Sensing for Free: Learn to Localize More Sources Than Antennas Without Pilots

Integrated sensing and communication (ISAC) represents a key paradigm for future wireless networks. However, existing approaches often require waveform modifications, dedicated pilots, or additional overhead that complicates standards integration. We propose “sensing for free”—performing multi-source localization without pilots by reusing random and unknown uplink data symbols, where sensing happens simultaneously with data transmission, making it directly compatible with the 3GPP 5G NR and 6G specifications. With the ever-increasing number of devices in dense 6G networks, this approach becomes particularly compelling when combined with sparse arrays, which can localize a much larger number of sources compared to uniform arrays through the enlarged virtual array. However, existing pilot-free multi-source localization algorithms for sparse arrays have numerous drawbacks. They mostly first reconstruct an extended covariance matrix and then apply subspace methods, which incur prohibitive cubic complexity while being limited to second-order statistics. Performance degrades under non-Gaussian modulated data symbols in cellular wireless networks as the higher-order statistics that could further enhance the localization capability remain unexploited. We address these challenges with an attention-only transformer that directly processes raw signal snapshots for grid-less end-to-end direction-of-arrival (DOA) estimation. The model efficiently captures higher-order statistics while being permutation-invariant and adaptive to varying numbers of snapshots. Our algorithm greatly outperforms state-of-the-art artificial intelligence (AI)-based benchmarks with over 30× reduction in parameters and runtime, and enjoys excellent generalization under practical mismatches. In addition, it can effectively handle multipath propagation and mixed modulation types. Beyond localization, our algorithm can also enhance multi-user MIMO beam training through angular reciprocity. The estimated DOAs in the uplink data transmission stage can significantly prune downlink beam sweeping candidates and enhance system throughput via sensing-assisted beam management. Overall, this work demonstrates how reusing existing random data payloads for sensing can enhance both multi-source localization and beam management, two key AI-for-communication use cases in 3GPP efforts towards 6G.