probability of error are valid measures of discrimination between the input statistics. This result would seem then to imply that the use of distance measures in this context can be inappropriate. However, to overcome this apparent obstacle, we demonstrate explicit relationships between various f-divergences and the loss in performance of an arbitrary detector relative to the optimal detector. In particular, we establish both upper and lower bounds on the performance loss of a suboptimal detector in terms of the "distance" between the pertinent statistics of both the optimal and suboptimal detectors. While designing detectors by minimizing these upper bounds can be an elusive task, in many practical cases, the lower bound presented herein holds with equality. In this case, minimizing the separation of the output statistics of the detector with respect to a particular f-divergence equivalently minimizes the resulting probability of error of the detector. To facilitate design, other researchers have established conditions under which one may design arbitrary detection strategies with respect to a specific f-divergence (Kullback-Leibler distance being a principal example). We extend this approach by deriving necessary and sufficient conditions under which one may design detection strategies with respect to an arbitrarily chosen f-divergence. Thus when these conditions are met, one may optimize a detector with respect to the most analytically tractable distance measure to obtain the minimum probability of error detector over a selected class of detection strategies. Examples demonstrating the utility of this theory for the problem of designing optimal linear detectors and optimal signal sets are presented.>

Two simple structures employing multilayer perceptrons are proposed for demodulation of spread-spectrum signals in both synchronous and asynchronous Gaussian channels. The optimum receiver is used to benchmark the performance of the proposed receiver; in particular, it is proved to be instrumental in identifying the decision regions for the neural networks. The neutral networks are trained for the demodulation of signals via backpropagation-type algorithms. A modified backpropagation-type algorithm is introduced for single-user and multiuser detection with near-optimum performance that could have applications in other classification and pattern recognition problems. A comparative performance analysis of the three receivers, optimum, conventional, and the one employing neural networks, is carried out via Monte Carlo simulations. An importance sampling technique is employed to reduce the number of simulations necessary to evaluate the performance of these receivers in a multiuser environment. In examples given, the receiver significantly outperforms the conventional receiver.>

A method based on recursive computation of the expected number of attempts and successes during the collision resolution phase of an access control algorithm is introduced for the design of near-optimum control strategies for multiple access collision channels with ternary and binary feedback. With this approach it is possible to circumvent the extremely difficult and still unsolved problem of finding the access control algorithm which achieves the highest throughput by settling for a near-optimum solution. The key to the design of the algorithms is to approximate the originally infinite-dimensional optimization problem by a one-dimensional optimization problem. In the ternary feedback case, the proposed algorithm achieves a throughput virtually identical to the highest throughput reported. Several forms of binary feedback are considered, and algorithms are introduced that achieve the highest throughput reported.>