While extremely large-scale antenna arrays (XL-arrays) hold immense promise for next-generation wireless networks, the paradigm shift encompassing new near-field communications renders conventional discrete Fourier transform (DFT)-based Type-I codebooks inefficient. Meanwhile, existing near-field alternatives usually impose prohibitively high storage and/or feedback burdens. To tackle these challenges, we propose in this letter a unified codebook applicable to both far-field and near-field XL-array systems. Our key idea is to express the near-field channel steering vector in an equivalent form, which is a Hadamard product of an angular vector and a curvature-related vector. This new representation allows direct reuse of the legacy Type-I codebook for angle quantization, while only a lightweight auxiliary codebook is newly added for additional curvature quantization. Subsequently, we optimize the auxiliary curvature codebook by maximizing the average beamforming gain given the information of user distribution. By leveraging the local monotonicity of the beamforming gain with respect to curvature mismatch, the optimization problem is further reduced to a quantization-error minimization problem, for which suboptimal curvature codewords are obtained. Numerical results demonstrate that the proposed unified codebook achieves near-optimal beamforming performance that approaches its upper bound based on perfect channel state information (CSI), while significantly reducing the required feedback overhead as compared to various benchmark schemes.

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