Accurate cross-band channel prediction is essential for 6G networks, particularly in the upper mid-band (FR3, 7-24 GHz), where penetration loss and blockage are severe. Although ray tracing (RT) provides high-fidelity modeling, it remains computationally intensive, and high-frequency data acquisition is costly. To address these challenges, we propose CIR-UNext, a deep learning framework designed to predict 7 GHz channel impulse responses (CIRs) by leveraging abundant 3.5 GHz CIRs. The framework integrates an RT-based dataset pipeline with attention U-Net (AU-Net) variants for gain and phase prediction. The proposed AU-Net-Aux model achieves a median gain error of 0.58 dB and a phase prediction error of 0.27 rad on unseen complex environments. Furthermore, we extend CIR-UNext into a foundation model, Channel2ComMap, for throughput prediction in MIMO-OFDM systems, demonstrating superior performance compared with existing approaches. Overall, CIR-UNext provides an efficient and scalable solution for cross-band prediction, enabling applications such as localization, beam management, digital twins, and intelligent resource allocation in 6G networks.

Sixth-generation (6G) networks are envisioned to achieve full-band cognition by jointly utilizing spectrum resources from Frequency Range 1 (FR1) to Frequency Range 3 (FR3, 7–24 GHz). Realizing this vision faces two challenges. First, physics-based ray tracing (RT), the standard tool for network planning and coverage modeling, becomes computationally prohibitive for multi-band and multi-directional analysis over large areas. Second, current 5G systems rely on inter-frequency measurement gaps for carrier aggregation and beam management, which reduce throughput, increase latency, and scale poorly as bands and beams proliferate. These limitations motivate a data-driven approach to infer high-frequency characteristics from low-frequency observations. This work proposes CommUNext, a unified deep learning framework for cross-band, multi-directional signal strength (SS) prediction. The framework leverages low-frequency coverage data and crowd-aided partial measurements at the target band to generate high-fidelity FR3 predictions. Two complementary architectures are introduced: Full CommUNext, which substitutes costly RT simulations for large-scale offline modeling, and Partial CommUNext, which reconstructs incomplete low-frequency maps to mitigate measurement gaps in real-time operation. Experimental results show that CommUNext delivers accurate and robust high-frequency SS prediction even with sparse supervision, substantially reducing both simulation and measurement overhead.

Spatial domain (SD) beam prediction to derive the received powers of all beams from a subset of beam measurements can significantly reduce the reference signal overhead and power consumption, and is currently under 3GPP standardization for 6G. In this paper, we identify the beam codebook variation problem, which is crucial for the field deployment of commercial beam prediction algorithm. To be more specific, note that the codebook for 5G mmWave beamforming has not been standardized, and the user-equipment (UE) beam prediction must accommodate different beam codebooks from different base stations (BSs), which are typically unknown to UE and lead to uncertain SD correlation across beams. To solve this problem, we develop a new downlink beam prediction approach universally applicable to all the BSs’ beam codebooks without the knowledge of the codebooks at the UE side by leveraging the post-processing of the prediction report in the BS. We first develop a mathematical model aligned with the ongoing 3GPP standardization and then derive the low-complexity universal beam prediction scheme. Our simulation based on a 3GPP channel model shows that the proposed universal approach achieves a prediction error probability 100 times lower than that of the conventional and machine learning model based algorithms, and matches the prediction performance achieved with the full beam codebook knowledge on the UE side.

With scaling of process technologies and increase in process variations, embedded memories will be inherently unreliable. In this paper, we propose redundancy-free adaptive error-correcting codes for the noisy min-sum decoder subject to memory errors. We consider the popular memory error model with a binary symmetric channel. We first revisit the density evolution analysis proposed by Balatsoukas-Stimming and Burg for the noisy min-sum decoder. Two important consequences of the density evolution analysis are: (a) after a large enough number of iterations, most of the messages have large magnitudes, and the residual errors are mostly from the sign bit flips due to memory failures, and (b) errors in the least significant bits in large-magnitude messages have a negligible effect on the residual error rate. We thus propose adaptive error-correcting codes to protect sign bits by least significant bits when the messages have large magnitudes. The proposed coding scheme does not require any further data storage (i.e., this code is redundancy-free). Density evolution analysis for the noisy min-sum decoder implementing the proposed coding scheme is derived, demonstrating that the proposed decoder achieves a residual error rate that is on the order of the square of the residual error rate achieved by the nominal min-sum decoder. Simulation results on the finite block length LDPC code also agree with this density evolution analysis.

The wide recognition that emerging nano-devices will be inherently unreliable motivates the evaluation of information processing algorithms running on noisy hardware as well as the design of robust schemes for reliable performance against hardware errors of varied characteristics. In this paper, we investigate the performance of a popular statistical inference algorithm, belief propagation (BP) on probabilistic graphical models, implemented on noisy hardware, and we propose two robust implementations of the BP algorithm targeting different computation noise distributions. We assume that the BP messages are subject to zero-mean transient additive computation noise. We focus on graphical models satisfying the contraction mapping condition that guarantees the convergence of the noise-free BP. We first upper bound the distances between the noisy BP messages and the fixed point of (noise-free) BP as a function of the iteration number. Next, we propose two implementations of BP, namely, censoring BP and averaging BP, that are robust to computation noise. Censoring BP rejects incorrect computations to keep the algorithm on the right track to convergence, while averaging BP takes the average of the messages in all iterations up to date to mitigate the effects of computation noise. Censoring BP works effectively when, with high probability, the computation noise is exactly zero, and averaging BP, although having a slightly larger overhead, works effectively for general zero-mean computation noise distributions. Sufficient conditions on the convergence of censoring BP and averaging BP are derived. Simulations on the Ising model demonstrate that the two proposed implementations successfully converge to the fixed point achieved by noise-free BP. Additionally, we apply averaging BP to a BP-based image denoising algorithm and as a BP decoder for LDPC codes. In the image denoising application, averaging BP successfully denoises an image even when nominal BP fails to do so in the presence of computation noise. In the BP LDPC decoder application, the power of averaging BP is manifested by the reduction in the residual error rates compared with the nominal BP decoder.

With scaling of process technologies and increase in process variations, embedded memories will be inherently unreliable. Approximate computing is a new class of techniques that relax the accuracy requirement of computing systems. In this paper, we present the Adaptive Coding for approximate Computing (ACOCO) framework, which provides us with an analysis-guided design methodology to develop adaptive codes for different computations on the data read from faulty memories. In ACOCO, we first compress the data by introducing distortion in the source encoder, and then add redundant bits to protect the data against memory errors in the channel encoder. We are thus able to protect the data against memory errors without additional memory overhead so that the coded data have the same bit-length as the uncoded data. We design the source encoder by first specifying a cost function measuring the effect of the data compression on the system output, and then design the source code according to this cost function. We develop adaptive codes for two types of systems under ACOCO. The first type of systems we consider, which includes many machine learning and graph-based inference systems, is the systems dominated by product operations. We evaluate the cost function statistics for the proposed adaptive codes, and demonstrate its effectiveness via two application examples: max-product image denoising and naïve Bayesian classification. Next, we consider another type of systems: iterative decoders with min operation and sign-bit decision, which are widely applied in wireless communication systems. We develop an adaptive coding scheme for the min-sum decoder subject to memory errors. A density evolution analysis and simulations on finite length codes both demonstrate that the decoder with our adaptive code achieves a residual error rate that is on the order of the square of the residual error rate achieved by the nominal min-sum decoder.

With the wide recognition that modern nanoscale devices will be error-prone, characterization of reliability of information processing systems built out of unreliable components has become an important topic. In this paper, we analyze the performance of orthogonal matching pursuit (OMP), a popular sparse recovery algorithm, running on faulty circuits. We identify sufficient conditions for correct recovery of the signal support and express these conditions in terms of the relationship among signal magnitudes, sparsity, and the mutual incoherence of the measurement matrix. We study both the effects of additive errors in arithmetic computations and logical errors in comparators. We find that the additive errors in the OMP computations have an impact on the overall performance comparable to that of the additive noise in the input measurements. We also show that parallel structures are more robust to logical errors than serial structures in the implementation of a noisy arg max operation, and thus lead to a better OMP performance.

This paper studies the performance of a noisy Gallager B decoder for regular LDPC codes. We assume that the noisy decoder is subject to both transient processor errors and permanent memory errors. We permit different error rates at different functional components. In addition, for the sake of generality, we allow asymmetry in the permanent error rates of component outputs, and thus we model error propagation in the decoder via a suitable asymmetric channel. We then develop a density evolution-type analysis on this asymmetric channel. The recursive expression for the bit error probability is derived as a function of the code parameters (node degrees), codeword weight, transmission error rate, and the error rates of the permanent and the transient errors. Based on this analysis, we then derive the residual error of the Gallager B decoder for the regime where the transmission error rate and the processing error rates are small. In this regime, we further observe that the residual error rate can be well approximated by a suitable combination of the transient error rate and the permanent error rate at variable nodes, provided that the check node degree is large enough. Based on this insight, we then propose and analyze a scheme for detecting permanent errors and correcting detected residual errors. The scheme exploits the parity check equations of the code and reuses the existing hardware to locate permanent errors in memory blocks. Performance analysis and simulation results show that, with high probability, the detection scheme discovers correct locations of permanent memory errors, while, with low probability, it mislabels the functional memory as being defective. The proposed error detection-and-correction scheme can be implemented in-circuit and is useful in combating failures arising from aging.

It is widely recognized that emerging hardware technologies will be inherently unreliable. In this paper, we study the performance of finite-alphabet iterative decoders when implemented on noisy hardware built out of unreliable components. We derive a recursive expression for the error probability in terms of both the transmission noise and processing errors. We allow different components of the decoding algorithm associated with certain computational units (i.e., bit and check nodes of varying degrees in the underlying graph) to be implemented using a collection of processors with varying levels of processing error rates. Performance analysis and optimal resource allocation of a noisy Gallager E decoder is presented as an application example of our general derivation. Simulations demonstrate that the implementation of a noisy iterative decoder according to the proposed analysis-guided optimal resource allocation outperforms implementations based on uninformed resource allocation under the common resource budget.

In this paper, the performance of a noisy Gallager B decoder used to decode regular LDPC codes is studied. We assume that the noisy decoder is subject to both transient processor errors and permanent memory errors. Due to the asymmetric nature of permanent errors, we model error propagation in the decoder via a suitable asymmetric channel. We then develop a density evolution type analysis on this asymmetric channel. The recursive expression for the bit error probability is derived as a function of the code parameters (node degrees), codeword weight, transmission error rate and the error rates of the permanent and the transient errors. Based on this analysis, we then derive the residual error of the Gallager B decoder for the regime where the transmission error rate and the processing error rates are small. In this regime, we further observe that the residual error can be well approximated by the sum of suitably combined transient errors and permanent errors, provided that the check node degree is large enough. Based on this insight we then propose and analyze a simple scheme for detecting permanent errors. The scheme exploits the parity check equations of the code itself and reuses the existing hardware to locate permanent errors in memory blocks. With high probability, the detection scheme discovers correct locations of permanent memory errors, while, with low probability, it mislabels the functional memory as being defective.

Most of the modern intelligent mobile devices such as intelligent vehicles or robots rely on sensor fusion to perceive the environment and make the decision on direction by traditional maximum likelihood (ML) criterion and possible direct decision feedback. To optimally fuse the sensor observation, we propose a novel approach called decision-prediction fusion (DP fusion). It further includes the previous decision as well as the previous state in the state transition concept of Kalman filter to derive the a prior probability of the current state. Thus traditional sensor ML fusion is converted to maximum a posteriori probability (MAP) detection by this approach. In this paper, we investigate service/rescue robot navigation problem to illustrate DP fusion theory and its application. The robot fuses sensors' observation to decide the direction of its destination. To derive the a prior probability for DP fusion, we establish the relationship of the current direction of destination with the previous decision: the angle of destination's current direction is nearly the same as the angle between the previous decision on direction and the true direction. Combining with sensor observation model, we formulate the sensor fusion problem as a prediction problem in the form of state space model. Then we derive DP fusion algorithm based on MAP detection. Simulations show that the proposed DP fusion outperforms the traditional scheme and is more robust to parameter variations such as observation SNR.