Reconfigurable intelligent surfaces (RISs) have attracted increasing interest due to their ability to improve the coverage, reliability, and energy efficiency of millimeter wave (mmWave) communication systems. However, designing the RIS beamforming typically requires large channel estimation or beam training overhead, which degrades the efficiency of these systems. In this paper, we propose to equip the RIS surfaces with visual sensors (cameras) that obtain sensing information about the surroundings and user/basestation locations, guide the RIS beam selection, and reduce the beam training overhead. We develop a machine learning (ML) framework that leverages this visual sensing information to efficiently select the optimal RIS reflection beams that reflect the signals between the basestation and mobile users. To evaluate the developed approach, we build a high-fidelity synthetic dataset that comprises co-existing wireless and visual data. Based on this dataset, the results show that the proposed vision-aided machine learning solution can accurately predict the RIS beams and achieve near-optimal achievable rate while significantly reducing the beam training overhead.

Can reconfigurable intelligent surfaces (RISs) operate in a standalone mode that is completely transparent to the 3GPP 5G initial access process? Realizing that may greatly simplify the deployment and operation of these surfaces and reduce the infrastructure control overhead. This paper investigates the feasibility of building standalone/transparent RIS systems and shows that one key challenge lies in determining the user equipment (UE)-side RIS beam reflection direction. To address this challenge, we propose to equip the RISs with multi-modal sensing capabilities (e.g., using wireless and visual sensors) that enable them to develop some perception of the surrounding environment and the mobile users. Based on that, we develop a machine learning framework that leverages the wireless and visual sensors at the RIS to select the high-performance beams between the base station (BS) and UEs and enable standalone/transparent RIS operation for 5G high-frequency systems. Using a high-fidelity synthetic dataset with co-existing wireless and visual data, we extensively evaluate the performance of the proposed framework. Experimental results demonstrate that the proposed approach can accurately predict the BS and UE-side candidate beams, and that the standalone RIS beam selection solution is capable of realizing near-optimal achievable rates with significantly reduced beam training overhead.

This paper addresses point-to-point communication over block-fading channels with independent fading blocks. When both channel state information at the transmitter and receiver (CSIR) are available, most achievable schemes use separable coding, i.e., coding independently and in parallel over different fading states. Unfortunately, separable coding has drawbacks, including large memory requirements at both communication ends. In this paper, a lattice coding and decoding scheme is proposed that achieves the ergodic capacity without separable coding, with lattice codebooks and decoding decision regions that are universal across channel realizations. We first demonstrate this result for fading distributions with discrete, finite support whose sequences are robustly typical. Results are then extended to continuous fading distributions, as well as multiple-input multiple-output (MIMO) systems. In addition, a variant of the proposed scheme is presented for the MIMO ergodic fading channel with CSIR only, where we prove the existence of a universal codebook that achieves rates within a constant gap to capacity for finite-support fading distributions. The gap is small compared with other schemes in the literature. Extension to continuous-valued fading is also provided.

One of the main challenges of communication in the absence of transmitter channel knowledge is codebook universality, i.e., the existence of a single codebook that guarantees a given rate for all channel states. We address this problem for a class of ergodic fading multiple-input multiple-output (MIMO) channels, whose fading distribution is not necessarily isotropic. It is shown that a universal codebook drawn from a nested lattice code achieves the ergodic capacity to within a constant gap. Interestingly, the gap vanishes in some scenarios.

The capacity region of the ergodic fading interference channel remains unknown. Moreover, most achievable rate regions known to date for fading interference channels are based on Gaussian codes, whose encoding and decoding complexity is too high. In this paper, we study the two-user ergodic interference channel with channel knowledge only at the receiver side. First, we establish the capacity region for the case where interference (cross) link gains are statistically stronger than forward link gains, denoted the ergodic strong interference channel. Additionally, we establish the rate region for this channel under lattice coding and decoding, whose complexity is significantly less than Gaussian codes. We show that the sum rate achieved using lattice codes is very close to sum capacity.

For ergodic fading, a lattice coding and decoding strategy is proposed and its performance is analyzed for the single-input single-output (SISO) and multiple-input multiple-output (MIMO) point-to-point channel as well as the multiple-access channel (MAC), with channel state information available only at the receiver (CSIR). At the decoder a novel strategy is proposed consisting of a time-varying equalization matrix followed by decision regions that depend only on channel statistics, not individual realizations. Our encoder has a similar structure to that of Erez and Zamir. For the SISO channel, the gap to capacity is bounded by a constant under a wide range of fading distributions. For the MIMO channel under Rayleigh fading, the rate achieved is within a gap to capacity that does not depend on the signal-to-noise ratio (SNR), and diminishes with the number of receive antennas. The analysis is extended to the K-user MAC where similar results hold. Achieving a small gap to capacity while limiting the use of CSIR to the equalizer highlights the scope for efficient decoder implementations, since decision regions are fixed, i.e., independent of channel realizations.

A multiple-input multiple-output (MIMO) version of the dirty paper channel is studied, where the channel input and the dirt experience the same fading process, and the fading channel state is known at the receiver. This represents settings where signal and interference sources are co-located, such as in the broadcast channel. First, a variant of Costa's dirty paper coding is presented, whose achievable rates are within a constant gap to capacity for all signal and dirt powers. In addition, a lattice coding and decoding scheme is proposed, whose decision regions are independent of the channel realizations. Under Rayleigh fading, the gap to capacity of the lattice coding scheme vanishes with the number of receive antennas, even at finite Signal-to-Noise Ratio (SNR). Thus, although the capacity of the fading dirty paper channel remains unknown, this paper shows it is not far from its dirt-free counterpart. The insights from the dirty paper channel directly lead to transmission strategies for the two-user MIMO broadcast channel, where the transmitter emits a superposition of desired and undesired (dirt) signals with respect to each receiver. The performance of the lattice coding scheme is analyzed under different fading dynamics for the two users, showing that high-dimensional lattices achieve rates close to capacity.

Many aspects of lattice coding and decoding under time-varying multiple-antenna systems remain unexplored. This paper studies the achievable rates using lattice codes for the multiple-input- multiple-output (MIMO) point-to-point channel with ergodic fading and channel state information at the receiver. The proposed lattice coding scheme involves the use of decision regions that are universal for almost all realizations of a given fading distribution and hence providing a significant computational advantage over channel- matching decision regions. Under Rayleigh fading, the rates achieved are within a constant gap to the ergodic capacity. The gap is also shown to diminish as the number of receive antennas increases, implying the asymptotic optimality of lattice codes even at finite signal-to-noise ratio (SNR).

A multiple-input-multiple-output (MIMO) version of Costa's dirty paper channel is studied, where both the input signal and the state experience ergodic fading with channel state information at the receiver. An inner bound using lattice coding and decoding is derived and its gap to the point-to- point ergodic capacity is computed. Under Rayleigh fading, the gap is a constant that vanishes as the number of receive antennas increases, even at finite signal-to-noise ratio (SNR). This implies that under certain configurations the point-to- point capacity can be almost realized in the presence of fading dirt, similar to Costa's result for the non-fading scalar case. The proposed scheme is applied to a class of MIMO broadcast channels with receive channel state information. The scheme is compared to time sharing as well as a dirty paper scheme with non-causal channel state information at the transmitter. The lattice coding scheme is shown to outperform time sharing as well as achieve most of the region of the dirty paper scheme for the cases under study.

A modified version of Costa's dirty paper channel is studied, in which both the input signal and the state experience stationary and ergodic time-varying fading. The fading coefficients are assumed to be known exclusively at the receiver. An inner bound of the achievable rates using lattice codes is derived and compared to an outer bound of the capacity. For a wide range of fading distributions, the gap to capacity is within a constant value that does not depend on either the power of the input signal or the state. The results presented in this paper are applied to a class of ergodic fading broadcast channels with receive channel state information, where the achievable rate region is shown to be close to capacity under certain configurations.

The performance of lattice codes in the additive white Gaussian noise (AWGN) channel has attracted much attention lately, however, their performance under ergodic fading channels has been relatively unexplored. We show that lattice coding and decoding achieve the capacity of the ergodic point-to-point and multiple-access channels (MAC). Additionally, a low-complexity scheme is proposed for the ergodic MAC. At moderate and high signal-to-noise ratio (SNR), the sum rate achieved by the low-complexity scheme is within a constant gap to the ergodic MAC sum capacity, whereas at low SNR the gap to capacity diminishes quadratically with linear SNR decrease.

It is known that lattice coding can achieve the capacity of the additive white Gaussian noise (AWGN) channel. This paper addresses the performance of lattice codes in the ergodic fading channel. Using nested lattice codes and ambiguity decoding, we show that the rates achieved by lattice coding and decoding are within a constant gap of the capacity of the ergodic channel at moderate and high signal-to-noise-ratio (SNR), and within a gap that decreases quadratically with the SNR for the low SNR regime.

A novel transmission scheme is developed for the downlink frame of cellular networks. Each base station (BS) aims at iteratively balancing the throughput at the mobile stations (MSs) of its cell with the interference it causes at the MSs of the neighboring cells, requiring negligible coordination between the BSs. A simplified version of the scheme that neither requires iterations nor cooperation is also proposed. Simulation results show that the proposed schemes achieve substantial gains over well-known schemes in the literature.