12 research outputs found
Ultra-Reliable and Low Latency Communication in mmWave-Enabled Massive MIMO Networks
Ultra-reliability and low-latency are two key components in 5G networks. In
this letter, we investigate the problem of ultra-reliable and low-latency
communication (URLLC) in millimeter wave (mmWave)-enabled massive
multiple-input multiple-output (MIMO) networks. The problem is cast as a
network utility maximization subject to probabilistic latency and reliability
constraints. To solve this problem, we resort to the Lyapunov technique whereby
a utility-delay control approach is proposed, which adapts to channel
variations and queue dynamics. Numerical results demonstrate that our proposed
approach ensures reliable communication with a guaranteed probability of
99.99%, and reduces latency by 28.41% and 77.11% as compared to baselines with
and without probabilistic latency constraints, respectively.Comment: Accepted May 12, 2017 by IEEE Communications Letters. Topic is
Ultra-Reliable and Low Latency Communication in 5G mmWave Network
Enforcing Statistical Orthogonality in Massive MIMO Systems via Covariance Shaping
This paper tackles the problem of downlink transmission in massive
multiple-input multiple-output(MIMO) systems where the equipments (UEs) exhibit
high spatial correlation and the channel estimation is limited by strong pilot
contamination. Signal subspace separation among the UEs is, in fact, rarely
realized in practice and is generally beyond the control of the network
designer (as it is dictated by the physical scattering environment). In this
context, we propose a novel statistical beamforming technique, referred to
asMIMO covariance shaping, that exploits multiple antennas at the UEs and
leverages the realistic non-Kronecker structure of massive MIMO channels to
target a suitable shaping of the channel statistics performed at the UE-side.
To optimize the covariance shaping strategies, we propose a low-complexity
block coordinate descent algorithm that is proved to converge to a limit point
of the original nonconvex problem. For the two-UE case, this is shown to
converge to a stationary point of the original problem. Numerical results
illustrate the sum-rate performance gains of the proposed method with respect
to reference scenarios employing the multiple antennas at the UE for spatial
multiplexing.Comment: Submitted for journal publicatio
Coordinated Multi-cell Beamforming for Massive MIMO: A Random Matrix Approach
We consider the problem of coordinated multi- cell downlink beamforming in
massive multiple input multiple output (MIMO) systems consisting of N cells, Nt
antennas per base station (BS) and K user terminals (UTs) per cell.
Specifically, we formulate a multi-cell beamforming algorithm for massive MIMO
systems which requires limited amount of information exchange between the BSs.
The design objective is to minimize the aggregate transmit power across all the
BSs subject to satisfying the user signal to interference noise ratio (SINR)
constraints. The algorithm requires the BSs to exchange parameters which can be
computed solely based on the channel statistics rather than the instantaneous
CSI. We make use of tools from random matrix theory to formulate the
decentralized algorithm. We also characterize a lower bound on the set of
target SINR values for which the decentralized multi-cell beamforming algorithm
is feasible. We further show that the performance of our algorithm
asymptotically matches the performance of the centralized algorithm with full
CSI sharing. While the original result focuses on minimizing the aggregate
transmit power across all the BSs, we formulate a heuristic extension of this
algorithm to incorporate a practical constraint in multi-cell systems, namely
the individual BS transmit power constraints. Finally, we investigate the
impact of imperfect CSI and pilot contamination effect on the performance of
the decentralized algorithm, and propose a heuristic extension of the algorithm
to accommodate these issues. Simulation results illustrate that our algorithm
closely satisfies the target SINR constraints and achieves minimum power in the
regime of massive MIMO systems. In addition, it also provides substantial power
savings as compared to zero-forcing beamforming when the number of antennas per
BS is of the same orders of magnitude as the number of UTs per cell