17,258 research outputs found
Near-capacity dirty-paper code design : a source-channel coding approach
This paper examines near-capacity dirty-paper code designs based on source-channel coding. We first point out that the performance loss in signal-to-noise ratio (SNR) in our code designs can be broken into the sum of the packing loss from channel coding and a modulo loss, which is a function of the granular loss from source coding and the target dirty-paper coding rate (or SNR). We then examine practical designs by combining trellis-coded quantization (TCQ) with both systematic and nonsystematic irregular repeat-accumulate (IRA) codes. Like previous approaches, we exploit the extrinsic information transfer (EXIT) chart technique for capacity-approaching IRA code design; but unlike previous approaches, we emphasize the role of strong source coding to achieve as much granular gain as possible using TCQ. Instead of systematic doping, we employ two relatively shifted TCQ codebooks, where the shift is optimized (via tuning the EXIT charts) to facilitate the IRA code design. Our designs synergistically combine TCQ with IRA codes so that they work together as well as they do individually. By bringing together TCQ (the best quantizer from the source coding community) and EXIT chart-based IRA code designs (the best from the channel coding community), we are able to approach the theoretical limit of dirty-paper coding. For example, at 0.25 bit per symbol (b/s), our best code design (with 2048-state TCQ) performs only 0.630 dB away from the Shannon capacity
Code designs for MIMO broadcast channels
Recent information-theoretic results show the optimality of dirty-paper coding (DPC) in achieving the full capacity region of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). This paper presents a DPC based code design for BCs. We consider the case in which there is an individual rate/signal-to-interference-plus-noise ratio (SINR) constraint for each user. For a fixed transmitter power, we choose the linear transmit precoding matrix such that the SINRs at users are uniformly maximized, thus ensuring the best bit-error rate performance. We start with Cover's simplest two-user Gaussian BC and present a coding scheme that operates 1.44 dB from the boundary of the capacity region at the rate of one bit per real sample (b/s) for each user. We then extend the coding strategy to a two-user MIMO Gaussian BC with two transmit antennas at the base-station and develop the first limit-approaching code design using nested turbo codes for DPC. At the rate of 1 b/s for each user, our design operates 1.48 dB from the capacity region boundary. We also consider the performance of our scheme over a slow fading BC. For two transmit antennas, simulation results indicate a performance loss of only 1.4 dB, 1.64 dB and 1.99 dB from the theoretical limit in terms of the total transmission power for the two, three and four user case, respectively
Nested turbo codes for the costa problem
Driven by applications in data-hiding, MIMO broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source- channel coding for algebraic binning. We then address practical code design based on nested lattice codes and propose nested turbo codes using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to TCQ, turbo-like TCQ offers structural similarity between the source and channel coding components, leading to more efficient nesting with TTCM and better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. Optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions and exhibits a gap of 0.94, 1.42 and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 bits/sample, respectively
Fundamental Limits in MIMO Broadcast Channels
This paper studies the fundamental limits of MIMO broadcast channels from a high level, determining the sum-rate capacity of the system as a function of system paramaters, such as the number of transmit antennas, the number of users, the number of receive antennas, and the total transmit power. The crucial role of channel state information at the transmitter is emphasized, as well as the emergence of opportunistic transmission schemes. The effects of channel estimation errors, training, and spatial correlation are studied, as well as issues related to fairness, delay and differentiated rate scheduling
Gaussian Multiple Access via Compute-and-Forward
Lattice codes used under the Compute-and-Forward paradigm suggest an
alternative strategy for the standard Gaussian multiple-access channel (MAC):
The receiver successively decodes integer linear combinations of the messages
until it can invert and recover all messages. In this paper, a multiple-access
technique called CFMA (Compute-Forward Multiple Access) is proposed and
analyzed. For the two-user MAC, it is shown that without time-sharing, the
entire capacity region can be attained using CFMA with a single-user decoder as
soon as the signal-to-noise ratios are above . A partial analysis
is given for more than two users. Lastly the strategy is extended to the
so-called dirty MAC where two interfering signals are known non-causally to the
two transmitters in a distributed fashion. Our scheme extends the previously
known results and gives new achievable rate regions.Comment: to appear in IEEE Transactions on Information Theor
Filter and nested-lattice code design for fading MIMO channels with side-information
Linear-assignment Gel'fand-Pinsker coding (LA-GPC) is a coding technique for
channels with interference known only at the transmitter, where the known
interference is treated as side-information (SI). As a special case of LA-GPC,
dirty paper coding has been shown to be able to achieve the optimal
interference-free rate for interference channels with perfect channel state
information at the transmitter (CSIT). In the cases where only the channel
distribution information at the transmitter (CDIT) is available, LA-GPC also
has good (sometimes optimal) performance in a variety of fast and slow fading
SI channels. In this paper, we design the filters in nested-lattice based
coding to make it achieve the same rate performance as LA-GPC in multiple-input
multiple-output (MIMO) channels. Compared with the random Gaussian codebooks
used in previous works, our resultant coding schemes have an algebraic
structure and can be implemented in practical systems. A simulation in a
slow-fading channel is also provided, and near interference-free error
performance is obtained. The proposed coding schemes can serve as the
fundamental building blocks to achieve the promised rate performance of MIMO
Gaussian broadcast channels with CDIT or perfect CSITComment: submitted to IEEE Transactions on Communications, Feb, 200
State Amplification
We consider the problem of transmitting data at rate R over a state dependent
channel p(y|x,s) with the state information available at the sender and at the
same time conveying the information about the channel state itself to the
receiver. The amount of state information that can be learned at the receiver
is captured by the mutual information I(S^n; Y^n) between the state sequence
S^n and the channel output Y^n. The optimal tradeoff is characterized between
the information transmission rate R and the state uncertainty reduction rate
\Delta, when the state information is either causally or noncausally available
at the sender. This result is closely related and in a sense dual to a recent
study by Merhav and Shamai, which solves the problem of masking the state
information from the receiver rather than conveying it.Comment: 9 pages, 4 figures, submitted to IEEE Trans. Inform. Theory, revise
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