2,648 research outputs found
Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks
In this paper, we develop various beamforming techniques for downlink
transmission for multiple-input single-output (MISO) non-orthogonal multiple
access (NOMA) systems. First, a beamforming approach with perfect channel state
information (CSI) is investigated to provide the required quality of service
(QoS) for all users. Taylor series approximation and semidefinite relaxation
(SDR) techniques are employed to reformulate the original non-convex power
minimization problem to a tractable one. Further, a fairness-based beamforming
approach is proposed through a max-min formulation to maintain fairness between
users. Next, we consider a robust scheme by incorporating channel
uncertainties, where the transmit power is minimized while satisfying the
outage probability requirement at each user. Through exploiting the SDR
approach, the original non-convex problem is reformulated in a linear matrix
inequality (LMI) form to obtain the optimal solution. Numerical results
demonstrate that the robust scheme can achieve better performance compared to
the non-robust scheme in terms of the rate satisfaction ratio. Further,
simulation results confirm that NOMA consumes a little over half transmit power
needed by OMA for the same data rate requirements. Hence, NOMA has the
potential to significantly improve the system performance in terms of transmit
power consumption in future 5G networks and beyond.Comment: accepted to publish in IEEE Transactions on Vehicular Technolog
Antenna Combining for the MIMO Downlink Channel
A multiple antenna downlink channel where limited channel feedback is
available to the transmitter is considered. In a vector downlink channel
(single antenna at each receiver), the transmit antenna array can be used to
transmit separate data streams to multiple receivers only if the transmitter
has very accurate channel knowledge, i.e., if there is high-rate channel
feedback from each receiver. In this work it is shown that channel feedback
requirements can be significantly reduced if each receiver has a small number
of antennas and appropriately combines its antenna outputs. A combining method
that minimizes channel quantization error at each receiver, and thereby
minimizes multi-user interference, is proposed and analyzed. This technique is
shown to outperform traditional techniques such as maximum-ratio combining
because minimization of interference power is more critical than maximization
of signal power in the multiple antenna downlink. Analysis is provided to
quantify the feedback savings, and the technique is seen to work well with user
selection and is also robust to receiver estimation error.Comment: Submitted to IEEE Trans. Wireless Communications April 2007. Revised
August 200
Symbol-Level Multiuser MISO Precoding for Multi-level Adaptive Modulation
Symbol-level precoding is a new paradigm for multiuser downlink systems which
aims at creating constructive interference among the transmitted data streams.
This can be enabled by designing the precoded signal of the multiantenna
transmitter on a symbol level, taking into account both channel state
information and data symbols. Previous literature has studied this paradigm for
MPSK modulations by addressing various performance metrics, such as power
minimization and maximization of the minimum rate. In this paper, we extend
this to generic multi-level modulations i.e. MQAM and APSK by establishing
connection to PHY layer multicasting with phase constraints. Furthermore, we
address adaptive modulation schemes which are crucial in enabling the
throughput scaling of symbol-level precoded systems. In this direction, we
design signal processing algorithms for minimizing the required power under
per-user SINR or goodput constraints. Extensive numerical results show that the
proposed algorithm provides considerable power and energy efficiency gains,
while adapting the employed modulation scheme to match the requested data rate
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