Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national laboratories have been developing proposals for the sixth-generation (6G), whose materialization is fraught with many fundamental challenges. To alleviate these challenges, a deep learning (DL)-enabled semantic communication (SemCom) has emerged as a promising 6G technology enabler, which embodies a paradigm shift that can change the status quo viewpoint that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meanings have been ignored. Since 6G is also critical for the materialization of major SemCom use cases, the paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration of 6G and SemCom. For this purpose, this comprehensive article provides the fundamentals of semantic information, semantic representation, and semantic entropy; details the state-of-the-art SemCom research landscape; presents the major SemCom trends and use cases; discusses current SemCom theories; exposes the fundamental and major challenges of SemCom; and offers future research directions for SemCom. We hope this article stimulates many lines of research on SemCom theories, algorithms, and implementation.

Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission – minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise. Such semantic noise’s impact can be alleviated using an interference-resistant and robust (IR2) SemCom design, though no such design exists yet. To stimulate fundamental research on IR2SemCom, the performance limits of a popular text SemCom system namedDeepSCare studied in the presence of (multi-interferer) RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of (multi-interferer) RFI gets very large. We also derive DeepSC’s practical limits and a lower bound on its outage probability under multi-interferer RFI, and propose a (generic) lifelong DL-based IR2SemCom system. We corroborate the derived limits with simulations and computer experiments, which also affirm the vulnerability of DeepSC to a wireless attack using RFI.

Radio frequency interference (RFI) is a manifestation of several interference instances that are caused by either intentional or unintentional interferes. Unless efficiently mitigated, RFI can evoke several system performance losses in various radio frequency (RF) operating systems. As a preliminary step of mitigating RFI, a number of RFI detectors have been proposed to date. These existing RFI detectors assume Gaussian noise with certain power despite the fact that it is impossible to know parameters to infinite precision. In this respect, the impact of noise power uncertainty on the existing RFI detectors needs to be investigated. Toward this end, this paper showcases the existence of performance limits-in a power-based RFI detector- dubbed INR walls. Interference-to-noise ratio (INR) walls are the maximum INRs below which an RFI detector cannot satisfy the target error probabilities, regardless of the number of intercepted samples. Simulations validate the derived INR walls which implicate the existence of fundamental RFI detection limits.

Radio frequency interference (RFI) is being common in both satellite and terrestrial communication systems. To detect it efficiently, this paper proposes, analyzes, and evaluates the utilization of eigenvalue-based blind RFI detector for single-input multiple-output systems. Simulations corroborate that the proposed detector performs as good as a generalized likelihood ratio test (GLRT) detector and a matched subspace detector, respectively, fed with the knowledge of the signal of interest (SOI) channel, and of the SOI and RFI channels even under sample starved settings. It is to be noted that such performance reveals the attractiveness of the eigenvalue-based RFI detector for real-time applications.

An F-test detector with a simple analytical false alarm threshold expression is considered an alternative to the blind detectors which exhibit complicated analytical expressions. However, the existing F-test requires the channel state information (CSI) as a prior knowledge implicating its sensitivity to CSI estimation error. In this paper, we present and evaluate the performance of a simple F-test based spectrum sensing technique that doesn't require the knowledge of the CSI for a multi-antenna cognitive radio. For this detector, exact and asymptotic analytical performance closed-form expressions are derived. Simulations assess the performance of the presented detector and validate the derived closed-form expressions.

For various sources of intentional or unintentional interference, radio frequency interference (RFI) is increasingly affecting many radio frequency operating systems. To mitigate RFI efficiently, it should, first, be detected robustly. On the other hand, energy detector (ED) is a very popular signal detector applied in various research sub-fields. On the contrary, it has not been deployed, to the best of our knowledge, for the detection of RFI to date. Accordingly, this paper investigates ED in the context of RFI detection and provides a theory for it. Monte-Carlo simulations corroborate that ED outperforms kurtosis detector (KD) even under the scenario that KD intercepts the received signal for a much longer interval. Furthermore, the performance of ED is assessed using real-world RFI contaminated data.

An F-test detector with a simple analytical false alarm threshold expression is considered an alternative to the blind detectors which exhibit complicated analytical expressions. Proposed for a single-input multiple-output (SIMO) systems, the existing F-test requires the channel state information (CSI) as a prior knowledge. On the contrary, the CSI requirement renders a sensitivity to a CSI estimation error and multiple-input multiple-output (MIMO) systems guarantee better array gain, spatial diversity gain, spatial multiplexing gain, and interference reduction than SIMO systems. Accordingly, we present and evaluate the performance of a simple F-test based spectrum sensing technique that doesn't require the knowledge of the CSI for the MIMO cognitive radio networks. For this detector, exact and asymptotic analytical performance closed-form expressions are derived. Simulations assess the performance of the presented detector and validate the derived closed-form expressions.

An F-test detector with a simple analytical false alarm threshold expression is considered an alternative to the blind detectors which exhibit complicated analytical expressions. However, the existing F-test requires the channel state information (CSI) as a prior knowledge and is known to be sensitive to CSI estimation errors. In this paper, we present and evaluate simple F-test-based spectrum sensing techniques that do not require the knowledge of CSI for multi-antenna cognitive radios. Exact and asymptotic analytical performance closed-form expressions are derived for the presented detectors. Simulations assess the performance of the presented detectors and validate the derived closed-form expressions. For an additive noise exhibiting the same variance across multiple-antenna frontends, simulations also corroborate that the presented detectors are constant false alarm rate detectors which are robust against noise uncertainty.

As radio frequency interference (RFI) affects many systems operating radio frequencies, RFI detection is essential for excising such RFI efficiently. For this reason, here we investigate an energy-based RFI detector for wireless communication systems suffering from RFI. For this detector, its average probability of RFI detection is studied and approximated, and asymptotic closed-form expressions are derived. Besides, an exact closed-form expression for its average probability of false alarm is derived. Monte-Carlo simulations validate the derived analytical expressions and corroborate that the investigated energy detector (ED) outperforms a kurtosis detector (KD)-even under the scenario that KD intercepts the received signal for a longer interval-and a generalized likelihood ratio test detector (GLRT). At last, the performance of ED is also assessed using real-world RFI contaminated data.

Radio frequency interference (RFI) is causing performance loss in microwave radiometry, radio astronomy, and satellite communications. As the number of interferers increases, the performance loss gets more severe and RFI excision becomes more difficult. In this regard, this paper introduces the multilinear algebra framework to the multi-interferer RFI (MI-RFI) excision research by proposing a multi-linear subspace estimation and projection (MLSEP) algorithm for single-input multiple-output (SIMO) systems suffering from MI-RFI. Having employed smoothed observation windows, a smoothed MLSEP (s-MLSEP) algorithm, which enhances MLSEP, is also proposed. MLSEP and s-MLSEP require the knowledge of the number of interferers and their respective channel order. Accordingly, a novel smoothed matrix-based joint number of interferers and channel order enumerator is proposed. Performance analyses corroborate that both MLSEP and s-MLSEP can excise all interferers when the perturbations get infinitesimally small. For such perturbations, the analyses also attest that s-MLSEP exhibit a faster convergence to a zero excision error than MLSEP which, in turn converges faster than a subspace projection algorithm. Despite its slight complexity, simulations and performance assessment on real-world data demonstrate that MLSEP outperforms projection-based RFI excision algorithms. Simulations also corroborate that s-MLSEP outperforms MLSEP as the smoothing factor gets smaller.