13 research outputs found
An Efficient Signaling for Multi-mode Transmission in Multi-user MIMO
In this paper the downlink of a multi-user MIMO (MUMIMO)
system with multi-mode transmission is considered.
We propose a low-complexity algorithm for selecting users
and the corresponding number of data streams to each user,
denoted as user transmission mode (UTM). The selection
is only based on the average received signal-to-noise ratio
(SNR) from the base station (BS) for each user. This reduces
the overall amount of feedback for scheduling, as opposed
to techniques that assume perfect instantaneous channel
state information (CSI) from all users. Analytical average
throughput approximations are derived for each user at different UTMs. Simulation results demonstrate that the proposed algorithm provides performance close to dirty paper coding (DPC) with considerably reduced feedback
Improving Energy Efficiency Through Multimode Transmission in the Downlink MIMO Systems
Adaptively adjusting system parameters including bandwidth, transmit power
and mode to maximize the "Bits per-Joule" energy efficiency (BPJ-EE) in the
downlink MIMO systems with imperfect channel state information at the
transmitter (CSIT) is considered in this paper. By mode we refer to choice of
transmission schemes i.e. singular value decomposition (SVD) or block
diagonalization (BD), active transmit/receive antenna number and active user
number. We derive optimal bandwidth and transmit power for each dedicated mode
at first. During the derivation, accurate capacity estimation strategies are
proposed to cope with the imperfect CSIT caused capacity prediction problem.
Then, an ergodic capacity based mode switching strategy is proposed to further
improve the BPJ-EE, which provides insights on the preferred mode under given
scenarios. Mode switching compromises different power parts, exploits the
tradeoff between the multiplexing gain and the imperfect CSIT caused inter-user
interference, improves the BPJ-EE significantly.Comment: 19 pages, 10 figures, EURASIP Journal on Wireless Communications and
Networking; EURASIP Journal on Wireless Communications and Networking (2011)
2011:20
Achieving "Massive MIMO" Spectral Efficiency with a Not-so-Large Number of Antennas
The main focus and contribution of this paper is a novel network-MIMO TDD
architecture that achieves spectral efficiencies comparable with "Massive
MIMO", with one order of magnitude fewer antennas per active user per cell. The
proposed architecture is based on a family of network-MIMO schemes defined by
small clusters of cooperating base stations, zero-forcing multiuser MIMO
precoding with suitable inter-cluster interference constraints, uplink pilot
signals reuse across cells, and frequency reuse. The key idea consists of
partitioning the users population into geographically determined "bins", such
that all users in the same bin are statistically equivalent, and use the
optimal network-MIMO architecture in the family for each bin. A scheduler takes
care of serving the different bins on the time-frequency slots, in order to
maximize a desired network utility function that captures some desired notion
of fairness. This results in a mixed-mode network-MIMO architecture, where
different schemes, each of which is optimized for the served user bin, are
multiplexed in time-frequency. In order to carry out the performance analysis
and the optimization of the proposed architecture in a clean and
computationally efficient way, we consider the large-system regime where the
number of users, the number of antennas, and the channel coherence block length
go to infinity with fixed ratios. The performance predicted by the large-system
asymptotic analysis matches very well the finite-dimensional simulations.
Overall, the system spectral efficiency obtained by the proposed architecture
is similar to that achieved by "Massive MIMO", with a 10-fold reduction in the
number of antennas at the base stations (roughly, from 500 to 50 antennas).Comment: Full version with appendice (proofs of theorems). A shortened version
without appendice was submitted to IEEE Trans. on Wireless Commun. Appendix B
was revised after submissio
Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users
In downlink multi-antenna systems with many users, the multiplexing gain is
strictly limited by the number of transmit antennas and the use of these
antennas. Assuming that the total number of receive antennas at the
multi-antenna users is much larger than , the maximal multiplexing gain can
be achieved with many different transmission/reception strategies. For example,
the excess number of receive antennas can be utilized to schedule users with
effective channels that are near-orthogonal, for multi-stream multiplexing to
users with well-conditioned channels, and/or to enable interference-aware
receive combining. In this paper, we try to answer the question if the data
streams should be divided among few users (many streams per user) or many users
(few streams per user, enabling receive combining). Analytic results are
derived to show how user selection, spatial correlation, heterogeneous user
conditions, and imperfect channel acquisition (quantization or estimation
errors) affect the performance when sending the maximal number of streams or
one stream per scheduled user---the two extremes in data stream allocation.
While contradicting observations on this topic have been reported in prior
works, we show that selecting many users and allocating one stream per user
(i.e., exploiting receive combining) is the best candidate under realistic
conditions. This is explained by the provably stronger resilience towards
spatial correlation and the larger benefit from multi-user diversity. This
fundamental result has positive implications for the design of downlink systems
as it reduces the hardware requirements at the user devices and simplifies the
throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/one-or-multiple-stream