11 research outputs found
Sum Rates, Rate Allocation, and User Scheduling for Multi-User MIMO Vector Perturbation Precoding
This paper considers the multiuser multiple-input multiple-output (MIMO)
broadcast channel. We consider the case where the multiple transmit antennas
are used to deliver independent data streams to multiple users via vector
perturbation. We derive expressions for the sum rate in terms of the average
energy of the precoded vector, and use this to derive a high signal-to-noise
ratio (SNR) closed-form upper bound, which we show to be tight via simulation.
We also propose a modification to vector perturbation where different rates can
be allocated to different users. We conclude that for vector perturbation
precoding most of the sum rate gains can be achieved by reducing the rate
allocation problem to the user selection problem. We then propose a
low-complexity user selection algorithm that attempts to maximize the high-SNR
sum rate upper bound. Simulations show that the algorithm outperforms other
user selection algorithms of similar complexity.Comment: 27 pages with 6 figures and 2 tables. Accepted for publication in
IEEE Trans. Wireless Comm
A New Approach to Linear Estimation Problem in Multi-user Massive MIMO Systems
A novel approach for solving linear estimation problem in multi-user massive
MIMO systems is proposed. In this approach, the difficulty of matrix inversion
is attributed to the incomplete definition of the dot product. The general
definition of dot product implies that the columns of channel matrix are always
orthogonal whereas, in practice, they may be not. If the latter information can
be incorporated into dot product, then the unknowns can be directly computed
from projections without inverting the channel matrix. By doing so, the
proposed method is able to achieve an exact solution with a 25% reduction in
computational complexity as compared to the QR method. Proposed method is
stable, offers an extra flexibility of computing any single unknown, and can be
implemented in just twelve lines of code
Linear-time nearest point algorithms for Coxeter lattices
The Coxeter lattices, which we denote , are a family of lattices
containing many of the important lattices in low dimensions. This includes
, , and their duals , and . We consider
the problem of finding a nearest point in a Coxeter lattice. We describe two
new algorithms, one with worst case arithmetic complexity and the
other with worst case complexity O(n) where is the dimension of the
lattice. We show that for the particular lattices and the
algorithms reduce to simple nearest point algorithms that already exist in the
literature.Comment: submitted to IEEE Transactions on Information Theor
Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
This paper surveys recent advances in the area of very large MIMO systems.
With very large MIMO, we think of systems that use antenna arrays with an
order of magnitude more elements than in systems being built today, say a
hundred antennas or more. Very large MIMO entails an unprecedented number of
antennas simultaneously serving a much smaller number of terminals. The
disparity in number emerges as a desirable operating condition and a practical
one as well. The number of terminals that can be simultaneously served is
limited, not by the number of antennas, but rather by our inability to acquire
channel-state information for an unlimited number of terminals. Larger numbers
of terminals can always be accommodated by combining very large MIMO technology
with conventional time- and frequency-division multiplexing via OFDM. Very
large MIMO arrays is a new research field both in communication theory,
propagation, and electronics and represents a paradigm shift in the way of
thinking both with regards to theory, systems and implementation. The ultimate
vision of very large MIMO systems is that the antenna array would consist of
small active antenna units, plugged into an (optical) fieldbus.Comment: Accepted for publication in the IEEE Signal Processing Magazine,
October 201
Quantum-aided multi-user transmission in non-orthogonal multiple access systems
With the research on implementing a universal quantum computer being under the technological spotlight, new possibilities appear for their employment in wireless communications systems for reducing their complexity and improving their performance. In this treatise, we consider the downlink of a rank-deficient, multi-user system and we propose the discrete-valued and continuous-valued Quantum-assisted Particle Swarm Optimization (QPSO) algorithms for performing Vector Perturbation (VP) precoding, as well as for lowering the required transmission power at the Base Station (BS), while minimizing the expected average Bit Error Ratio (BER) at the mobile terminals. We use the Minimum BER (MBER) criterion. We show that the novel quantum-assisted precoding methodology results in an enhanced BER performance, when compared to that of a classical methodology employing the PSO algorithm, while requiring the same computational complexity in the challenging rank-deficient scenarios, where the number of transmit antenna elements at the BS is lower than the number of users. Moreover, when there is limited Channel State Information (CSI) feedback from the users to the BS, due to the necessary quantization of the channel states, the proposed quantum-assisted precoder outperforms the classical precoder