706 research outputs found
Two-User Gaussian Interference Channel with Finite Constellation Input and FDMA
In the two-user Gaussian Strong Interference Channel (GSIC) with finite
constellation inputs, it is known that relative rotation between the
constellations of the two users enlarges the Constellation Constrained (CC)
capacity region. In this paper, a metric for finding the approximate angle of
rotation (with negligibly small error) to maximally enlarge the CC capacity for
the two-user GSIC is presented. In the case of Gaussian input alphabets with
equal powers for both the users and the modulus of both the cross-channel gains
being equal to unity, it is known that the FDMA rate curve touches the capacity
curve of the GSIC. It is shown that, with unequal powers for both the users
also, when the modulus of one of the cross-channel gains being equal to one and
the modulus of the other cross-channel gain being greater than or equal to one,
the FDMA rate curve touches the capacity curve of the GSIC. On the contrary, it
is shown that, under finite constellation inputs, with both the users using the
same constellation, the FDMA rate curve strictly lies within (never touches)
the enlarged CC capacity region throughout the strong-interference regime. This
means that using FDMA it is impossible to go close to the CC capacity. It is
well known that for the Gaussian input alphabets, the FDMA inner-bound, at the
optimum sum-rate point, is always better than the simultaneous-decoding
inner-bound throughout the weak-interference regime. For a portion of the weak
interference regime, it is shown that with identical finite constellation
inputs for both the users, the simultaneous-decoding inner-bound, enlarged by
relative rotation between the constellations, is strictly better than the FDMA
inner-bound.Comment: 12 pages, 10 figure
Uplink Non-Orthogonal Multiple Access with Finite-Alphabet Inputs
This paper focuses on the non-orthogonal multiple access (NOMA) design for a
classical two-user multiple access channel (MAC) with finite-alphabet inputs.
We consider practical quadrature amplitude modulation (QAM) constellations at
both transmitters, the sizes of which are assumed to be not necessarily
identical. We propose to maximize the minimum Euclidean distance of the
received sum-constellation with a maximum likelihood (ML) detector by adjusting
the scaling factors (i.e., instantaneous transmitted powers and phases) of both
users. The formulated problem is a mixed continuous-discrete optimization
problem, which is nontrivial to resolve in general. By carefully observing the
structure of the objective function, we discover that Farey sequence can be
applied to tackle the formulated problem. However, the existing Farey sequence
is not applicable when the constellation sizes of the two users are not the
same. Motivated by this, we define a new type of Farey sequence, termed punched
Farey sequence. Based on this, we manage to achieve a closed-form optimal
solution to the original problem by first dividing the entire feasible region
into a finite number of Farey intervals and then taking the maximum over all
the possible intervals. The resulting sum-constellation is proved to be a
regular QAM constellation of a larger size. Moreover, the superiority of NOMA
over time-division multiple access (TDMA) in terms of minimum Euclidean
distance is rigorously proved. Furthermore, the optimal rate allocation among
the two users is obtained in closed-form to further maximize the obtained
minimum Euclidean distance of the received signal subject to a total rate
constraint. Finally, simulation results are provided to verify our theoretical
analysis and demonstrate the merits of the proposed NOMA over existing
orthogonal and non-orthogonal designs.Comment: Submitted for possible journal publicatio
A Novel Power Allocation Scheme for Two-User GMAC with Finite Input Constellations
Constellation Constrained (CC) capacity regions of two-user Gaussian Multiple
Access Channels (GMAC) have been recently reported, wherein an appropriate
angle of rotation between the constellations of the two users is shown to
enlarge the CC capacity region. We refer to such a scheme as the Constellation
Rotation (CR) scheme. In this paper, we propose a novel scheme called the
Constellation Power Allocation (CPA) scheme, wherein the instantaneous transmit
power of the two users are varied by maintaining their average power
constraints. We show that the CPA scheme offers CC sum capacities equal (at low
SNR values) or close (at high SNR values) to those offered by the CR scheme
with reduced decoding complexity for QAM constellations. We study the
robustness of the CPA scheme for random phase offsets in the channel and
unequal average power constraints for the two users. With random phase offsets
in the channel, we show that the CC sum capacity offered by the CPA scheme is
more than the CR scheme at high SNR values. With unequal average power
constraints, we show that the CPA scheme provides maximum gain when the power
levels are close, and the advantage diminishes with the increase in the power
difference.Comment: To appear in IEEE Transactions on Wireless Communications, 10 pages
and 7 figure
Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations
Large-scale (or massive) multiple-input multiple-output (MIMO) is expected to
be one of the key technologies in next-generation multi-user cellular systems,
based on the upcoming 3GPP LTE Release 12 standard, for example. In this work,
we propose - to the best of our knowledge - the first VLSI design enabling
high-throughput data detection in single-carrier frequency-division multiple
access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate
matrix inversion algorithm relying on a Neumann series expansion, which
substantially reduces the complexity of linear data detection. We analyze the
associated error, and we compare its performance and complexity to those of an
exact linear detector. We present corresponding VLSI architectures, which
perform exact and approximate soft-output detection for large-scale MIMO
systems with various antenna/user configurations. Reference implementation
results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to
achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale
MIMO system. We finally provide a performance/complexity trade-off comparison
using the presented FPGA designs, which reveals that the detector circuit of
choice is determined by the ratio between BS antennas and users, as well as the
desired error-rate performance.Comment: To appear in the IEEE Journal of Selected Topics in Signal Processin
On Precoding for Constant K-User MIMO Gaussian Interference Channel with Finite Constellation Inputs
This paper considers linear precoding for constant channel-coefficient
-User MIMO Gaussian Interference Channel (MIMO GIC) where each
transmitter- (Tx-), requires to send independent complex symbols
per channel use that take values from fixed finite constellations with uniform
distribution, to receiver- (Rx-) for . We define the
maximum rate achieved by Tx- using any linear precoder, when the
interference channel-coefficients are zero, as the signal to noise ratio (SNR)
tends to infinity to be the Constellation Constrained Saturation Capacity
(CCSC) for Tx-. We derive a high SNR approximation for the rate achieved by
Tx- when interference is treated as noise and this rate is given by the
mutual information between Tx- and Rx-, denoted as . A set of
necessary and sufficient conditions on the precoders under which
tends to CCSC for Tx- is derived. Interestingly, the precoders designed for
interference alignment (IA) satisfy these necessary and sufficient conditions.
Further, we propose gradient-ascent based algorithms to optimize the sum-rate
achieved by precoding with finite constellation inputs and treating
interference as noise. Simulation study using the proposed algorithms for a
3-user MIMO GIC with two antennas at each node with for all , and
with BPSK and QPSK inputs, show more than 0.1 bits/sec/Hz gain in the ergodic
sum-rate over that yielded by precoders obtained from some known IA algorithms,
at moderate SNRs.Comment: 15 pages, 9 figure
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