Power loading for OFDM in tactical packet radio systems

Juang, Michael A.; Pursley, M.B.

doi:10.1109/milcom.2011.6127734

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…We can also see that M_diag algorithm always outperforms M_eig algorithms. This result is expected for the reason that the upper bound in ( 24) is tighter than the upper bound in (21). From It is noted that the BER decreases with the number of users, which can be explained by the multiuser diversity effect.…”

Section: Simulation Resultsmentioning

confidence: 83%

“…However, its complexity to find the optimal solution can be still high as the parameter θ  must be numerically obtained. Replacing the , k n BER with the upper bound in (12), we can use a similar algorithm in [14] [21] which allocates more power to the best subcarriers when average signal noise ratio (SNR) is low and allocates more power to the worst subcarriers average SNR is high. The derivation of the algorithm can be found in Appendix, and the power allocation for the ML detector is given as follows:…”

Section: Suboptimal Resource Allocation Algorithmmentioning

confidence: 99%

“…Then, we use the suboptimal power allocation algorithm as described in Appendix based on the upper bound in (21). Thus, n…”

Section: Suboptimal Resource Allocation Algorithmmentioning

confidence: 99%

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Subcarrier and Power Allocation for Multiuser MIMO-OFDM Systems with Various Detectors

2017

KSII TIIS

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Resource allocation plays a crucial role in multiuser multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems to improve overall system performance. While previously proposed resource allocation algorithms are mainly designed from the point of view of the information-theoretic, we formulate the resource allocation problem as an average bit error rate (BER) minimization problem subject to a total power constraint when considering employing realistic MIMO detection techniques. Subsequently, we derive the optimal subcarrier and power allocation algorithms for three types of well-known MIMO detectors, including the maximum likelihood (ML) detector, linear detectors, and successive interference cancellation (SIC) detectors. To reduce the complexity, we also propose a two-step suboptimal algorithm that separates subcarrier and power allocation for each detector. We also analyze the diversity gain of the proposed suboptimal algorithms for various MIMO detectors. Simulation results confirm that the proposed suboptimal algorithm for each detector can achieve a comparable performance with the optimal allocation with a much lower complexity. Moreover, it is shown that the suboptimal algorithms perform better than the conventional algorithms that are known in the literature.

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Section: Simulation Resultsmentioning

confidence: 83%

Section: Suboptimal Resource Allocation Algorithmmentioning

confidence: 99%

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Subcarrier and Power Allocation for Multiuser MIMO-OFDM Systems with Various Detectors

2017

KSII TIIS

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“…Furthermore, the power loading algorithms that minimize the bit error probability use approximations for the error probability that are not accurate for the signal-tonoise ratios of interest in packet radio systems that employ error-control coding. In [10], we investigated half-duplex tactical packet communications with OFDM modulation, error-control coding, and iterative decoding. We examined the adaptation of the code rate and subcarrier modulation (without power loading) as an alternative to reliance on power loading.…”

Section: Research Resultsmentioning

confidence: 99%

Receiver Statistics for Cognitive Radios in Dynamic Spectrum Access Networks

Pursley¹

2012

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“…We have obtained both analytical and simulation results that demonstrate the performance of our protocol, and we also have performance bounds to use as benchmarks in the design of future OFDM systems for tactical networks. Comparisons with power loading, which is often proposed for commercial applications, are given in [JuP11], where we show that our protocol for adaptive modulation and coding provides higher throughput and more robust performance.…”

Section: Scientific Progressmentioning

confidence: 98%

Cognitive Radio for Tactical Wireless Communication Networks

Pursley¹

2011

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Research has been completed on adaptive modulation and coding for multicast transmission in a tactical packet radio network. Our adaptive multicast transmission compensates for changes in propagation conditions that occur from packet to packet during a session with one sender and multiple receivers. We also completed our research investigation of adaptive coding for frequency-hop communications over tactical communications channels that have time-varying fading and partial-band interference. New protocols were developed that combat fading and ABSTRACTResearch has been completed on adaptive modulation and coding for multicast transmission in a tactical packet radio network. Our adaptive multicast transmission compensates for changes in propagation conditions that occur from packet to packet during a session with one sender and multiple receivers. We also completed our research investigation of adaptive coding for frequency-hop communications over tactical communications channels that have time-varying fading and partial-band interference. New protocols were developed that combat fading and interference while providing throughput levels that are near the theoretical upper bounds. New distributed protocols for channel access and adaptive spreading were devised and evaluated for use in direct-sequence spread-spectrum packet radio networks that have no central controllers, access points, or base stations. Information theoretic bounds and tools have been derived that give performance benchmarks for practical protocols, provide analytical techniques for the minimization of resource consumption in tactical networks, and simplify the design of protocols for the adaptation of coding and spreading in direct-sequence spread-spectrum packet radios.

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Power loading for OFDM in tactical packet radio systems

Cited by 5 publications

References 15 publications

Subcarrier and Power Allocation for Multiuser MIMO-OFDM Systems with Various Detectors

Subcarrier and Power Allocation for Multiuser MIMO-OFDM Systems with Various Detectors

Receiver Statistics for Cognitive Radios in Dynamic Spectrum Access Networks

Cognitive Radio for Tactical Wireless Communication Networks

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