182 research outputs found
Konveksiin optimointiin perustuva resurssien allokointi moniantennijärjestelmissä
No full textAbstractThe use of multiple antennas is a fundamental requirement in future wireless networks as it helps to increase the reliability and spectral efficiency of mobile radio links. In this thesis, we study convex optimization based radio resource allocation methods for the downlink of multi-antenna systems.First, the problem of admission control in the downlink of a multicell multiple-input single-output (MISO) system has been considered. The objective is to maximize the number of admitted users subject to a signal-to-interference-plus-noise ratio (SINR) constraint at each admitted user and a transmit power constraint at each base station (BS). We have cast the admission control problem as an ℓ0 minimization problem; it is known to be combinatorial, NP-hard. Centralized and distributed algorithms to solve this problem have been proposed. To develop the centralized algorithm, we have used sequential convex programming (SCP). The distributed algorithm has been derived by using the consensus-based alternating direction method of multipliers in conjunction with SCP. We have shown numerically that the proposed admission control algorithms achieve a near-to-optimal performance. Next, we have extended the admission control problem to provide fairness, where long-term fairness among the users has been guaranteed. We have focused on proportional and max-min fairness, and proposed dynamic control algorithms via Lyapunov optimization. Results show that these proposed algorithms guarantee fairness.Then, the problem of admission control for the downlink of a MISO heterogeneous networks (hetnet) has been considered, and the proposed centralized and distributed algorithms have been adapted to find a solution. Numerically, we have illustrated that the centralized algorithm achieves a near-to-optimal performance, and the distributed algorithm’s performance is closer to the optimal value.Finally, an algorithm to obtain the set of all achievable power-rate tuples for a multiple-input multiple-output hetnet has been provided. The setup consists of a single macrocell and a set of femtocells. The interference power to the macro users from the femto BSs has been kept below a threshold. To find the set of all achievable power-rate tuples, a two-dimensional vector optimization problem is formulated, where we have considered maximizing the sum-rate while minimizing the sum-power, subject to maximum power and interference threshold constraints. This problem is known to be NP-hard. A solution method is provided by using the relationship between the weighted sum-rate maximization and weighted-sum-mean-squared-error minimization problems. The proposed algorithm was used to evaluate the impact of imposing interference threshold constraints and the co-channel deployments in a hetnet.TiivistelmäMonen antennin käyttö on perusvaatimus tulevissa langattomissa verkoissa, koska se auttaa lisäämään matkaviestinyhteyksien luotettavuutta ja spektritehokkuutta. Tässä väitöskirjassa tutkitaan konveksiin optimointiin perustuvia radioresurssien allokointimenetelmiä moniantennijärjestelmien alalinkin suunnassa.Ensiksi on käsitelty pääsynvalvonnan ongelmaa alalinkin suuntaan monen solun moni-tulo yksi-lähtö (MISO) -verkoissa. Tavoitteena on maksimoida hyväksyttyjen käyttäjien määrä, kun hyväksytyille käyttäjille on asetettu signaali-häiriö-kohinasuhteen (SINR) rajoitus, ja tukiasemille lähetystehon rajoitus. Pääsynvalvonnan ongelma on muotoiltu ℓ0-minimointiongelmana, jonka tiedetään olevan kombinatorinen, NP-vaikea ongelma. Ongelman ratkaisemiseksi on ehdotettu keskitettyjä ja hajautettuja algoritmeja. Keskitetty optimointialgoritmi perustuu sekventiaaliseen konveksiin optimointiin. Hajautettu algoritmi pohjautuu konsensusoptimointimenetelmään ja sekventiaaliseen konveksiin optimointiin. Ehdotettujen pääsynvalvonta-algoritmien on numeerisesti osoitettu saavuttavan lähes optimaalinen suorituskyky. Lisäksi pääsynvalvontaongelma on laajennettu takaamaan pitkän aikavälin oikeudenmukaisuus käyttäjien välillä. Työssä käytetään erilaisia määritelmiä oikeudenmukaisuuden takaamiseen, ja ehdotetaan dynaamisia algoritmeja pohjautuen Lyapunov-optimointiin. Tulokset osoittavat, että ehdotetuilla algoritmeilla taataan käyttäjien välinen oikeudenmukaisuus.Tämän jälkeen käsitellään heterogeenisen langattoman MISO-verkon pääsynvalvonnan ongelmaa. Edellä ehdotettuja keskitettyjä ja hajautettuja algoritmeja on muokattu tämän ongelman ratkaisemiseksi. Työssä osoitetaan numeerisesti, että sekä keskitetyllä että hajautetulla algoritmilla saavutetaan lähes optimaalinen suorituskyky.Lopuksi on laadittu algoritmi, jolla löydetään kaikki saavutettavissa olevat teho-datanopeusparit heterogeenisessä langattomassa moni-tulo moni-lähtö (MIMO) -verkossa. Verkko koostuu yhdestä makrosolusta ja useasta piensolusta. Piensolutukiasemista makrokäyttäjiin kohdistuvan häiriön teho on pidetty tietyn rajan alapuolella. Kaikkien saavutettavien teho-datanopeusparien löytämiseksi on laadittu kaksiulotteinen vektorioptimointiongelma, jossa maksimoidaan summadatanopeus pyrkien minimoimaan kokonaisteho, kun enimmäisteholle ja häiriökynnykselle on asetettu rajoitukset. Tämän ongelman tiedetään olevan NP-vaikea. Ongelman ratkaisemiseksi käytetään painotetun summadatanopeuden maksimointiongelman, ja painotetun keskineliövirheen minimointiongelman välistä suhdetta. Ehdotettua algoritmia käytettiin arvioimaan häiriörajoitusten ja saman kanavan käyttöönoton vaikutusta heterogeenisessä langattomassa verkossa.Academic dissertation to be presented with the assent of the Doctoral Training Committee of Technology and Natural Sciences of the University of Oulu for public defence in Kuusamonsali (YB210), Linnanmaa, on 9 January 2018, at 12 noonAbstract
The use of multiple antennas is a fundamental requirement in future wireless networks as it helps to increase the reliability and spectral efficiency of mobile radio links. In this thesis, we study convex optimization based radio resource allocation methods for the downlink of multi-antenna systems.
First, the problem of admission control in the downlink of a multicell multiple-input single-output (MISO) system has been considered. The objective is to maximize the number of admitted users subject to a signal-to-interference-plus-noise ratio (SINR) constraint at each admitted user and a transmit power constraint at each base station (BS). We have cast the admission control problem as an ℓ0 minimization problem; it is known to be combinatorial, NP-hard. Centralized and distributed algorithms to solve this problem have been proposed. To develop the centralized algorithm, we have used sequential convex programming (SCP). The distributed algorithm has been derived by using the consensus-based alternating direction method of multipliers in conjunction with SCP. We have shown numerically that the proposed admission control algorithms achieve a near-to-optimal performance. Next, we have extended the admission control problem to provide fairness, where long-term fairness among the users has been guaranteed. We have focused on proportional and max-min fairness, and proposed dynamic control algorithms via Lyapunov optimization. Results show that these proposed algorithms guarantee fairness.
Then, the problem of admission control for the downlink of a MISO heterogeneous networks (hetnet) has been considered, and the proposed centralized and distributed algorithms have been adapted to find a solution. Numerically, we have illustrated that the centralized algorithm achieves a near-to-optimal performance, and the distributed algorithm’s performance is closer to the optimal value.
Finally, an algorithm to obtain the set of all achievable power-rate tuples for a multiple-input multiple-output hetnet has been provided. The setup consists of a single macrocell and a set of femtocells. The interference power to the macro users from the femto BSs has been kept below a threshold. To find the set of all achievable power-rate tuples, a two-dimensional vector optimization problem is formulated, where we have considered maximizing the sum-rate while minimizing the sum-power, subject to maximum power and interference threshold constraints. This problem is known to be NP-hard. A solution method is provided by using the relationship between the weighted sum-rate maximization and weighted-sum-mean-squared-error minimization problems. The proposed algorithm was used to evaluate the impact of imposing interference threshold constraints and the co-channel deployments in a hetnet.Tiivistelmä
Monen antennin käyttö on perusvaatimus tulevissa langattomissa verkoissa, koska se auttaa lisäämään matkaviestinyhteyksien luotettavuutta ja spektritehokkuutta. Tässä väitöskirjassa tutkitaan konveksiin optimointiin perustuvia radioresurssien allokointimenetelmiä moniantennijärjestelmien alalinkin suunnassa.
Ensiksi on käsitelty pääsynvalvonnan ongelmaa alalinkin suuntaan monen solun moni-tulo yksi-lähtö (MISO) -verkoissa. Tavoitteena on maksimoida hyväksyttyjen käyttäjien määrä, kun hyväksytyille käyttäjille on asetettu signaali-häiriö-kohinasuhteen (SINR) rajoitus, ja tukiasemille lähetystehon rajoitus. Pääsynvalvonnan ongelma on muotoiltu ℓ0-minimointiongelmana, jonka tiedetään olevan kombinatorinen, NP-vaikea ongelma. Ongelman ratkaisemiseksi on ehdotettu keskitettyjä ja hajautettuja algoritmeja. Keskitetty optimointialgoritmi perustuu sekventiaaliseen konveksiin optimointiin. Hajautettu algoritmi pohjautuu konsensusoptimointimenetelmään ja sekventiaaliseen konveksiin optimointiin. Ehdotettujen pääsynvalvonta-algoritmien on numeerisesti osoitettu saavuttavan lähes optimaalinen suorituskyky. Lisäksi pääsynvalvontaongelma on laajennettu takaamaan pitkän aikavälin oikeudenmukaisuus käyttäjien välillä. Työssä käytetään erilaisia määritelmiä oikeudenmukaisuuden takaamiseen, ja ehdotetaan dynaamisia algoritmeja pohjautuen Lyapunov-optimointiin. Tulokset osoittavat, että ehdotetuilla algoritmeilla taataan käyttäjien välinen oikeudenmukaisuus.
Tämän jälkeen käsitellään heterogeenisen langattoman MISO-verkon pääsynvalvonnan ongelmaa. Edellä ehdotettuja keskitettyjä ja hajautettuja algoritmeja on muokattu tämän ongelman ratkaisemiseksi. Työssä osoitetaan numeerisesti, että sekä keskitetyllä että hajautetulla algoritmilla saavutetaan lähes optimaalinen suorituskyky.
Lopuksi on laadittu algoritmi, jolla löydetään kaikki saavutettavissa olevat teho-datanopeusparit heterogeenisessä langattomassa moni-tulo moni-lähtö (MIMO) -verkossa. Verkko koostuu yhdestä makrosolusta ja useasta piensolusta. Piensolutukiasemista makrokäyttäjiin kohdistuvan häiriön teho on pidetty tietyn rajan alapuolella. Kaikkien saavutettavien teho-datanopeusparien löytämiseksi on laadittu kaksiulotteinen vektorioptimointiongelma, jossa maksimoidaan summadatanopeus pyrkien minimoimaan kokonaisteho, kun enimmäisteholle ja häiriökynnykselle on asetettu rajoitukset. Tämän ongelman tiedetään olevan NP-vaikea. Ongelman ratkaisemiseksi käytetään painotetun summadatanopeuden maksimointiongelman, ja painotetun keskineliövirheen minimointiongelman välistä suhdetta. Ehdotettua algoritmia käytettiin arvioimaan häiriörajoitusten ja saman kanavan käyttöönoton vaikutusta heterogeenisessä langattomassa verkossa
Spectrum Occupancy Prediction based on adaptive Recurrent Neural Networks
No full textAbstractThis paper investigates the prediction of spectrum usage by wireless local area networks based on recurrent neural networks (RNNs). The prediction results can be used to enhance the spectrum efficiency in dynamic spectrum sharing, and accuracy of spectrum sensing in cognitive radio networks. Observed time series of duty cycle (DC), which indicates the spectrum usage trend, is utilized to predict the future DC by the RNN. At first, we reveal a drawback of prediction by a single RNN, and this approach is denoted by a conventional approach in this paper. Specifically, the prediction results may have a significant biased error if the observed DCs are biased to either high or low values. For this problem, we propose the DC prediction with two RNNs and each of them designed for high DC case and low DC case, respectively. The prediction algorithm at first identifies the state of current DC either high or low. Then, the RNN for the identified state is performed for an accurate DC prediction. Numerical evaluations based on comprehensive measurement experiments of spectrum usage have presented that the proposed DC prediction can improve the accuracy of DC prediction compared to the conventional approach.Abstract
This paper investigates the prediction of spectrum usage by wireless local area networks based on recurrent neural networks (RNNs). The prediction results can be used to enhance the spectrum efficiency in dynamic spectrum sharing, and accuracy of spectrum sensing in cognitive radio networks. Observed time series of duty cycle (DC), which indicates the spectrum usage trend, is utilized to predict the future DC by the RNN. At first, we reveal a drawback of prediction by a single RNN, and this approach is denoted by a conventional approach in this paper. Specifically, the prediction results may have a significant biased error if the observed DCs are biased to either high or low values. For this problem, we propose the DC prediction with two RNNs and each of them designed for high DC case and low DC case, respectively. The prediction algorithm at first identifies the state of current DC either high or low. Then, the RNN for the identified state is performed for an accurate DC prediction. Numerical evaluations based on comprehensive measurement experiments of spectrum usage have presented that the proposed DC prediction can improve the accuracy of DC prediction compared to the conventional approach
Energy Detection for <em>M</em>-QAM Signals
No full textAbstractAccurate threshold setting for energy detector is important for example in dynamic spectrum access. This requires accurate statistical distribution models of the observed energy. In this paper, we consider energy detection (ED) for -ary quadrature amplitude modulation (QAM) signals. The derivation of the exact solution of the distribution model (ES) requires all combinations of QAM signals in the observed signals based on the brute-force search and it leads to a significant computational cost. For this issue, this paper proposes three statistical distribution models which assume =∞ to avoid the brute-force search. Due to the assumption of , the proposed models are independent of and can handle adaptive modulation where can be changed dynamically. In the numerical evaluations, we compare the three proposed models with the other typical approximation models under additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. In addition, the proposed models are extended for more realistic scenario where imperfect synchronization is considered. The comprehensive numerical evaluations show that the first proposed model is most accurate among all considered models except ES but requires relatively high computational cost. The second proposed model where the observed energy is assumed to follow Gaussian distribution is the least complexity but can have reduced accuracy. The third proposed model based on skew-normal distribution can achieve comparable accuracy and less complexity compared to the first model.Abstract
Accurate threshold setting for energy detector is important for example in dynamic spectrum access. This requires accurate statistical distribution models of the observed energy. In this paper, we consider energy detection (ED) for -ary quadrature amplitude modulation (QAM) signals. The derivation of the exact solution of the distribution model (ES) requires all combinations of QAM signals in the observed signals based on the brute-force search and it leads to a significant computational cost. For this issue, this paper proposes three statistical distribution models which assume =∞ to avoid the brute-force search. Due to the assumption of , the proposed models are independent of and can handle adaptive modulation where can be changed dynamically. In the numerical evaluations, we compare the three proposed models with the other typical approximation models under additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. In addition, the proposed models are extended for more realistic scenario where imperfect synchronization is considered. The comprehensive numerical evaluations show that the first proposed model is most accurate among all considered models except ES but requires relatively high computational cost. The second proposed model where the observed energy is assumed to follow Gaussian distribution is the least complexity but can have reduced accuracy. The third proposed model based on skew-normal distribution can achieve comparable accuracy and less complexity compared to the first model
Enhanced Signal Detection and Constellation Design for Massive SIMO Communications With 1-Bit ADCs
No full textAbstractIn this paper, we investigate a transmitter and receiver design for a single-user massive SIMO (single-input multiple-output) system with 1-bit analog-to-digital converters (ADCs) at the base station (BS), where the user adopts higher-order modulation, e.g., 16-quadrature amplitude modulation (16-QAM), for the data transmission. For the channel estimation and the signal detection, linear least-squares (LS) estimation and maximum ratio combining (MRC) are respectively employed. In this context, we first introduce closed-form formulas for the mean of the estimated symbols and for the correlation matrix between their real and imaginary parts considering the effect of 1-bit quantization. The study of the distribution of the estimated symbols indicates that, in presence of 1-bit ADCs, the conventional 16-QAM detector and the typical square 16-QAM modulation are not adequate. In light of this, we propose three novel symbol detectors and re-design the 16-QAM modulation in order to improve the symbol error rate (SER). Furthermore, the upper bound on the SER is analyzed based on the pair-wise error probability and the boundary equation between two regions is also studied. Through numerical results, the proposed framework, i.e., the symbol detector and the transmit constellation design, shows a significant enhancement in the SER performance against the conventional detector and the typical square 16-QAM modulation.Abstract
In this paper, we investigate a transmitter and receiver design for a single-user massive SIMO (single-input multiple-output) system with 1-bit analog-to-digital converters (ADCs) at the base station (BS), where the user adopts higher-order modulation, e.g., 16-quadrature amplitude modulation (16-QAM), for the data transmission. For the channel estimation and the signal detection, linear least-squares (LS) estimation and maximum ratio combining (MRC) are respectively employed. In this context, we first introduce closed-form formulas for the mean of the estimated symbols and for the correlation matrix between their real and imaginary parts considering the effect of 1-bit quantization. The study of the distribution of the estimated symbols indicates that, in presence of 1-bit ADCs, the conventional 16-QAM detector and the typical square 16-QAM modulation are not adequate. In light of this, we propose three novel symbol detectors and re-design the 16-QAM modulation in order to improve the symbol error rate (SER). Furthermore, the upper bound on the SER is analyzed based on the pair-wise error probability and the boundary equation between two regions is also studied. Through numerical results, the proposed framework, i.e., the symbol detector and the transmit constellation design, shows a significant enhancement in the SER performance against the conventional detector and the typical square 16-QAM modulation
Analysis of optimal weighted cooperative spectrum sensing based on estimation of primary user signal power
No full text
Proactive Radar Protection System in Shared Spectrum via Forecasting Secondary User Power Levels
No full textAbstractSpectrum sharing in radar bands with interference forecasting for enhanced radar protection can help design proactive resource allocation solutions which can achieve high data rates for wireless communication networks on one hand and help protect the incumbent radar systems. We consider radar spectrum sharing in 5.6GHz where a weather radar operates as a primary system and the dominant secondary system is an enterprise network consisting of access points (APs) in a university campus. Our work models transmit the power of the APs as a time series with multinomial distribution based on real collected data. The aggregated interference due to the transmissions from the APs at the radar is forecasted using a long short-term memory (LSTM) based neural network. Monte Carlo dropout is utilized to generate prediction intervals that capture the uncertainties in the interference from the APs. Finally, by using both average and upper limits of predicted interference time series a cloud-assisted efficient sharing and radar protection algorithm is proposed. Tracking the rotating radar is not required in the proposed system. The results show that the proposed efficient sharing and radar protection system ensures better radar protection and increased throughput for wireless communication users.Abstract
Spectrum sharing in radar bands with interference forecasting for enhanced radar protection can help design proactive resource allocation solutions which can achieve high data rates for wireless communication networks on one hand and help protect the incumbent radar systems. We consider radar spectrum sharing in 5.6GHz where a weather radar operates as a primary system and the dominant secondary system is an enterprise network consisting of access points (APs) in a university campus. Our work models transmit the power of the APs as a time series with multinomial distribution based on real collected data. The aggregated interference due to the transmissions from the APs at the radar is forecasted using a long short-term memory (LSTM) based neural network. Monte Carlo dropout is utilized to generate prediction intervals that capture the uncertainties in the interference from the APs. Finally, by using both average and upper limits of predicted interference time series a cloud-assisted efficient sharing and radar protection algorithm is proposed. Tracking the rotating radar is not required in the proposed system. The results show that the proposed efficient sharing and radar protection system ensures better radar protection and increased throughput for wireless communication users
Welch FFT Segment Size Selection Method for FFT Based Wide Band Spectrum Measurement
No full textAbstractWe introduce a Welch FFT segment size selection method for FFT-based wide band spectrum measurement in the context of smart spectrum access (SSA), in which statistical spectrum usage information of primary users (PUs), such as duty cycle (DC), will be exploited by secondary users (SUs). Energy detectors (EDs) based on Welch FFT can detect the presence of PU signals in a broadband environment efficiently, and DC can be estimated properly if a Welch FFT segment size is set suitably. There is a trade-off between detection performance and frequency resolution in terms of the Welch FFT segment size. The optimum segment size depends on signal-to-noise ratio (SNR) which makes practical and optimum segment size setting difficult. For this issue, we previously proposed a segment size selection method employing a relationship between noise floor (NF) estimation output and the segment size without SNR information. It can achieve accurate spectrum awareness at the expense of relatively high computational complexity since it employs exhaustive search to select a proper segment size. In this paper, we propose a segment size selection method that offers reasonable spectrum awareness performance with low computational complexity since limited search is used. Numerical evaluations show that the proposed method can match the spectrum awareness performance of the conventional method with 70% lower complexity or less.Abstract
We introduce a Welch FFT segment size selection method for FFT-based wide band spectrum measurement in the context of smart spectrum access (SSA), in which statistical spectrum usage information of primary users (PUs), such as duty cycle (DC), will be exploited by secondary users (SUs). Energy detectors (EDs) based on Welch FFT can detect the presence of PU signals in a broadband environment efficiently, and DC can be estimated properly if a Welch FFT segment size is set suitably. There is a trade-off between detection performance and frequency resolution in terms of the Welch FFT segment size. The optimum segment size depends on signal-to-noise ratio (SNR) which makes practical and optimum segment size setting difficult. For this issue, we previously proposed a segment size selection method employing a relationship between noise floor (NF) estimation output and the segment size without SNR information. It can achieve accurate spectrum awareness at the expense of relatively high computational complexity since it employs exhaustive search to select a proper segment size. In this paper, we propose a segment size selection method that offers reasonable spectrum awareness performance with low computational complexity since limited search is used. Numerical evaluations show that the proposed method can match the spectrum awareness performance of the conventional method with 70% lower complexity or less
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