23,796 research outputs found
The Approximate Capacity of the Many-to-One and One-to-Many Gaussian Interference Channels
Recently, Etkin, Tse, and Wang found the capacity region of the two-user
Gaussian interference channel to within one bit/s/Hz. A natural goal is to
apply this approach to the Gaussian interference channel with an arbitrary
number of users. We make progress towards this goal by finding the capacity
region of the many-to-one and one-to-many Gaussian interference channels to
within a constant number of bits. The result makes use of a deterministic model
to provide insight into the Gaussian channel. The deterministic model makes
explicit the dimension of signal scale. A central theme emerges: the use of
lattice codes for alignment of interfering signals on the signal scale.Comment: 45 pages, 16 figures. Submitted to IEEE Transactions on Information
Theor
On Interference Alignment and the Deterministic Capacity for Cellular Channels with Weak Symmetric Cross Links
In this paper, we study the uplink of a cellular system using the linear
deterministic approximation model, where there are two users transmitting to a
receiver, mutually interfering with a third transmitter communicating with a
second receiver. We give an achievable coding scheme and prove its optimality,
i.e. characterize the capacity region. This scheme is a form of interference
alignment which exploits the channel gain difference of the two-user cell.Comment: Submitted to IEEE International Symposium on Information Theory
(ISIT) 2011, 5 page
Enabling the Multi-User Generalized Degrees of Freedom in the Gaussian Cellular Channel
There has been major progress over the last decade in understanding the
classical interference channel (IC). Recent key results show that constant bit
gap capacity results can be obtained from linear deterministic models (LDMs).
However, it is widely unrecognized that the time-invariant, frequency-flat
cellular channel, which contains the IC as a special case, possesses some
additional generalized degrees of freedom (GDoF) due to multi-user operation.
This was proved for the LDM cellular channel very recently but is an open
question for the corresponding Gaussian counterpart. In this paper, we close
this gap and provide an achievable sum-rate for the Gaussian cellular channel
which is within a constant bit gap of the LDM sum capacity. We show that the
additional GDoFs from the LDM cellular channel carry over. This is enabled by
signal scale alignment. In particular, the multi-user gain reduces the
interference by half in the 2-user per cell case compared to the IC.Comment: 5 pages, to appear in IEEE ITW 2014, Hobart, Australi
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
Upper Bounds and Duality Relations of the Linear Deterministic Sum Capacity for Cellular Systems
The MAC-BC duality of information theory and wireless communications is an
intriguing concept for efficient algorithm design. However, no concept is known
so far for the important cellular channel. To make progress on this front, we
consider in this paper the linear deterministic cellular channel. In
particular, we prove duality of a network with two interfering MACs in each
cell and a network with two interfering BCs in each cell. The operational
region is confined to the weak interference regime. First, achievable schemes
as well as upper bounds will be provided. These bounds are the same for both
channels. We will show, that for specific cases the upper bound corresponds to
the achievable scheme and hence establishing a duality relationship between
them.Comment: 6 pages, to appear in IEEE ICC 2014, Sydney, Australi
Computation Alignment: Capacity Approximation without Noise Accumulation
Consider several source nodes communicating across a wireless network to a
destination node with the help of several layers of relay nodes. Recent work by
Avestimehr et al. has approximated the capacity of this network up to an
additive gap. The communication scheme achieving this capacity approximation is
based on compress-and-forward, resulting in noise accumulation as the messages
traverse the network. As a consequence, the approximation gap increases
linearly with the network depth.
This paper develops a computation alignment strategy that can approach the
capacity of a class of layered, time-varying wireless relay networks up to an
approximation gap that is independent of the network depth. This strategy is
based on the compute-and-forward framework, which enables relays to decode
deterministic functions of the transmitted messages. Alone, compute-and-forward
is insufficient to approach the capacity as it incurs a penalty for
approximating the wireless channel with complex-valued coefficients by a
channel with integer coefficients. Here, this penalty is circumvented by
carefully matching channel realizations across time slots to create
integer-valued effective channels that are well-suited to compute-and-forward.
Unlike prior constant gap results, the approximation gap obtained in this paper
also depends closely on the fading statistics, which are assumed to be i.i.d.
Rayleigh.Comment: 36 pages, to appear in IEEE Transactions on Information Theor
Lattice Codes for Many-to-One Interference Channels With and Without Cognitive Messages
A new achievable rate region is given for the Gaussian cognitive many-to-one
interference channel. The proposed novel coding scheme is based on the
compute-and-forward approach with lattice codes. Using the idea of decoding
sums of codewords, our scheme improves considerably upon the conventional
coding schemes which treat interference as noise or decode messages
simultaneously. Our strategy also extends directly to the usual many-to-one
interference channels without cognitive messages. Comparing to the usual
compute-and-forward scheme where a fixed lattice is used for the code
construction, the novel scheme employs scaled lattices and also encompasses key
ingredients of the existing schemes for the cognitive interference channel.
With this new component, our scheme achieves a larger rate region in general.
For some symmetric channel settings, new constant gap or capacity results are
established, which are independent of the number of users in the system.Comment: To appear in IEEE Transactions on Information Theor
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