No Arabic abstract
The rising interest in applications requiring the transmission of small amounts of data has recently lead to the development of accurate performance bounds and of powerful channel codes for the transmission of short-data packets over the AWGN channel. Much less is known about the interaction between error control coding and channel estimation at short blocks when transmitting over channels with states (e.g., fading channels, phase-noise channels, etc...) for the setup where no a priori channel state information (CSI) is available at the transmitter and the receiver. In this paper, we use the mismatched-decoding framework to characterize the fundamental tradeoff occurring in the transmission of short data packet over an AWGN channel with unknown gain that stays constant over the packet. Our analysis for this simplified setup aims at showing the potential of mismatched decoding as a tool to design and analyze transmission strategies for short blocks. We focus on a pragmatic approach where the transmission frame contains a codeword as well as a preamble that is used to estimate the channel (the codeword symbols are not used for channel estimation). Achievability and converse bounds on the block error probability achievable by this approach are provided and compared with simulation results for schemes employing short low-density parity-check codes. Our bounds turn out to predict accurately the optimal trade-off between the preamble length and the redundancy introduced by the channel code.
This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel. Due to the bottleneck constraint, it is impossible for the oblivious relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node can get the perfect CSI at no cost and two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.
Channel matrix sparsification is considered as a promising approach to reduce the progressing complexity in large-scale cloud-radio access networks (C-RANs) based on ideal channel condition assumption. In this paper, the research of channel sparsification is extend to practical scenarios, in which the perfect channel state information (CSI) is not available. First, a tractable lower bound of signal-to-interferenceplus-noise ratio (SINR) fidelity, which is defined as a ratio of SINRs with and without channel sparsification, is derived to evaluate the impact of channel estimation error. Based on the theoretical results, a Dinkelbach-based algorithm is proposed to achieve the global optimal performance of channel matrix sparsification based on the criterion of distance. Finally, all these results are extended to a more challenging scenario with pilot contamination. Finally, simulation results are shown to evaluate the performance of channel matrix sparsification with imperfect CSIs and verify our analytical results.
A network consisting of $n$ source-destination pairs and $m$ relays is considered. Focusing on the large system limit (large $n$), the throughput scaling laws of two-hop relaying protocols are studied for Rayleigh fading channels. It is shown that, under the practical constraints of single-user encoding-decoding scheme, and partial channel state information (CSI) at the transmitters (via integer-value feedback from the receivers), the maximal throughput scales as $log n$ even if full relay cooperation is allowed. Furthermore, a novel decentralized opportunistic relaying scheme with receiver CSI, partial transmitter CSI, and no relay cooperation, is shown to achieve the optimal throughput scaling law of $log n$.
The problem of channel coding over the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC) with additive independent Gaussian states is considered. The states are known in a noncausal manner to the encoder, and it wishes to minimize the amount of information that the receivers can learn from the channel outputs about the state sequence. The state leakage rate is measured as a normalized blockwise mutual information between the state sequence and the channel outputs sequences. We employ a new version of a state-dependent extremal inequality and show that Gaussian input maximizes the state-dependent version of Martons outer bound. Further we show that our inner bound coincides with the outer bound. Our result generalizes previously studied scalar Gaussian BC with state and MIMO BC without state.
We consider the two-receiver memoryless broadcast channel with states where each receiver requests both common and private messages, and may know part of the private message requested by the other receiver as receiver message side information (RMSI). We address two categories of the channel (i) channel with states known causally to the transmitter, and (ii) channel with states known non-causally to the transmitter. Starting with the channel without RMSI, we first propose a transmission scheme and derive an inner bound for the causal category. We then unify our inner bound for the causal category and the best-known inner bound for the non-causal category, although their transmission schemes are different. Moving on to the channel with RMSI, we first apply a pre-coding to the transmission schemes of the causal and non-causal categories without RMSI. We then derive a unified inner bound as a result of having a unified inner bound when there is no RMSI, and applying the same pre-coding to both categories. We show that our inner bound is tight for some new cases as well as the cases whose capacity region was known previously.