Complete Channel Knowledge Research Articles

Multi-user transmit beamforming for broadband acoustic systems

Multi-user transmit beamforming for acoustic systems based on orthogonal frequency division multiplexing (OFDM) modulation is studied and demonstrated. Transmit beamforming consists of assigning weight coefficients to multiple transmit elements, such that the signals transmitted from the array add constructively at the users' receive sides. In an optimal setting, the transmitter assigns weights that are proportional to the channel transfer functions to the users. However, the assumption of complete channel knowledge is not entirely accurate, as the transmitter obtains the channels via feedback from the users. Thus, the transmitter has partial knowledge of the channels, as their estimates are not only noisy, but can be outdated if the channels have changed during the time it took to close the feedback loop. These adversities are notably pronounced in time-varying acoustic channels, as the feedback delay is large as compared to the channel coherence time due to the low speed of sound propagation. To counteract this problem, a technique that targets only those features of the channels that can withstand the feedback delay is proposed. Such features are the users' angles of arrival of the principal propagation paths. The principal paths are stable paths that do not experience rapid fluctuations and are thus varying sufficiently slowly that they can tolerate long feedback delays. The design concepts are demonstrated using experimental over-the-air transmissions in an indoor environment of an acoustic communications testbed.

Multi-armed Bandit Online Learning Based on POMDP in Cognitive Radio

In cognitive radio, most of existing research efforts devoted to spectrum sharing have two weakness as follows. First, they are largely formulated as a Markov decision process (MDP), which requires a complete knowledge of channel. Second, most of the studies are online learning based on perceived channel. To solve the above problems, a new algorithm is proposed in this paper: if the authorized user exists in the current channel, Second user will send conservatively in low rate, or send aggressively. When sending conservatively, the state of the channel is not directly observable, the problem turns out to be Partially Observable Markov Decision Process (POMDP).We first establish the optimal threshold when the channel is known, then consider the optimal transmission when the channel is unknown and model for multi-armed bandit. We get the optimal K-conservative policy through the UCB algorithm and improve the convergence speed by UCB-TUNED algorithm. Simulation and analysis results show that it is the same result of K-conservative policy no matter the multiarmed bandit online learning under not fully known channel or the optimal threshold policy under known channel .At the same time, we improve the convergence speed by UCB-TUNED algorithm.

Maximizing capacity with trellis exploration aided limited feedback precoder design for multiuser MIMO-MAC

In this paper, a linear trellis based precoding technique is proposed for maximizing the multiuser MIMO capacity in a multiple access channel with inter symbol interference (ISI). We use the trellis exploration algorithm to design a precoding matrix that minimizes the interference from other users by allocating power in the appropriate eigenmodes. Employing a finite number of phases, the precoder matrix for each user is first custom designed at the receiver. Then a bit sequence indicating the phase indices of the optimized precoding matrix elements for the corresponding user is fed back to the transmitter. We show that under this approach, the sum rate capacity achieved is comparable to the optimal sum-rate capacity employing the well-known water filling solution with complete channel knowledge at the transmitter for spatio-temporal vector coding (STVC). Our simulations for various multiuser MIMO cases, show that the per user precoding with limited feedback and equal power allocation strategy achieves desirable capacity gains relative to the eigenbeamforming and Grassmannian precoding.

Grassmannian beamforming for multiple-input multiple-output wireless systems

Transmit beamforming and receive combining are simple methods for exploiting the significant diversity that is available in multiple-input multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal beamforming vector; both are hard to realize. In this article, a quantized maximum signal-to-noise ratio (SNR) beamforming technique is proposed where the receiver only sends the label of the best beamforming vector in a predetermined codebook to the transmitter. By using the distribution of the optimal beamforming vector in independent and identically distributed Rayleigh fading matrix channels, the codebook design problem is solved and related to the problem of Grassmannian line packing. The proposed design criterion is flexible enough to allow for side constraints on the codebook vectors. Bounds on the codebook size are derived to guarantee full diversity order. Results on the density of Grassmannian line packings are derived and used to develop bounds on the codebook size given a capacity or SNR loss. Monte Carlo simulations are presented that compare the probability of error for different quantization strategies.

Source localization in a partially known shallow‐water channel

The source localization in shallow water is beset with problems arising from the presence of a large number of correlated multipaths. Nevertheless, given a complete knowledge of the water channel it is definitely possible to localize a source. A complete knowledge of the channel, however, is rarely available under most practical conditions. A new approach is proposed wherein the bottom reflection coefficients are not required; hence the bottom conditions need not be known. Further, because of the use of signal subspace for localization, the proposed approach is robust against the background noise (−20 dB) and channel depth uncertainty (10λ). All these nice features of the proposed approach are possible only when the array size is large (≳40 sensors).