Abstract
This paper presents a novel software radio implementation for joint channel estimation, data decoding, and noise variance estimation in multiple-input multiple-output (MIMO) space division multiple access (SDMA). In contrast to many other iterative solutions, the proposed receiver is derived within the theoretical framework of a unified message-passing algorithm, combining belief propagation (BP) and the mean field approximation (MF) on the corresponding factor graph. The algorithm minimizes the region-based variational free energy in the system under appropriate conditions and, hence, converges to a fixpoint. As a use-case, we consider the high-rate packet-oriented IEEE 802.11n standard. Our receiver is implemented on a software-defined radio platform dubbed MIMONet, composed of a GNU radio software component and a universal software radio peripheral (USRP). The receiver was evaluated in real indoor environments. The results of our study clearly show that, once synchronization issues are properly addressed, the BP-MF receiver provides a substantial performance improvement compared to a conventional receiver also in real-world settings. Such improvement comes at the expense of an increase in running time that can be as high as 87. Therefore, the trade-off between communication performance and receiver complexity should be carefully evaluated in practical settings.
Highlights
Multiple-input multiple-output (MIMO) technology is popular in wireless communications due to the increased spectrum efficiency brought along by the use of multiple antennas in transmission, reception, or both
There is only one publication related to a universal software radio peripheral (USRP) hardware/GNU radio software implementation based on a theoretical framework: the expectationmaximization (EM) algorithm with a belief propagation (BP) maximization step has been used in the context of orthogonal frequency division multiplexing (OFDM) physical-layer network coding (PNC) systems for phase tracking and single-user channel decoding [20]
The results of our experiments clearly demonstrate the receiver complexity vs. performance trade-off: if BP-mean field approximation (MF) is executed with a single iteration, its performance is worse than that of the conventional receiver
Summary
Multiple-input multiple-output (MIMO) technology is popular in wireless communications due to the increased spectrum efficiency brought along by the use of multiple antennas in transmission, reception, or both. When the probabilistic model contains both continuous and discrete variables and the dependencies between them are both deterministic and stochastic, it is advantageous to apply the BP and MF algorithms in those parts of the factor graph where they are most suitable For this purpose, we employ the recently proposed unifying inference framework that combines BP and MF [7]. There is only one publication related to a USRP hardware/GNU radio software implementation based on a theoretical framework: the expectationmaximization (EM) algorithm with a BP maximization step has been used in the context of OFDM physical-layer network coding (PNC) systems for phase tracking and single-user channel decoding [20]. The pdf of a Gamma distribution with scale a and rate b is denoted by Ga(·; a, b)
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