12,281 research outputs found
On the Capacity of Vector Gaussian Channels With Bounded Inputs
The capacity of a deterministic multiple-input multiple-output channel under the peak and average power constraints is investigated. For the identity channel matrix, the approach of Shamai et al. is generalized to the higher dimension settings to derive the necessary and sufficient conditions for the optimal input probability density function. This approach prevents the usage of the identity theorem of the holomorphic functions of several complex variables which seems to fail in the multi-dimensional scenarios. It is proved that the support of the capacity-achieving distribution is a finite set of hyper-spheres with mutual independent phases and amplitude in the spherical domain. Subsequently, it is shown that when the average power constraint is relaxed, if the number of antennas is large enough, the capacity has a closed-form solution and constant amplitude signaling at the peak power achieves it. Moreover, it will be observed that in a discrete-time memoryless Gaussian channel, the average power constrained capacity, which results from a Gaussian input distribution, can be closely obtained by an input where the support of its magnitude is a discrete finite set. Finally, we investigate some upper and lower bounds for the capacity of the non-identity channel matrix and evaluate their performance as a function of the condition number of the channel
A digital interface for Gaussian relay and interference networks: Lifting codes from the discrete superposition model
For every Gaussian network, there exists a corresponding deterministic
network called the discrete superposition network. We show that this discrete
superposition network provides a near-optimal digital interface for operating a
class consisting of many Gaussian networks in the sense that any code for the
discrete superposition network can be naturally lifted to a corresponding code
for the Gaussian network, while achieving a rate that is no more than a
constant number of bits lesser than the rate it achieves for the discrete
superposition network. This constant depends only on the number of nodes in the
network and not on the channel gains or SNR. Moreover the capacities of the two
networks are within a constant of each other, again independent of channel
gains and SNR. We show that the class of Gaussian networks for which this
interface property holds includes relay networks with a single
source-destination pair, interference networks, multicast networks, and the
counterparts of these networks with multiple transmit and receive antennas.
The code for the Gaussian relay network can be obtained from any code for the
discrete superposition network simply by pruning it. This lifting scheme
establishes that the superposition model can indeed potentially serve as a
strong surrogate for designing codes for Gaussian relay networks.
We present similar results for the K x K Gaussian interference network, MIMO
Gaussian interference networks, MIMO Gaussian relay networks, and multicast
networks, with the constant gap depending additionally on the number of
antennas in case of MIMO networks.Comment: Final versio
On the Gaussian Many-to-One X Channel
In this paper, the Gaussian many-to-one X channel, which is a special case of
general multiuser X channel, is studied. In the Gaussian many-to-one X channel,
communication links exist between all transmitters and one of the receivers,
along with a communication link between each transmitter and its corresponding
receiver. As per the X channel assumption, transmission of messages is allowed
on all the links of the channel. This communication model is different from the
corresponding many-to-one interference channel (IC). Transmission strategies
which involve using Gaussian codebooks and treating interference from a subset
of transmitters as noise are formulated for the above channel. Sum-rate is used
as the criterion of optimality for evaluating the strategies. Initially, a many-to-one X channel is considered and three transmission strategies
are analyzed. The first two strategies are shown to achieve sum-rate capacity
under certain channel conditions. For the third strategy, a sum-rate outer
bound is derived and the gap between the outer bound and the achieved rate is
characterized. These results are later extended to the case. Next,
a region in which the many-to-one X channel can be operated as a many-to-one IC
without loss of sum-rate is identified. Further, in the above region, it is
shown that using Gaussian codebooks and treating interference as noise achieves
a rate point that is within bits from the sum-rate capacity.
Subsequently, some implications of the above results to the Gaussian
many-to-one IC are discussed. Transmission strategies for the many-to-one IC
are formulated and channel conditions under which the strategies achieve
sum-rate capacity are obtained. A region where the sum-rate capacity can be
characterized to within bits is also identified.Comment: Submitted to IEEE Transactions on Information Theory; Revised and
updated version of the original draf
Gaussian Multiple and Random Access in the Finite Blocklength Regime
This paper presents finite-blocklength achievabil- ity bounds for the Gaussian multiple access channel (MAC) and random access channel (RAC) under average-error and maximal-power constraints. Using random codewords uniformly distributed on a sphere and a maximum likelihood decoder, the derived MAC bound on each transmitter’s rate matches the MolavianJazi-Laneman bound (2015) in its first- and second-order terms, improving the remaining terms to ½ log n/n + O(1/n) bits per channel use. The result then extends to a RAC model in which neither the encoders nor the decoder knows which of K possible transmitters are active. In the proposed rateless coding strategy, decoding occurs at a time n t that depends on the decoder’s estimate t of the number of active transmitters k. Single-bit feedback from the decoder to all encoders at each potential decoding time n_i, i ≤ t, informs the encoders when to stop transmitting. For this RAC model, the proposed code achieves the same first-, second-, and third-order performance as the best known result for the Gaussian MAC in operation
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