The distance-weighted rate achievable in wireless networks that utilize multiple antennas and multi-user links is analyzed. The transport capacities of vector multiple-access and broadcast channels are given; these network topologies are used at the physical layer of an ad-hoc network to obtain achievable distance-weighted rate regions for a multiple-antenna wireless network. These regions are obtained by considering interfering multiple-access and broadcast channels in all possible link configurations. Multi-hop routing is shown to increase the capacity regions significantly. A centralized technique for link activation which results in networks with throughput close to capacity is developed. We give an analytic upper bound on the capacity and show that in networks with an asymptotically large number of nodes, the throughput grows with the number of antennas, as well as with the expected path loss between nodes. Numerical examples agree with the analysis and show the benefit of interfering multiple-access links over interfering broadcast or point-to-point links for a uniform per-node power constraint.

A lower bound on the capacity of trained space-time modulation is presented for the case of a time-varying channel. We consider the case where the channel consists of two components of variable strength: a specular component from line-of-sight or strong coherent multipath, and a time-varying diffuse component due to a mobile receiver or scatterers. The training period and the data period are assumed quasi-static, with the time-autocorrelation function of the channel used to model the variation between each sub-block. The training signal, training signal length, power allocation and training frequency that optimize the capacity bound are derived. We compare the effective SNR of this bound to that of differential modulation at high SNR, and find it to be lower. We find the best number of antennas to use at high SNR with differential modulation, and compare it with the optimal value for trained modulation. For our time-varying channel model, trained modulation often has a higher achievable rate than differential modulation. Our results are supported by several numerical examples.

Recent results on the throughput achievable with wireless networks have not fully considered multiple antennas and multi-user links. We introduce these topics by giving the transport capacity of the multiple-antenna multiple-access and broadcast channels. We use these topologies at the physical layer of an ad-hoc network to obtain achievable distance-weighted rates for a multi-antenna wireless network. These values are obtained by maximizing the distance-weighted rate over all combinations of point-to-point, uplink, and downlink topologies, respectively. A distributed algorithm which seeks a Nash equilibrium is used to optimize the transmit covariance matrices for the centralized topology search. Numerical examples with a uniform per-node power constraint show the benefit of uplink topologies over downlink and point-to-point topologies, especially at high transmit power, high numbers of antennas, and a large number of nodes

Recent theoretical results describing the sum-capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum-rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the energy of the transmitted signal. The paper is comprised of two parts. In this second part, we show that, after the regularization of the channel inverse introduced in the first part, a certain perturbation of the data using a "sphere encoder" can be chosen to further reduce the energy of the transmitted signal. The performance difference with and without this perturbation is shown to be dramatic. With the perturbation, we achieve excellent performance at all signal-to-noise ratios. The results of both uncoded and turbo-coded simulations are presented.

Recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the power of the transmitted signal. This work is comprised of two parts. In this first part, we show that while the sum capacity grows linearly with the minimum of the number of antennas and users, the sum rate of channel inversion does not. This poor performance is due to the large spread in the singular values of the channel matrix. We introduce regularization to improve the condition of the inverse and maximize the signal-to-interference-plus-noise ratio at the receivers. Regularization enables linear growth and works especially well at low signal-to-noise ratios (SNRs), but as we show in the second part, an additional step is needed to achieve near-capacity performance at all SNRs.

Rapid temporal variations in wireless channels pose a significant challenge for space-time modulation and coding algorithms. This letter examines the performance degradation that results when time-varying flat fading is encountered when using trained and unitary space-time modulation. Performance is characterized for a channel having a constant specular component plus a time-varying diffuse component. A first-order autoregressive (AR) model is used to characterize diffuse channel coefficients that vary from symbol to symbol, and is shown to lead to an effective signal-to-noise ratio (SNR) that decreases with time. Differential modulation is shown to have an advantage in effective SNR over trained unitary modulation at high power. Simulation results are provided to support our analysis.

We analyze the performance of trained and differential space-time modulation for channels with a constant specular component and time-varying diffuse fading. We examine the case where the channel varies from sample to sample within a space-time symbol matrix according to a first-order time-varying model. We show that the effect of the time-varying diffuse channel can be described by an effective signal-to-noise ratio (SNR) that decreases with time. We derive pairwise probability of error expressions based on these effective SNR values that accurately describe performance for unitary modulation. We quantify the significant advantage that differential modulation provides at high SNR where the effect of the time-varying channel dominates. At low SNR where additive noise dominates, we note that trained modulation with perfect channel state information provides a 3-dB advantage over differential modulation, but decoding based on a maximum likelihood channel estimate yields worse performance than differential modulation at all SNR values. Simulation results are provided to support our analysis.

The pairwise probability of error for trained unitary space-time modulation over channels with a constant specular component and time-varying diffuse fading is derived. We consider the case where the diffuse channel varies from sample to sample within a symbol according to a first-order AR model. Our previous results are reviewed which show that the effect of the time-varying diffuse channel can be described by an effective SNR that decreases with time. We derive pairwise probability of error expressions using these effective SNR values, which are shown by simulation to describe the performance accurately.

The pairwise probability of error for differential unitary space-time modulation for channels with a constant specular component and time-varying diffuse fading is derived in this paper. We consider the case where the channel varies from sample to sample within a symbol according to a first-order Gauss-innovations model. Our previous results are reviewed which show that the effect of the time-varying diffuse channel can be described by an effective SNR that decreases with time. We derive pairwise probability of error expressions using these effective SNR values, which are shown by simulation to accurately describe performance.

Using a model based on the time-autocorrelation function for time-varying MIMO channels, we find a lower bound on capacity for trained modulation, and find the training signal, training power, and length of training signal which maximize this bound. An approximation for the training frequency that maximizes the bound at high SNR is given for Jakes' fading model. The capacity for differential unitary space-time modulation is discussed and compared with that for trained modulation. We present several numerical examples to illustrate our results.

The fading channel is a significant problem in many communications environments. We examine the performance of unitary space-time modulation in a time-varying channel. We use a Gauss-Markov model of the continuously varying channel to characterize performance of differential and trained modulation. We find a performance ceiling at high SNR where the effect of the changing channel dominates. We show that while trained modulation provides an advantage at low SNR, above a certain SNR differential modulation gives better performance. We conclude with simulation results that support our analysis.

The fading channel is a significant problem in many communications environments. We examine the performance of unitary space-time modulation in a time-varying channel. We use a Gauss-Markov model of the continuously varying channel to characterize performance of differential and trained modulation. We find a performance ceiling at high SNR where the effect of the changing channel dominates. We show that while trained modulation provides an advantage at low SNR, above a certain SNR differential modulation gives better performance. We conclude with simulation results that support our analysis.

A lossy image compression algorithm designed for high-speed, high quality data applications is described. The algorithm consists of a vector quantizer followed by a modified Huffman entropy encoder. The quantizer is a mean-removed, adaptive-search, residual vector quantizer. A few details of a high-speed hardware implementation for reconnaissance are given, as well as an example of the performance of two variations of the algorithm.

Full-search vector quantization (VQ) provides optimal results only with high memory and computational cost. We describe the computational and memory requirements of tree-structured VQ, residual VQ (RVQ), and tree-structured RVQ. We present multiple-rate, adaptive-search implementations of these VQ structures, and simulation results with video sequences. Tree-structured RVQ provides up to 1.5 db PSNR quality improvements over RVQ, as well as significant perceptual improvement. These algorithms maintain many of the benefits of full-search VQ, while providing trade-offs between computational, storage, and performance requirements.

We describe a method of using a Lagrange multiplier to make a locally optimal trade off between rate and distortion in the motion search for video sequences, while maintaining a constant bit rate channel. Simulation of this method shows that it gives up to 3.5 dB PSNR improvement in a high motion sequence. A locally rate-distortion (R-D) optimal mode selection mechanism is also described. This method also gives significant quality benefit over the nominal method. Though the benefit of these techniques is significant when used separately, when the optimal mode selection is combined with the R-D optimal motion search, it does not perform much better than the codec does with only the R-D optimal motion search.