No abstract

In this paper, we investigate the sequence estimation problem of binary and quadrature phase shift keying faster-than-Nyquist (FTN) signaling and propose two novel low-complexity sequence estimation techniques based on concepts of successive interference cancellation. To the best of our knowledge, this is the first approach in the literature to detect FTN signaling on a symbol-by-symbol basis. In particular, based on the structure of the self-interference inherited in FTN signaling, we first find the operating region boundary-defined by the root-raised cosine (rRC) pulse shape, its roll-off factor, and the time acceleration parameter of the FTN signaling-where perfect estimation of the transmit data symbols on a symbol-by-symbol basis is guaranteed, assuming noise-free transmission. For noisy transmission, we then propose a novel low-complexity technique that works within the operating region and is capable of estimating the transmit data symbols on a symbol-by-symbol basis. To reduce the error propagation of the proposed successive symbol-by-symbol sequence estimator (SSSSE), we propose a successive symbol-by-symbol with go-back-K sequence estimator (SSSgbKSE) that goes back to reestimate up to K symbols, and subsequently improves the estimation accuracy of the current data symbol. Simulation results show that the proposed sequence estimation techniques perform well for low intersymbol interference (ISI) scenarios and can significantly increase the data rate and spectral efficiency. Additionally, results reveal that choosing the value of K as low as 2 or 3 data symbols is sufficient to significantly improve the bit-error-rate performance. Results also show that the performance of the proposed SSSgbKSE, with K = 1 or 2, surpasses the performance of the lowest complexity equalizers reported in the literature, with reduced computational complexity. Index TermsFaster-than-Nyquist (FTN) signaling, intersymbol interference (ISI), Mazo limit, self-interference, sequence estimation, successive interference cancellation

This work addresses the sum-rate maximization for a downlink non-orthogonal multiple access (NOMA) system in the presence of imperfect successive interference cancellation (SIC). We assume that the NOMA users adopt improper Gaussian signalling (IGS), and hence, derive new expressions of their rates under residual interference from imperfect SIC. We optimize the circularity coefficient of the IGS-based NOMA system to maximize its sum-rate subject to quality-of-service (QoS) requirements. Compared to the NOMA with proper Gaussian signaling (PGS), simulation results show that the IGS-based NOMA system demonstrates considerable sum-rate performance gain under imperfect SIC.

No abstract

In this paper, we propose a novel algorithm to optimize the energy efficiency (EE) of orthogonal frequency-division multiplexing (OFDM)-based cognitive radio systems under channel uncertainties. We formulate an optimization problem that guarantees a minimum required rate and a specified power budget for the secondary user (SU) while restricting the interference to primary users (PUs) in a statistical manner. The optimization problem is nonconvex, and it is transformed to an equivalent problem using the concept of fractional programming. Unlike all related works in the literature, we consider the effect of imperfect channel-state information (CSI) on the links between the SU transmitter and receiver pairs, and we additionally consider the effect of limited sensing capabilities of the SU. Since the interference constraints are met statistically, the SU transmitter does not require perfect CSI feedback from the PUs' receivers. Simulation results show that the EE deteriorates as the channel estimation error increases. Comparisons with relevant works from the literature show that the interference thresholds at the PUs' receivers can be severely exceeded and that the EE is slightly deteriorated if the SU does not account for spectrum sensing errors.Index Terms-Cognitive radio (CR), energy efficiency (EE), imperfect channel-state information (CSI) and sensing, orthogonal frequencydivision multiplexing (OFDM) systems, power loading. 1 The EE can be defined as the number of bits per unit energy. However, it is common to define it as the total energy consumed to deliver 1 bit; see [5]-[7]. 0018-9545

No abstract

No abstract

No abstract