Optimal Power Allocation for PSM-OFDM Systems With Imperfect Channel Estimation

Li, You; Pang, Yashan; Tang, Wanbin; Lei, Xia; Xiao, Yue

doi:10.1109/access.2018.2818940

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…However, it is shown in [29] that the derived PA matrix makes the final equivalent channel an identity matrix, which may obscure the estimation of the active antenna index and hence only a marginal BER gain was achieved. Moreover, the authors of [30] investigated the RSM-aided OFDM systems and proposed an optimal method for power allocation between the data and pilot symbols. By formulating the PA problem into an approximate convex approximation based one, a new unified PA framework was proposed for RSM-MIMO systems in [31], where the low-complexity maximum minimum Euclidean distance (MMD) criterion was adopted rather than the complex performance bound based criterion considered in [29].…”

Section: Enhanced Receive Spatial Modulation Based On Power Allocationmentioning

confidence: 99%

Enhanced Receive Spatial Modulation Based on Power Allocation

Yang¹,

Xiao²,

Xiao³

et al. 2019

IEEE J. Sel. Top. Signal Process.

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In this paper, we investigate the benefits of power allocation (PA) for multiple-input multiple-output (MIMO) receive spatial modulation (RSM) with both a total transmit power constraint (TTPC) and a per-antenna power constraint (PAPC). First, we derive optimal PA closed-form solutions that maximize the minimum distance d min between the received signal points for (N t × 2)-element RSM with arbitrary phase-shift keying schemes (where N t is the number of transmit antennas) subject to a TTPC. Based on the derived solutions and the error vector reduction (EVR) method, we propose a low-complexity iterative algorithm to identify PA parameters for high numbers of receive antennas (N r ≥ 2). Specifically, the EVR-based PA (EVR-PA) algorithm resembles its traditional exhaustive-search-based counterpart, but only exploits the receive distances of a few dominant error vectors to iteratively optimize the PA matrix. Then, a more strict yet practical PAPC is considered for PA in RSM-MIMO systems, and a welldesigned approximate convex optimization (ACO)-based iterative PA algorithm is proposed. Compared to EVR-PA, the ACO-based PA (ACO-PA) algorithm first formulates the PA problems with the PAPC in RSM into constrained quadratic program problems and then utilizes the powerful augmented Lagrangian multiplier to find their optimal solutions. Our simulation results show that the proposed EVR-PA-and ACO-PA-aided RSM schemes outperform the equal-power-allocated RSM-and PA-aided spatial multiplexing schemes.

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Section: Enhanced Receive Spatial Modulation Based On Power Allocationmentioning

confidence: 99%

Enhanced Receive Spatial Modulation Based on Power Allocation

Yang¹,

Xiao²,

Xiao³

et al. 2019

IEEE J. Sel. Top. Signal Process.

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“…Further, PA algorithms designed upon signal constellations for orthogonal frequency division multiplexing (OFDM) and layered division multiplexing scenarios were also deployed for SM systems and its variants [24,25]. Another class of CSI-aided SM techniques [26][27][28][29][30], a variant of SM, was conceived for encoding information bits to the receive antenna index and providing a low-complexity receiver.…”

Section: Introductionmentioning

confidence: 99%

Power allocation for offset spatial modulation

et al. 2023

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Offset spatial modulation (OSM) is an attractive variant in the family of spatial modulation technology, which is capable of reducing the frequency of radio frequency chain switching in practical implementations. This paper elaborates on the benefits of adaptive power allocation (PA) conceived for OSM systems. First, a closed-form solution of the optimal PA algorithm is derived for the case of two transmit antennas, which maximizes the minimum Euclidean distance (ED) between the received signal points. Moreover, based on the above solutions, a low-complexity iterative PA algorithm is proposed for larger numbers of transmit antennas, by exploiting the EDs of a few dominant error vectors. Furthermore, for striking a flexible trade-off between the bit error performance and complexity, an iterative convex approximation based PA algorithm is also developed. Finally, simulation results are presented to show that the proposed PA algorithms provide considerable improvements on the original OSM in terms of the bit error rate.

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