Relying on the method of characteristic function (CF) and residue calculation, we obtained an exact and closed-form error floor for downlink multicarrier code-division multiple access (MC-CDMA) with maximal ratio combining (MRC) in correlated Nakagami (1960) fading channels. Analysis results show that orthogonal spreading sequences (Walsh and orthogonal Gold sequences) perform better than non-orthogonal sequences (Gold sequences) and Walsh codes are the best. We also find that downlink MC-CDMA can benefit from the correlation between subcarriers.
This paper provides a novel low complexity and provably convergent algorithm to find the sum capacity for vector Gaussian broadcast channels. We formulate the problem in the context of a MIMO-OFDM system and discuss the simplifications provided by the block diagonal channel structure. The algorithm also provides the optimum set of covariance matrices for the dual multiple access channel, from which the transmit covariance matrices for the broadcast channel are easy to compute. The key idea of the algorithm is to iteratively maximize the sum capacity for randomly selected pairs of users whilst considering the other users' signals as noise. The proposed algorithm can be considered dual to the iterative water filling (IWF) algorithm used to maximize the sum capacity for the multiple access channel with individual power constraints, but unlike the recently proposed sum power constraint IWF, our proposed algorithm has lower complexity and does not require additional precautions to ensure the convergence. We have proved analytically that the proposed algorithm is convergent in probability and the computer simulations clearly show that the proposed algorithm converges faster than other known algorithms. Index Terms—Broadcast channels, MIMO systems, orthogonal frequency division multiplexing, iterative water-filling.
Generative AI has received significant attention among a spectrum of diverse industrial and academic domains, thanks to the magnificent results achieved from deep generative models such as generative pre-trained transformers (GPT) and diffusion models. In this paper, we explore the applications of denoising diffusion probabilistic models (DDPMs) in wireless communication systems under practical assumptions such as hardware impairments (HWI), low-SNR regime, and quantization error. Diffusion models are a new class of state-of-the-art generative models that have already showcased notable success with some of the popular examples by OpenAI1 and Google Brain2. The intuition behind DDPM is to decompose the data generation process over small ``denoising'' steps. Inspired by this, we propose using denoising diffusion model-based receiver for a practical wireless communication scheme, while providing network resilience in low-SNR regimes, non-Gaussian noise, different HWI levels, and quantization error. We evaluate the reconstruction performance of our scheme in terms of mean-squared error (MSE) metric. Our results show that more than 25 dB improvement in MSE is achieved compared to deep neural network (DNN)-based receivers. We also highlight robust out-of-distribution performance under non-Gaussian noise.
In this paper, a novel simple generalized Chase algorithm (GCA) based multiuser detector (MUD) is proposed for MC-CDMA systems. After getting the initial solution from the front-end frequency domain linear minimum mean-squared error (LMMSE) receiver, different Chase-type algorithms are adopted to extend the potential solution list. Among the list, the candidate which can achieve the minimum distance to the received signal, is selected as the final solution. Both bit- and symbol-level receivers for high level modulation schemes are considered. Compared to the conventional MMSE receiver, the proposed receiver can provide superior performance with little increase in complexity. Moreover, the robustness of the proposed receiver is studied by incorporating a semi-blind subspace based channel estimation.
