Bits About the Channel: Multi-round Protocols for Two-way Fading Channels

Most communication systems use some form of feedback, often related to channel state information. In this paper, we study diversity multiplexing tradeoff for both FDD and TDD systems, when both receiver and transmitter knowledge about the channel is noisy and potentially mismatched. For FDD systems, we first extend the achievable tradeoff region for 1.5 rounds of message passing to get higher diversity compared to the best known scheme, in the regime of higher multiplexing gains. We then break the mold of all current channel state based protocols by using multiple rounds of conferencing to extract more bits about the actual channel. This iterative refinement of the channel increases the diversity order with every round of communication. The protocols are on-demand in nature, using high powers for training and feedback only when the channel is in poor states. The key result is that the diversity multiplexing tradeoff with perfect training and K levels of perfect feedback can be achieved, even when there are errors in training the receiver and errors in the feedback link, with a multi-round protocol which has K rounds of training and K-1 rounds of binary feedback. The above result can be viewed as a generalization of Zheng and Tse, and Aggarwal and Sabharwal, where the result was shown to hold for K=1 and K=2 respectively. For TDD systems, we also develop new achievable strategies with multiple rounds of communication between the transmitter and the receiver, which use the reciprocity of the forward and the feedback channel. The multi-round TDD protocol achieves a diversity-multiplexing tradeoff which uniformly dominates its FDD counterparts, where no channel reciprocity is available.Comment: Submitted to IEEE Transactions on Information Theor

Discrimination on the Grassmann Manifold: Fundamental Limits of Subspace Classifiers

We present fundamental limits on the reliable classification of linear and affine subspaces from noisy, linear features. Drawing an analogy between discrimination among subspaces and communication over vector wireless channels, we propose two Shannon-inspired measures to characterize asymptotic classifier performance. First, we define the classification capacity, which characterizes necessary and sufficient conditions for the misclassification probability to vanish as the signal dimension, the number of features, and the number of subspaces to be discerned all approach infinity. Second, we define the diversity-discrimination tradeoff which, by analogy with the diversity-multiplexing tradeoff of fading vector channels, characterizes relationships between the number of discernible subspaces and the misclassification probability as the noise power approaches zero. We derive upper and lower bounds on these measures which are tight in many regimes. Numerical results, including a face recognition application, validate the results in practice.Comment: 19 pages, 4 figures. Revised submission to IEEE Transactions on Information Theor

Power-Controlled Feedback and Training for Two-way MIMO Channels

Most communication systems use some form of feedback, often related to channel state information. The common models used in analyses either assume perfect channel state information at the receiver and/or noiseless state feedback links. However, in practical systems, neither is the channel estimate known perfectly at the receiver and nor is the feedback link perfect. In this paper, we study the achievable diversity multiplexing tradeoff using i.i.d. Gaussian codebooks, considering the errors in training the receiver and the errors in the feedback link for FDD systems, where the forward and the feedback are independent MIMO channels. Our key result is that the maximum diversity order with one-bit of feedback information is identical to systems with more feedback bits. Thus, asymptotically in $\mathsf{SNR}$, more than one bit of feedback does not improve the system performance at constant rates. Furthermore, the one-bit diversity-multiplexing performance is identical to the system which has perfect channel state information at the receiver along with noiseless feedback link. This achievability uses novel concepts of power controlled feedback and training, which naturally surface when we consider imperfect channel estimation and noisy feedback links. In the process of evaluating the proposed training and feedback protocols, we find an asymptotic expression for the joint probability of the $\mathsf{SNR}$ exponents of eigenvalues of the actual channel and the estimated channel which may be of independent interest.Comment: in IEEE Transactions on Information Theory, 201

Enhanced thresholding-based wavelet noise filtering in optical fiber communications

Nowadays, the growing requirement of higher data transmission rates for real-time applications of communication systems. The capacity of data transmission increased with the higher carrier frequency. Optical Fiber Communication (OFC) systems gained a significant interest of researchers due to its capability of enhancing the data-carrying capacity. The optical waves in OFC systems operate in the range of THz those results in the increased capacity of data-carrying. The OFC systems achieved a high data rate, however, suffered from the challenges of various noises. The presence of noises in OFC may degrade the transmitting signal quality & increases the error rates. The OFC systems design by considering the noise in optical communication links recently received great interest from researchers. In this paper, we first presented the OFC design with noises such as white Gaussian noise, shot noise, & thermal noise. Secondly, the impact of noises in OFC analyzed through simulation results by performing optical communications with & without noises. Third, to suppress the noise effects on optical communications, we propose the enhanced thresholding-based wavelet Denoising approach called Wavelet Denoising using Enhanced Thresholding (WDET). The aim of WDET for optical communications is to improve the signal quality & minimize the signal errors effectively in the presence of various noises. The design of WDET is based on the properties of hard & soft thresholding of wavelet Denoising. The simulation results show that the proposed Denoising approach improves the signal quality factor with reduced Bit Error Rate (BER) & Mean Square Error (MSE) compared to existing filtering methods

Turbo Packet Combining for Broadband Space-Time BICM Hybrid-ARQ Systems with Co-Channel Interference

In this paper, efficient turbo packet combining for single carrier (SC) broadband multiple-input--multiple-output (MIMO) hybrid--automatic repeat request (ARQ) transmission with unknown co-channel interference (CCI) is studied. We propose a new frequency domain soft minimum mean square error (MMSE)-based signal level combining technique where received signals and channel frequency responses (CFR)s corresponding to all retransmissions are used to decode the data packet. We provide a recursive implementation algorithm for the introduced scheme, and show that both its computational complexity and memory requirements are quite insensitive to the ARQ delay, i.e., maximum number of ARQ rounds. Furthermore, we analyze the asymptotic performance, and show that under a sum-rank condition on the CCI MIMO ARQ channel, the proposed packet combining scheme is not interference-limited. Simulation results are provided to demonstrate the gains offered by the proposed technique.Comment: 12 pages, 7 figures, and 2 table

Multiple Importance Sampling for Symbol Error Rate Estimation of Maximum-Likelihood Detectors in MIMO Channels

In this paper we propose a multiple importance sampling (MIS) method for the efficient symbol error rate (SER) estimation of maximum likelihood (ML) multiple-input multiple-output (MIMO) detectors. Given a transmitted symbol from the input lattice, obtaining the SER requires the computation of an integral outside its Voronoi region in a high-dimensional space, for which a closed-form solution does not exist. Hence, the SER must be approximated through crude or naive Monte Carlo (MC) simulations. This practice is widely used in the literature despite its inefficiency, particularly severe at high signal-to-noise-ratio (SNR) or for systems with stringent SER requirements. It is well-known that more sophisticated MC-based techniques such as MIS, when carefully designed, can reduce the variance of the estimators in several orders of magnitude with respect to naive Monte Carlo in rare-event estimation, or equivalently, they need significantly less samples for attaining a desired performance. The proposed MIS method provides unbiased SER estimates by sampling from a mixture of components that are carefully chosen and parametrized. The number of components, the parameters of the components, and their weights in the mixture, are automatically chosen by the proposed method. As a result, the proposed method is flexible, easy-to-use, theoretically sound, and presents a high performance in a variety of scenarios. We show in our simulations that SERs lower than 10?8 can be accurately estimated with just 104 random samples.The work of V. Elvira was supported by the Agence Nationale de la Recherche of France under PISCES project (ANR-17-CE40-0031-01). The work of I. Santamaria was supported by Ministerio de Ciencia, Innovación y Universidades and AEI/FEDER funds of the E.U., under grant PID2019-104958RB-C43 (ADELE). A short preliminary version of this paper was presented at the 2019 Asilomar Conference on Signals, Systems, and Computers

Improving Performance for CSMA/CA Based Wireless Networks

Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) based wireless networks are becoming increasingly ubiquitous. With the aim of supporting rich multimedia applications such as high-definition television (HDTV, 20Mbps) and DVD (9.8Mbps), one of the technology trends is towards increasingly higher bandwidth. Some recent IEEE 802.11n proposals seek to provide PHY rates of up to 600 Mbps. In addition to increasing bandwidth, there is also strong interest in extending the coverage of CSMA/CA based wireless networks. One solution is to relay traffic via multiple intermediate stations if the sender and the receiver are far apart. The so called “mesh” networks based on this relay-based approach, if properly designed, may feature both “high speed” and “large coverage” at the same time. This thesis focusses on MAC layer performance enhancements in CSMA/CA based networks in this context. Firstly, we observe that higher PHY rates do not necessarily translate into corresponding increases in MAC layer throughput due to the overhead of the CSMA/CA based MAC/PHY layers. To mitigate the overhead, we propose a novel MAC scheme whereby transported information is partially acknowledged and retransmitted. Theoretical analysis and extensive simulations show that the proposed MAC approach can achieve high efficiency (low MAC overhead) for a wide range of channel variations and realistic traffic types. Secondly, we investigate the close interaction between the MAC layer and the buffer above it to improve performance for real world traffic such as TCP. Surprisingly, the issue of buffer sizing in 802.11 wireless networks has received little attention in the literature yet it poses fundamentally new challenges compared to buffer sizing in wired networks. We propose a new adaptive buffer sizing approach for 802.11e WLANs that maintains a high level of link utilisation, while minimising queueing delay. Thirdly, we highlight that gross unfairness can exist between competing flows in multihop mesh networks even if we assume that orthogonal channels are used in neighbouring hops. That is, even without inter-channel interference and hidden terminals, multi-hop mesh networks which aim to offer a both “high speed” and “large coverage” are not achieved. We propose the use of 802.11e’s TXOP mechanism to restore/enfore fairness. The proposed approach is implementable using off-the-shelf devices and fully decentralised (requires no message passing)