telecom engineers

Abstract: Academic research in wireless communications has no tradition of benchmarks so far. Remarkable progress in benchmark-oriented fields like machine learning suggests that the adoption of benchmarks could benefit our field. In the absence of benchmarks, however, a scientific debt has accumulated: since the academic community has not canonized specific problem setups, methods that address essentially the same problem were often developed for slightly different setups and lack a common framework for comparison. Any attempt to establish a new benchmark is faced with the challenge of resolving this debt. Recent advances in agentic artificial intelligence (AI) create an opportunity to do so by leveraging AI-assisted coding. This makes it possible to implement the state-of-the art at scale and to adapt it to a common framework for comparison. We present Sionna Bench to initiate benchmarks in academic wireless communications research. As a pilot study, we use the problem of MIMO-OFDM channel estimation. At the time of writing, Sionna Bench's channel estimation benchmark features twenty channel estimation methods that were implemented from ten different papers. The ability to compare methods against the state-of-the-art will be crucial for navigating the research landscape in an era where AI-generated methods coexist with human-developed algorithms.

Abstract: Sparse Code Multiple Access (SCMA) is a promising non-orthogonal multiplexing technique for enhancing connectivity and spectral efficiency in next-generation communication networks. This paper proposes near-optimal uplink SCMA codebook designs based on a novel deep neural network (DNN)-assisted framework. The DNN is trained in an unsupervised manner, with a loss function derived from the structural characteristics of the generated codebooks, thereby eliminating the need for labeled training data. Based on error-probability analysis, Rician-factor-dependent design criteria are derived for uplink Rician fading and used to formulate the loss function. The resulting criteria naturally encompass AWGN and Rayleigh fading as limiting cases, providing a unified framework for codebook design. Moreover, upper and lower bounds on the average mutual information (AMI) are derived, providing an information-theoretic interpretation of the PEP-based criterion and establishing its asymptotic consistency with the AMI-lower-bound-based criterion in the high-SNR regime. The analysis further reveals how the dominant design factors evolve as the channel transitions from Rayleigh fading to AWGN. Numerical results show that the proposed near-optimal codebooks achieve significant performance gains over representative benchmark schemes across a wide range of Rician factors and exhibit excellent error-rate performance in the error-floor region, making them particularly attractive for ultra-reliable applications.