Constant envelope orthogonal frequency division multiplexing (CE-OFDM) is a multi-carrier waveform with 0 dB peak-to-average power ratio (PAPR). This property enables the exploitation of ...multi-carrier waveforms with non-linear power amplifiers, avoiding the undesirable clipping effects. However, the existing channel estimation techniques designed for OFDM cannot be reused, since the use of a phase modulator makes CE-OFDM a non-linear waveform. Previous works assumed that the channel estimation process relies on the transmission of preambles, and the data symbols are equalized using a frequency domain equalizer (FDE). To avoid the overhead induced by preambles, a phase-domain injected training (PIT) is proposed, where the pilot sequence is embedded in the phase dimension of the data symbols. This novel approach does not waste time and/or frequency resources as in preamble-based schemes. Moreover, it does not require additional power for the training. The received symbols are averaged with a dual procedure, and owing to the particular structure of CE-OFDM, the channel estimates are recovered. Also, the analytical expression of the channel estimation mean squared error (MSE) is derived. Finally, several numerical results illustrate the performance of the proposal, showing that the MSE, bit error rate (BER) and achievable rate are improved, as compared to the existing works.
Cognitive radio is considered a relevant communication paradigm to deal with the increasing demands in modern communications systems. Adaptive schemes are required to recognize channel conditions and ...to properly adjust main transmission parameters to improve the quality of communications. In this direction, blind algorithms to recover constellation, from phase-modulated signals, represent a means to implement cognitive capabilities to allow automatic modulation recognition (AMR) on receivers. Commonly, the most popular approaches for blind constellation recovery are based on a two-step scheme. The first step uses to equalize channel effects and reduce inter-symbol interference (ISI). The second step carries out constellation recovery utilizing phase locked loop (PLL) systems like the Costas Loop, then to classify the incoming signal. This work proposes a novel single-step blind adaptive filter solution, inspired by an adaptive interference canceler, for joint equalization and constellation symbol recovery from received phase shift keying (PSK) waveforms. Furthermore, we propose new coefficients update mechanisms based on the constant amplitude of PSK signals. The proposed solution exhibits reduced computational complexity compared to the state of the art and a reduced time of convergence. Additionally, the proposed scheme does not require a training sequence to operate properly. The obtained results clearly show that the proposed scheme significantly improves accuracy regarding phase symbol estimation and ISI reduction.
Pilot-pouring superimposed training (PPST) is a novel channel estimation technique specially designed for cyclic block filtered multi-tone (CB-FMT), where the pilot symbols are poured into the ...subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the opt where the pilot symbols are poured into the subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the optimization process, where the spectral efficiency must be maximized through the signal-to-interference plus noise ratio (SINR). Two optimization approaches are proposed, where the first one, referred as pilot-pouring optimization (PPO), is focused on performance at the expense of a high complexity, while the second one, denoted as low-complexity PPO (LPPO), is able to trade-off between performance and execution time. Numerical results are provided in order to show the validity of our proposal, where the different optimization problems are compared in terms of SINR and execution time.
Network densification is an emerging technology to achieve the key performance indicators expected in future wireless systems. However, several practical challenges as the appearance of interference ...channels need to be addressed. Interference alignment (IA) is a powerful technique to manage multiple concurrent transmissions in ultra-dense networks (UDNs). The fully digital (FD) IA has been widely studied. However, the research on IA with hybrid digital-analog beamforming (HB) for massive multiple-input multiple-output (mMIMO) scenarios, is limited. The reported works on HB-IA are designed assuming ideal phase shifters (PSs) without quantized phase constraints. Therefore, their performance severely degrades in practical systems with low-resolution PSs. Although quantized phase restrictions were considered in some HB schemes, they were limited to scenarios with a single transmitter, either single-user or multi-user. Thus, they fail when applied to UDNs since interference from neighboring cells is not considered in the design. Then, a new HB-IA is proposed for UDNs based on an iterative partial construction (IPC) procedure with the novelty of awareness of PSs quantization. The IPC algorithm relies on algebraic properties to iteratively compute the analog and digital matrices, via a partial submatrix composition, such that the Euclidean distance to the FD-IA solution is reduced. An additional digital block, based on an IA algorithm, is also applied to cancel the residual interference. Numerical results reveal that this proposal, referred as HB IPC-IA, significantly outperforms reported works achieving a near FD-IA performance. The obtained results provide practical insights for HB design in UDNs.
Cyclic block filtered multi-tone (CB-FMT) is a waveform that can be efficiently synthesized through a filter-bank in the frequency domain. Although the main principles have been already established, ...channel estimation has not been addressed yet. This is because of assuming that the existing techniques based on pilot symbol assisted modulation (PSAM), implemented in OFDM-like schemes, can be reused. However, PSAM leads to an undesirable loss of data-rate. In this paper, an alternative method inspired by the superimposed training (ST) concept, namely pilot pouring ST (PPST), is proposed. In PPST, pilots are superimposed over data taking advantage of the particular spectral characteristics of CB-FMT. Exploiting the sub-channel spectrum, the pilot symbols are poured in those resources unused for data transmission. This spectral shaping of pilots is also exploited at the receiver to carry out channel estimation, by enhancing those channel estimates that exhibit a low data interference contribution. Furthermore, a frequency domain resource mapping strategy for the data and poured pilot symbols is proposed to enable an accurate estimation in strongly frequency-selective channels. The parameters of the proposed scheme are optimized to minimize the channel estimation mean squared error (MSE). Finally, several numerical results illustrate the performance advantages of the proposed technique as compared to other alternatives.
Superimposed training (ST) is a semiblind channel estimation technique, proposed for orthogonal frequency division multiplexing (OFDM), where training sequences are added to data symbols, avoiding ...the use of dedicated pilot-subcarriers, and increasing the available bandwidth compared with pilot symbol assisted modulation (PSAM). Filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM) is a promising waveform technique considered to replace the OFDM, which takes advantage of well-designed filters to avoid the use of cyclic prefix and reduce the out-band-emissions. In this paper, we provide the expressions of the average channel capacity of the FBMC-OQAM combined with either PSAM or ST schemes, considering imperfect channel estimation and the presence of the pilot sequences. In order to compute the capacity expression of our proposal, ST-FBMC-OQAM, we analyze the channel estimation error and its variance. The average channel capacity is deduced considering the noise, data interference from ST, and the intrinsic self-interference of the FBMC-OQAM. Additionally, to maximize the average channel capacity, the optimal value of data power allocation is also obtained. The simulation results confirm the validity of the capacity analysis and demonstrate the superiority of the ST-FBMC-OQAM over existing proposals.
Nowadays, Cognitive Radio Sensor Networks (CRSN) arise as an emergent technology to deal with the spectrum scarcity issue and the focus is on devising novel energy-efficient solutions. In static ...CRSN, where nodes have spatial fixed positions, several reported solutions are implemented via sensor selection strategies to reduce consumed energy during cooperative spectrum sensing. However, energy-efficient solutions for dynamic CRSN, where nodes are able to change their spatial positions due to their movement, are nearly reported despite today's growing applications of mobile networks. This paper investigates a novel framework to optimally predict energy consumption in cooperative spectrum sensing tasks, considering node mobility patterns suitable to model dynamic CRSN. A solution based on the Kataoka criterion is presented, that allows to minimize the consumed energy. It accurately estimates -with a given probability-the spent energy on the network, then to derive an optimal energy-efficient solution. An algorithm of reduced-complexity is also implemented to determine the total number of active nodes improving the exhaustive search method. Proper performance of the proposed strategy is illustrated by extensive simulation results for pico-cells and femto-cells in dynamic scenarios.
Orthogonal frequency division multiplexing (OFDM) is the main transmission scheme for the fifth generation (5G) of wireless communications. To properly operate, an estimation of the channel ...coefficients is mandatory, for this reason, superimposed training (ST), a technique that transmits pilot and data symbols together, have become an attractive solution. In ST-based channel estimation, an averaging of the received signal is required to mitigate the data interference over the pilots. Unlike reported works that employed oversimplified block-selective channel models to determine the averaging length of ST, in this paper, a frequencyselective model is considered in the analysis. Thus, realistic and more accurate mean square error (MSE) expressions for the ST-based least squares (LS) and minimum MSE (MMSE) channel estimators are theoretically derived. Then, the optimum number of averaged subcarriers, which provides the lowest MSE, is analytically computed. Simulated results considering parameters from 5G New Radio (NR) standard show that the MSE and symbol error rate (SER) metrics can be improved by more than one order of magnitude in contrast to previous works, which either did not average any subcarrier or averaged a group of subcarriers given by the coherence bandwidth simplification. Finally, the robustness of the proposed method, which guarantees a high tolerance in performance for a wide range of imperfect input parameters, is addressed.
Optimal Sensing Policy for Energy Harvesting Cognitive Radio Systems Villalonga, David Alejandro Urquiza; Gomez, Jorge Torres; Garcia, M. Julia Fernandez-Getino
IEEE transactions on wireless communications,
2020-June, 2020-6-00, 20200601, Letnik:
19, Številka:
6
Journal Article
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Energy harvesting (EH) emerges as a novel technology to promote green energy policies. Based on Cognitive Radio (CR) paradigm, nodes are designed to operate with harvested energy from radio frequency ...signals. CR-EH systems state several strategies based on sensing and access policies to maximize throughput and protect primary users from interference, simultaneously. However, reported solutions do not consider to maximize detection performance to detect spectrum holes which represent a major drawback whenever available energy is not efficiently used. In this concern, this paper addresses optimal sensing policies based on energy harvesting schemes to maximize probability of detection of available spectrum. These novel policies may be incorporated to previous reported solutions to maximize performance. Optimal processing scheduling schemes are proposed for offline and online scenarios based on convex optimization theory, Dynamic Programming (DP) algorithm and heuristic solutions (Constant Power and Greedy policies). Performance of proposed policies are validated by simulations for common detection techniques such as Matched Filter (MF), Quadrature Matched Filter (QMF) and Energy Detector (ED). As a result, it is shown that the best detection scheme theoretically addressed by MF, does not always perform better than the poorest detection scheme, given by the ED, in an energy harvesting scenario.
The Industry 4.0 paradigm conceives a cyber-physical supporting framework for the manufacturing processes in smart factories. In this context, solutions concerning the wired communications at the ...field-level have been reported which utilize either fieldbuses, which exhibit a huge distance range but a reduced data rate in a bus topology, or Ethernet-based technologies, which provide an increased data rate but reduced distance in a ring topology. To overcome this shortage, we propose the use of orthogonal frequency division multiplexing (OFDM) to significantly increase the achievable data rates over large distances in industrial bus systems. Also, we establish a novel methodology to compute the signal-to-noise ratio between arbitrary pairs of nodes, which in turn allows to compute the communication capacity. Our wideband system was validated by connecting up to 32 nodes in the distance range 100 m-1 km. Compared to fieldbuses, the results of the proposal exhibit an amazing improvement in data rate of about fifty times for 100m distance and more than ten times for 0.5 km. Moreover, with respect to Ethernet-based solutions, the results show a relevant improvement in the data rate of around five times for 100m distance, but Ethernet-based systems cannot go beyond this distance, to which our proposal is not limited.
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