Rohit Negi

Effective capacity: a wireless link model for support of quality of service

To facilitate the efficient support of quality of service (QoS) in next-generation wireless networks, it is essential to model a wireless channel in terms of connection-level QoS metrics such as data rate, delay, and delay-violation probability. However, the existing wireless channel models, i.e., physical-layer channel models, do not explicitly characterize a wireless channel in terms of these QoS metrics. In this paper, we propose and develop a link-layer channel model termed effective capacity (EC). In this approach, we first model a wireless link by two EC functions, namely, the probability of nonempty buffer, and the QoS exponent of a connection. Then, we propose a simple and efficient algorithm to estimate these EC functions. The physical-layer analogs of these two link-layer EC functions are the marginal distribution (e.g., Rayleigh-Ricean distribution) and the Doppler spectrum, respectively. The key advantages of the EC link-layer modeling and estimation are: 1) ease of translation into QoS guarantees, such as delay bounds; 2) simplicity of implementation; and 3) accuracy, and hence, efficiency in admission control and resource reservation. We illustrate the advantage of our approach with a set of simulation experiments, which show that the actual QoS metric is closely approximated by the QoS metric predicted by the EC link-layer model, under a wide range of conditions.

Pilot Tone Selection For Channel Estimation In A Mobile Ofdm System

Channel estimation for mobile OFDM systems requires transmission of pilot tones. This paper addresses the important issue of selecting these pilot tones, so as to achieve a good quality estimate. It is shown that the best set of tones to be used are those which are equally spaced. Furthermore, it is shown using the case of a first order Markov channel, that it is more efficient to use a few pilot tones in all symbols, rather than use all tones as pilot tones in some symbols.

Combined ML and DFE decoding for the V-BLAST system

This paper proposes to combine maximum-likelihood (ML) decoding and decision feedback equalization (DFE) for the Vertical Bell Laboratories Layered Space-Time (V-BLAST) system. In the new decoding algorithm, we perform ML decoding for the first p subchannels, and use the DFE procedure for the remaining subchannels. We mathematically show that the new decoding scheme increases the diversity order for the worst subchannel from 1 to p, and verify it by computer simulation. Also, we propose an ordering scheme which gives the best performance for the worst subchannel, and show that an SNR gain equal to the number of transmit antennas can be achieved by the suggested ordering.

Capacity of power constrained ad-hoc networks

Throughput capacity is a critical parameter for the design and evaluation of ad-hoc wireless networks. Consider n identical randomly located nodes, on a unit area, forming an ad-hoc wireless network. Assuming a fixed per node transmission capability of T hits per second at a fixed range, it has been shown that the uniform throughput capacity per node r(n) is /spl otimes/(T//spl radic/nlogn). We consider an alternate communication model, with each node constrained to a maximum transmit power P/sub 0/ and capable of utilizing W Hz of bandwidth. Under the limiting case W/spl rarr//spl infin/, such as in ultra wide band networks, the uniform throughput per node is /spl otimes/((nlogn)/sup /spl alpha/-1/2/ (upper bound) and /spl Omega/(n/sup (/spl alpha/-1)/2//(logn)/sup (/spl alpha/+1)/2/) (achievable lower bound). These bounds demonstrate that throughput increases with node density n, in contrast to previously published results. This is the result of the large bandwidth, and the assumed power and rate adaptation, which alleviate interference. Thus, the significance of physical layer properties on the capacity of ad-hoc wireless networks is demonstrated.

Delay-constrained capacity with causal feedback

A block-fading channel model is considered, and a K-block delay constraint is imposed on data transmission. The key consideration is that the channel state information is fed back to the transmitter in a causal manner. A general cost function /spl mu/(x) is considered in solving the delay-constrained transmission problem, under the short-term and the long-term power constraints. A causal power adaptation strategy is needed to maximize the cost function, hence dynamic programming is found to give the optimum solution. The general cost function is then specialized to the cases of expected and outage capacities. In the case of expected capacity, it is observed that optimizing the transmitted power does not give much benefit at high signal-to-noise ratio (SNR), but provides a substantial gain at low SNR. At low SNR, it is proved that the capacity increases by a factor of approximately log K/m, due to power adaptation, when the channel fades according to the /spl chi//sub 2m//sup 2/ statistics. In the case of outage capacity, it is shown that the optimum power adaptation solution to the long-term constraint problem provides a substantial SNR gain at both low and high values of SNR. Random coding bounds are derived for the outage capacity algorithms.

Message-in-a-bottle: user-friendly and secure key deployment for sensor nodes

Existing protocols for secure key establishment all rely on an unspecified mechanism for initially deploying secrets to sensor nodes. However, no commercially viable and secure mechanism exists for initial setup. Without a guarantee of secure key deployment, the traffic over a sensor network cannot be presumed secure. To address this problem, we present a user-friendly protocol for the secure deployment of cryptographic keys in sensor networks. We propose a collection of five techniques to prevent an attacker from eavesdropping on key deployment. To demonstrate feasibility for real-world use, we implement our protocol on Telos motes and conduct a user study.

Effective capacity-based quality of service measures for wireless networks

An important objective of next-generation wireless networks is to provide quality of service (QoS) guarantees. This requires a simple and efficient wireless channel model that can easily translate into connection-level QoS measures such as data rate, delay and delay-violation probability. To achieve this, in Wu and Negi (IEEE Trans. on Wireless Communications 2(4) (2003) 630–643), we developed a link-layer channel model termed effective capacity, for the setting of a single hop, constant-bit-rate arrivals, fluid traffic, and wireless channels with negligible propagation delay. In this paper, we apply the effective capacity technique to deriving QoS measures for more general situations, namely, (1) networks with multiple wireless links, (2) variable-bit-rate sources, (3) packetized traffic, and (4) wireless channels with non-negligible propagation delay.

Blind OFDM symbol synchronization in ISI channels

We present a new algorithm for blind symbol synchronization in orthogonal frequency division multiplexing (OFDM) systems. The new algorithm declares symbol synchronization when a certain autocorrelation matrix, constructed from the received signal, achieves minimum rank. Unlike previously proposed blind algorithms, the new rank method guarantees correct symbol synchronization, even in the presence of intersymbol interference. Also, it does not assume that the OFDM time samples are i.i.d. In particular, the rank method works even with OFDM systems that employ pulse shaping. The increased complexity of the algorithm would be acceptable for systems, such as fixed-receiver broadcast systems, that require guaranteed synchronization under all conditions.

Signal Processing for Near 10 Tbit/in

Two-dimensional magnetic recording (TDMR) is a new magnetic recording paradigm that aims to record one bit of information in one or a few grains, with the goal of achieving a recording density of nearly 10 Tbit/in2. In addition to the usual noise, a TDMR channel experiences the problem that some bits are never recorded because of the randomness of grain size and location. Thus, it is believed that a key component of a TDMR channel is two-dimensional (2-D) signal processing along with a strong error correction code. In this study, the TDMR channel is investigated based on a random Voronoi grain model and a signal processing architecture is proposed. Here, a 2-D linear minimum mean squared error (LMMSE) equalizer and a low-density parity-check (LDPC) code are employed and the effects of unwritten bits are modeled by a Gaussian mixture model. In numerical simulations, the proposed architecture shows the feasibility of user bit densities near 10 Tbit/in2 for media with a 20 Tgrains/in2 grain density.

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Two-dimensional magnetic recording (TDMR) is a novel architecture for magnetic recording systems proposed to achieve densities towards 10 Tb/in2. TDMR differs from other solutions in that it does not require a complete redesign of the head or medium for its gains, relying instead on powerful 2-D codes and signal processing to reliably store and retrieve the information. To explore the viability of TDMR via simulation, models for the 2-D medium, writing, and readback are needed. In this paper, we propose models for these processes and discuss ongoing progress in 2-D experimental waveform retrieval that will be used to validate the models. We show some initial results achieved using these models which demonstrate the viability of TDMR and that will provide an indication of what densities may be possible with TDMR, when the 2-D codes and detectors are completed.