913 research outputs found
Low Complexity Blind Equalization for OFDM Systems with General Constellations
This paper proposes a low-complexity algorithm for blind equalization of data
in OFDM-based wireless systems with general constellations. The proposed
algorithm is able to recover data even when the channel changes on a
symbol-by-symbol basis, making it suitable for fast fading channels. The
proposed algorithm does not require any statistical information of the channel
and thus does not suffer from latency normally associated with blind methods.
We also demonstrate how to reduce the complexity of the algorithm, which
becomes especially low at high SNR. Specifically, we show that in the high SNR
regime, the number of operations is of the order O(LN), where L is the cyclic
prefix length and N is the total number of subcarriers. Simulation results
confirm the favorable performance of our algorithm
Two-Stage LASSO ADMM Signal Detection Algorithm For Large Scale MIMO
This paper explores the benefit of using some of the machine learning
techniques and Big data optimization tools in approximating maximum likelihood
(ML) detection of Large Scale MIMO systems. First, large scale MIMO detection
problem is formulated as a LASSO (Least Absolute Shrinkage and Selection
Operator) optimization problem. Then, Alternating Direction Method of
Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is
motivated by its ability of solving convex optimization problems by breaking
them into smaller sub-problems, each of which are then easier to handle.
Further improvement is obtained using two stages of LASSO with interference
cancellation from the first stage. The proposed algorithm is investigated at
various modulation techniques with different number of antennas. It is also
compared with widely used algorithms in this field. Simulation results
demonstrate the efficacy of the proposed algorithm for both uncoded and coded
cases.Comment: 5 pages, 4 figure
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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