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Register a new userLow-Complexity OFDM Spectral Precoding
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Added by
Shashi Kant V
Publication date
2019
fields
Electronic Engineering
and research's language is
English
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This paper proposes a new large-scale mask-compliant spectral precoder (LS-MSP) for orthogonal frequency division multiplexing systems. In this paper, we first consider a previously proposed mask-compliant spectral precoding scheme that utilizes a generic convex optimization solver which suffers from high computational complexity, notably in large-scale systems. To mitigate the complexity of computing the LS-MSP, we propose a divide-and-conquer approach that breaks the original problem into smaller rank 1 quadratic-constraint problems and each small problem yields closed-form solution. Based on these solutions, we develop three specialized first-order low-complexity algorithms, based on 1) projection on convex sets and 2) the alternating direction method of multipliers. We also develop an algorithm that capitalizes on the closed-form solutions for the rank 1 quadratic constraints, which is referred to as 3) semi-analytical spectral precoding. Numerical results show that the proposed LS-MSP techniques outperform previously proposed techniques in terms of the computational burden while complying with the spectrum mask. The results also indicate that 3) typically needs 3 iterations to achieve similar results as 1) and 2) at the expense of a slightly increased computational complexity.
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Spectral precoding is a promising technique to suppress out-of-band emissions and comply with leakage constraints over adjacent frequency channels and with mask requirements on the unwanted emissions. However, spectral precoding may distort the original data vector, which is formally expressed as the error vector magnitude (EVM) between the precoded and original data vectors. Notably, EVM has a deleterious impact on the performance of multiple-input multiple-output orthogonal frequency division multiplexing-based systems. In this paper we propose a novel spectral precoding approach which constrains the EVM while complying with the mask requirements. We first formulate and solve the EVM-unconstrained mask-compliant spectral precoding problem, which serves as a springboard to the design of two EVM-constrained spectral precoding schemes. The first scheme takes into account a wideband EVM-constraint which limits the average in-band distortion. The second scheme takes into account frequency-selective EVM-constraints, and consequently, limits the signal distortion at the subcarrier level. Numerical examples illustrate that both proposed schemes outperform previously developed schemes in terms of important performance indicators such as block error rate and system-wide throughput while complying with spectral mask and EVM constraints.
OFDM sensing is gaining increasing popularity in wideband radar applications as well as in joint communication and radar/radio sensing (JCAS). As JCAS will potentially be integrated into future mobile networks where OFDM is crucial, OFDM sensing is envisioned to be ubiquitously deployed. A fast Fourier transform (FFT) based OFDM sensing (FOS) method was proposed a decade ago and has been regarded as a de facto standard given its simplicity. In this article, we introduce an easy trick -- a pre-processing on target echo -- to further reduce the computational complexity of FOS without degrading key sensing performance. Underlying the trick is a newly disclosed feature of the target echo in OFDM sensing which, to the best of our knowledge, has not been effectively exploited yet.
Large-scale antenna (LSA) has gained a lot of attention recently since it can significantly improve the performance of wireless systems. Similar to multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM, LSA can be also combined with OFDM to deal with frequency selectivity in wireless channels. However, such combination suffers from substantially increased complexity proportional to the number of antennas in LSA systems. For the conventional implementation of LSA-OFDM, the number of inverse fast Fourier transforms (IFFTs) increases with the antenna number since each antenna requires an IFFT for OFDM modulation. Furthermore, zero-forcing (ZF) precoding is required in LSA systems to support more users, and the required matrix inversion leads to a huge computational burden. In this paper, we propose a low-complexity recursive convolutional precoding to address the issues above. The traditional ZF precoding can be implemented through the recursive convolutional precoding in the time domain so that only one IFFT is required for each user and the matrix inversion can be also avoided. Simulation results show that the proposed approach can achieve the same performance as that of ZF but with much lower complexity.
Millimeter-wave and terahertz technologies have been attracting attention from the wireless research community since they can offer large underutilized bandwidths which can enable the support of ultra-high-speed connections in future wireless communication systems. While the high signal attenuation occurring at these frequencies requires the adoption of very large (or the so-called ultra-massive) antenna arrays, in order to accomplish low complexity and low power consumption, hybrid analog/digital designs must be adopted. In this paper we present a hybrid design algorithm suitable for both mmWave and THz multiuser multiple-input multiple-output (MIMO) systems, which comprises separate computation steps for the digital precoder, analog precoder and multiuser interference mitigation. The design can also incorporate different analog architectures such as phase shifters, switches and inverters, antenna selection and so on. Furthermore, it is also applicable for different structures namely, fully connected, arrays of subarrays (AoSA) and dynamic arrays of subarrays (DAoSA), making it suitable for the support of ultra-massive MIMO (UM-MIMO) in severely hardware constrained THz systems. We will show that, by using the proposed approach, it is possible to achieve good trade-offs between spectral efficiency and simplified implementation, even as the number of users and data streams increases.)
Linear precoding techniques can achieve near- optimal capacity due to the special channel property in down- link massive MIMO systems, but involve high complexity since complicated matrix inversion of large size is required. In this paper, we propose a low-complexity linear precoding scheme based on the Gauss-Seidel (GS) method. The proposed scheme can achieve the capacity-approaching performance of the classical linear precoding schemes in an iterative way without complicated matrix inversion, which can reduce the overall complexity by one order of magnitude. The performance guarantee of the proposed GS-based precoding is analyzed from the following three aspects. At first, we prove that GS-based precoding satisfies the transmit power constraint. Then, we prove that GS-based precoding enjoys a faster convergence rate than the recently proposed Neumann-based precoding. At last, the convergence rate achieved by GS-based precoding is quantified, which reveals that GS-based precoding converges faster with the increasing number of BS antennas. To further accelerate the convergence rate and reduce the complexity, we propose a zone-based initial solution to GS-based precoding, which is much closer to the final solution than the traditional initial solution. Simulation results demonstrate that the proposed scheme outperforms Neumann- based precoding, and achieves the exact capacity-approaching performance of the classical linear precoding schemes with only a small number of iterations both in Rayleigh fading channels and spatially correlated channels.
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