In the last years, it has been shown that nonlinear (NL) orthogonal frequency division multiplexing (OFDM) can outperform linear OFDM because the nonlinear distortion has useful information about the transmitted signals. However, only a maximum likelihood (ML) receiver can exploit this information. Despite its potential, the optimal ML receiver for NL OFDM is highly complex, and its performance is difficult to simulate. Moreover, although some theoretical performance bounds exist for OFDM transmissions involving many subcarriers and high signal-to-noise ratio (SNR), the behavior of NL OFDM under more practical SNR conditions remains insufficiently explored. This paper focuses on optimal detection methods for NL OFDM systems. We present a comprehensive analysis of how the distortion received on different subcarriers contributes to the signal of a specific subcarrier and derive a performance bound for the ML detection of NL OFDM that is applicable across a broad range of SNR values. Furthermore, we propose a practical iterative decision-directed receiver that achieves significant performance improvements over linear OFDM in both uncoded and coded setups.11This work supported by Fundação para a Ciência e Tecnologia and Instituto de Telecomunicações under the project UIDB/50008/2020, DOI:10.54499/UIDB/50008/2020, and Universidade Lusófona.

Nonlinearities are usually something to be avoided in orthogonal frequency division multiplexing (OFDM) transceivers. However, it has been demonstrated that nonlinear (NL) OFDM can outperform linear OFDM, since NL distortion has useful information on the transmitted signals that can be exploited by the optimum maximum likelihood receiver. The optimum ML receiver for NL OFDM is too complex and it is not easy to obtain its performance, although some theoretical performance bounds are available for scenarios with a large number of subcarriers and signal-to-noise ratio (SNR). However, the optimum performance of NL OFDM in small-to-moderate SNR values is still not well studied. In this paper, we consider the optimum detection of NL OFDM schemes, where we take advantage of the NL distortion to improve performance. We present a detailed study of the contributions to the signal associated with a given subcarrier from the distortion received at the other subcarriers. This allowed us to obtain a bound on the maximum likelihood (ML) performance of NL OFDM that is valid over a wide range of SNR values subcarriers and demonstrates that the performance gains observed in the asymptotic SNR region also take place for lower SNRs. Additionally, we propose a practical, iterative decision-directed receiver that exhibits large performance gains over linear OFDM in both uncoded and coded scenarios.

In the last years, it has been shown that nonlinear (NL) orthogonal frequency division multiplexing (OFDM) schemes can outperform linear OFDM. This can be explained by the fact that nonlinear distortion has useful information on the transmitted signals that can be employed for detection purposes. Although the performance gains of NL OFDM can be theoretically evaluated in the asymptotic signal-to-noise ratio (SNR) region and when the number of sub carriers is large, the performance in small-to-moderate SNR values is still not clear. Moreover, receivers that try to exploit those potential gains involve very high complexity. In this paper, we present a bound on the optimum performance of NL OFDM valid for a wide range of SNR values. Moreover, a novel receiver structure that approximates the optimum performance is proposed1,

Extremely large antenna arrays (ELAAs) are being envisioned for sixth-generation (6G) to increase spatial selectivity and achieve higher capacities. The use of larger arrays combined with higher carrier frequencies increases the near-field region, where the channel model must account for the curvature of the electromagnetic waves, and the plane wave regime is inaccurate. When conventional matched-filtering (MF) beamforming is de-signed for near-field, a beam-focusing effect is observed around the focal point. This can be used to mitigate the impact of a malicious user and increase the physical layer security (PLS). In this paper, we propose an optimized beamforming design for uplink near-field communications that accounts not only for the positioning of the legitimate user but also for the position of the jammer. It is shown that the jamming rejection capabilities of the proposed beamforming are superior to that of conventional MF.1

Multiple Input Multiple Output (MIMO) architectures are now widely adopted in wireless systems, providing substantial capacity benefits by harnessing spatial diversity and multiplexing. Nonetheless, the large peak-to-average power ratio (PAPR) associated with common pre-processing techniques, like singular value decomposition (SVD), increases the system's susceptibility to nonlinear distortion. Conventional receiver designs that mitigate this distortion often neglect the fact that it has useful information on the transmitted data. Maximum likelihood (ML) detection offers the capability to take advantage of the nonlinear distortion, but its inherent complexity is prohibitively high. This paper introduces a new MIMO receiver design aimed at exploiting the diversity introduced by the transmitter nonlinearities. It also provides an approximate bound on the achievable ML bit error rate (BER) performance. Our results indicate that the proposed receiver can have a performance close to the ML receiver with just a few iterations.1