Turbo Receiver Channel Estimation for GFDM-Based Cognitive Radio Networks

Na, Zhenyu; Pan, Zheng; Xiong, Ming; Liu, Xin; Lu, Weidang; Wang, Yongjian; Fan, Lisheng

doi:10.1109/access.2018.2803742

Cited by 37 publications

(15 citation statements)

References 28 publications

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“…GFDM uses one CP to protect the entire block, reducing the overhead in the physical layer when compared with OFDM. The GFDM filtering reduces the OOBE, allowing this waveform to be used in cognitive radio networks [40]. All versions of the prototype filter can be organized in the transmit matrix A, allowing the GFDM symbol to be defined as [41]:…”

Section: Proposed Scma-gfdm Integrationmentioning

confidence: 99%

Sparse code multiple access on the generalized frequency division multiplexing

Aquino

Mendes

2020

J Wireless Com Network

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Recent advances in the communication systems culminated in a new class of multiple access schemes, named non-orthogonal multiple access (NOMA), where the main goal is to increase the spectrum efficiency by overlapping data from different users in a single time-frequency resource used by the physical layer. NOMA receivers can resolve the interference among data symbols from different users, increasing the overall system spectrum efficiency without introducing symbol error rate (SER) performance loss, which makes this class of multiple access techniques interesting for future mobile communication systems. This paper analyzes one promising NOMA technique, called sparse code multiple access (SCMA), where C users can share U<C time-frequency resources from the physical layer. Initially, the SCMA and orthogonal frequency division multiplexing (OFDM) integration is considered, defining a benchmark for the overall SER performance for the multiple access technique. Furthermore, this paper proposes the SCMA and generalized frequency division multiplexing (GFDM) integration. Since GFDM is a highly flexible non-orthogonal waveform that can mimic several other waveforms as corner cases, it is an interesting candidate for future wireless communication systems. This paper proposes two approaches for combining SCMA and GFDM. The first one combines a soft equalizer, called block expectation propagation (BEP), and a multi-user detection (MUD) scheme based on the sum-product algorithm (SPA). This approach achieves the best SER performance, but with the significant increment of the complexity at the receiver. In the second approach, BEP is integrated with a simplified MUD, which is an original contribution of this paper, aiming for reducing the receiver’s complexity at the cost of SER performance loss. The solutions proposed in this paper show that SCMA-GFDM can be an interesting solution for future mobile networks.

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Section: Proposed Scma-gfdm Integrationmentioning

confidence: 99%

Sparse code multiple access on the generalized frequency division multiplexing

Aquino

Mendes

2020

J Wireless Com Network

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“…Additionally, a large number of the devices will be sharing the same radio frequency spectrum [4]. These approximations indicate an increasing necessitation for reliable wireless connections for a huge number of devices [5]. Among the many solutions proposed to address the aforementioned challenges, cognitive radio network (CRN) is of vital importance [6].…”

Section: Introductionmentioning

confidence: 99%

HBLP: A Hybrid Underlay-Interweave Mode CRN for the Future 5G-Based Internet of Things

Khan

Abbas

et al. 2020

IEEE Access

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Enhancing spectrum utilization efficiency (SUE) to accommodate the multitude of 5G-based IoT devices within the available scarce spectrum has been a pivotal point of research in the current decade. Equipped with interweave and underlay modes, cognitive radio networks (CRNs) are envisioned to be the most promising technology for SUE enhancement. Since the 5G-based IoT is swiftly transforming into a heterogeneous network, hybrid underlay-interweave mode CRNs appear to be the optimal key for SUE enhancement. Besides enhancing SUE, ensuring fairness among secondary users (SUs) to equitably utilize the network's resources in the wake of primary users' (PUs) service emergence has been a critical problem in CRNs. Considering the importance of SUE enhancement, in this paper, we investigate two problems. Firstly, we precisely analyze, in a novel way, the performance of hybrid underlay-interweave mode of CRNs for SUE enhancement from the standpoint of SUs. For this purpose, we propose a hybrid underlay-interweave mode enabled CRN scheme and apply it on a legacy CRN. We model the proposed scheme with continuous time Markov chain and derive closed form expressions for various SUE-related quality of service parameters. The analysis is conducted under dynamic reservation of channels for interrupted users, considering the effects of multi-levels network traffic loads and varying channel failure rates. Secondly, we propose a multi-attribute based fairness driven algorithm for determination and interruption of SUs' services to ensure fairness among services in the network's resources utilization. We evaluate our proposed scheme under both perfect and imperfect spectrum sensing scenarios. The obtained results demonstrate that, as compared to the state-ofthe-art, the proposed scheme significantly enhances SUE while the proposed algorithm achieves a noticeable fairness among SUs' services. INDEX TERMS Cognitive radio networks, dynamic spectrum access, dynamic channel reservation, hybrid underlay-interweave mode, spectrum utilization efficiency.

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“…Among numerous non-stationary signals that arise in many radars [1] and communication [2,3], instantaneous frequency (IF) signals, for instance, linear frequency modulated (LFM) signals, have obvious time-frequency characteristics which are continuous and decided the location. Similar to the time-frequency signatures, the spatial signature of the signal source also includes significant information about the signal source [4].…”

Section: Introductionmentioning

confidence: 99%

Spatial time-frequency distribution of cross term-based direction-of-arrival estimation for weak non-stationary signal

Shao

Liu

et al. 2019

J Wireless Com Network

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In the radar array signal processing direction of arrival (DOA), the estimation of weak non-stationary signal is an important and difficult problem when both strong and weak signals are coexisting particularly because the weak non-stationary signals are often submerged in noise. In this paper, we proposed a novelty method to estimate the direction of arrival (DOA) of weak non-stationary signal in scenario for strong non-stationary interference signals and Gaussian white noise. The method utilizes spatial time-frequency distribution (STFD) of cross terms rather than suppressing cross terms in time-frequency analysis. The STFD of cross terms are introduced as an alternative matrix, which is similar to data covariance matrix in multiple signal classification (MUSIC), for the DOA estimation of a weak non-stationary signal. The cross-term amplitude of the strong and weak signals is usually above the noise and is easier to use than the auto-term of the weak signal. In the cross term, the information of the weak signal is included, and the auto-term of these weak signals is difficult to extract directly. The ability to incorporate the STFD of cross terms empowers information about a weak non-stationary signal for DOA estimation, leading to improved signal estimates for direction finding. The method based on the STFD of cross terms for DOA estimation of the weak non-stationary signal is revealed to outperform the time-frequency MUSIC and traditional MUSIC algorithm by simulation, respectively. This method has the advantages of the time-frequency direction finding method and also deals with the situation of weak signals. When the strong and weak signals exist at the same time and the two angles are similar, the cross-terms can be used to perform DOA estimation on the weak signal.

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Turbo Receiver Channel Estimation for GFDM-Based Cognitive Radio Networks

Cited by 37 publications

References 28 publications

Sparse code multiple access on the generalized frequency division multiplexing

Sparse code multiple access on the generalized frequency division multiplexing

HBLP: A Hybrid Underlay-Interweave Mode CRN for the Future 5G-Based Internet of Things

Spatial time-frequency distribution of cross term-based direction-of-arrival estimation for weak non-stationary signal

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