Shengli Zhou

Cross-Layer combining of adaptive Modulation and coding with truncated ARQ over wireless links

We developed a cross-layer design which combines adaptive modulation and coding at the physical layer with a truncated automatic repeat request protocol at the data link layer, in order to maximize spectral efficiency under prescribed delay and error performance constraints. We derive the achieved spectral efficiency in closed-form for transmissions over Nakagami-m block fading channels. Numerical results reveal that retransmissions at the data link layer relieve stringent error control requirements at the physical layer, and thereby enable considerable spectral efficiency gain. This gain is comparable with that offered by diversity, provided that the maximum number of transmissions per packet equals the diversity order. Diminishing returns on spectral efficiency, that result when increasing the maximum number of retransmissions, suggest that a small number of retransmissions offers a desirable delay-throughput tradeoff, in practice.

The challenges of building mobile underwater wireless networks for aquatic applications

The large-scale mobile underwater wireless sensor network (UWSN) is a novel networking paradigm to explore aqueous environments. However, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, floating node mobility, and high error probability, are significantly different from ground-based wireless sensor networks. The novel networking paradigm poses interdisciplinary challenges that will require new technological solutions. In particular, in this article we adopt a top-down approach to explore the research challenges in mobile UWSN design. Along the layered protocol stack, we proceed roughly from the top application layer to the bottom physical layer. At each layer, a set of new design intricacies is studied. The conclusion is that building scalable mobile UWSNs is a challenge that must be answered by interdisciplinary efforts of acoustic communications, signal processing, and mobile acoustic network protocol design.

Queuing with adaptive modulation and coding over wireless links: cross-Layer analysis and design

Assuming there are always sufficient data waiting to be transmitted, adaptive modulation and coding (AMC) schemes at the physical layer have been traditionally designed separately from higher layers. However, this assumption is not always valid when queuing effects are taken into account at the data link layer. In this paper, we analyze the joint effects of finite-length queuing and AMC for transmissions over wireless links. We present a general analytical procedure, and derive the packet loss rate, the average throughput, and the average spectral efficiency (ASE) of AMC. Guided by our performance analysis, we introduce a cross-layer design, which optimizes the target packet error rate of AMC at the physical layer, to minimize thpacket loss rate and maximize the average throughput, when combined with a finite-length queue at the data link layer. Numerical results illustrate the dependence of system performance on various parameters, and quantify the performance gain due to cross-layer optimization. Our focus is on the single user case, but we also discuss briefly possible applications to multiuser scenarios.

Optimal transmitter eigen-beamforming and space-time block coding based on channel mean feedback

Optimal transmitter designs obeying the water-filling principle are well-documented; they are widely applied when the propagation channel is deterministically known and regularly updated at the transmitter. Because channel state information is impossible to be known perfectly at the transmitter in practical wireless systems, we design, in this paper, an optimal multiantenna transmitter based on the knowledge of mean values of the underlying channels. Our optimal transmitter design turns out to be an eigen-beamformer with multiple beams pointing to orthogonal directions along the eigenvectors of the correlation matrix of the estimated channel at the transmitter and with proper power loading across beams. The optimality pertains to minimizing an upper bound on the symbol error rate, which leads to better performance than maximizing the expected signal-to-noise ratio (SNR) at the receiver. Coupled with orthogonal space-time block codes, two-directional eigen-beamforming emerges as a more attractive choice than conventional one-directional beamforming with uniformly improved performance, without rate reduction, and without essential increase in complexity. With multiple receive antennas and reasonably good feedback quality, the two-directional eigen-beamformer is also capable of achieving the best possible performance in a large range of transmit-power-to-noise ratios, without a rate penalty.

Sparse Channel Estimation for Multicarrier Underwater Acoustic Communication: From Subspace Methods to Compressed Sensing

In this paper, we present various channel estimators that exploit the channel sparsity in a multicarrier underwater acoustic system, including subspace algorithms from the array precessing literature, namely root-MUSIC and ESPRIT, and recent compressed sensing algorithms in form of Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP). Numerical simulation and experimental data of an OFDM block-by-block receiver are used to evaluate the proposed algorithms in comparison to the conventional least-squares (LS) channel estimator. We observe that subspace methods can tolerate small to moderate Doppler effects, and outperform the LS approach when the channel is indeed sparse. On the other hand, compressed sensing algorithms uniformly outperform the LS and subspace methods. Coupled with a channel equalizer mitigating intercarrier interference, the compressed sensing algorithms can handle channels with significant Doppler spread.

Application of compressive sensing to sparse channel estimation

Compressive sensing is a topic that has recently gained much attention in the applied mathematics and signal processing communities. It has been applied in various areas, such as imaging, radar, speech recognition, and data acquisition. In communications, compressive sensing is largely accepted for sparse channel estimation and its variants. In this article we highlight the fundamental concepts of compressive sensing and give an overview of its application to pilot aided channel estimation. We point out that a popular assumption - that multipath channels are sparse in their equivalent baseband representation - has pitfalls. There are over-complete dictionaries that lead to much sparser channel representations and better estimation performance. As a concrete example, we detail the application of compressive sensing to multicarrier underwater acoustic communications, where the channel features sparse arrivals, each characterized by its distinct delay and Doppler scale factor. To work with practical systems, several modifications need to be made to the compressive sensing framework as the channel estimation error varies with how detailed the channel is modeled, and how data and pilot symbols are mixed in the signal design.

Achieving the Welch bound with difference sets

Consider a codebook containing N unit-norm complex vectors in a K-dimensional space. In a number of applications, the codebook that minimizes the maximal cross-correlation amplitude (I/sub max/) is often desirable. Relying on tools from combinatorial number theory, we construct analytically optimal codebooks meeting, in certain cases, the Welch lower bound. When analytical constructions are not available, we develop an efficient numerical search method based on a generalized Lloyd algorithm, which leads to considerable improvement on the achieved I/sub max/ over existing alternatives. We also derive a composite lower bound on the minimum achievable I/sub max/ that is effective for any codebook size N.

How accurate channel prediction needs to be for transmit-beamforming with adaptive modulation over Rayleigh MIMO channels?

Adaptive modulation improves the system throughput considerably by matching transmitter parameters to time-varying wireless fading channels. Crucial to adaptive modulation is the quality of channel state information at the transmitter. In this paper, we first present a channel predictor based on pilot symbol assisted modulation for multiple-input multiple-output Rayleigh fading channels. We then analyze the impact of the channel prediction error on the bit error rate performance of a transmit-beamformer with adaptive modulation that treats the predicted channels as perfect. Our numerical results reveal the critical value of the normalized prediction error, below which the predicted channels can be treated as perfect by the adaptive modulator; otherwise, explicit consideration of the channel imperfection must be accounted for at the transmitter.

Quantifying the power loss when transmit beamforming relies on finite-rate feedback

Transmit beamforming achieves optimal performance in systems with multiple transmit antennas and a single receive antenna from both the capacity and the received signal-to-noise ratio (SNR) perspectives but ideally requires perfect channel knowledge at the transmitter. In practical systems where the feedback link can only convey a finite number of bits, transmit beamformer designs have been extensively investigated using either the outage probability or the average SNR as the figure of merit. In this paper, we study the symbol error rate (SER) for transmit beamforming with finite-rate feedback in a multi-input single-output setting. We derive an SER lower bound that is tight for good beamformer designs. Comparing this bound with the SER corresponding to the ideal case, we quantify the power loss due to the finite-rate constraint across the entire SNR range.

Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM

Space time coding has by now been well documented as an attractive means of achieving high data rate transmissions with diversity and coding gains, provided that the underlying propagation channels can be accounted for. We rely on redundant linear precoding to develop a (semi-)blind channel estimation algorithm for space time (ST) orthogonal frequency division multiplexing (OFDM) transmissions with Alamoutis (see IEEE J. Select. Areas Commun., vol.16, p.1451-58, Oct. 1998) block code applied on each subcarrier. We establish that multichannel identifiability is guaranteed up to one or two scalar ambiguities, regardless of the channel zero locations and the underlying signal constellations, when distinct or identical precoders are employed for even and odd indexed symbol blocks. With known pilots inserted either before or after precoding, we resolve the residual scalar ambiguities and show that distinct precoders require half the number of pilots than identical precoders to achieve the same channel estimation accuracy. Simulation results confirm our theoretical analysis and illustrate that the proposed semi-blind algorithm is capable of tracking slow channel variations and improving the overall system performance relative to competing differential ST alternatives.