Uplink Wave-Domain Combiner for Stacked Intelligent Metasurfaces Accounting for Hardware Limitations

Using refractive metasurfaces (RMTSs) as part of the antenna design offers a promising solution to the ever-increasing demand for improved energy efficiency of wireless communications. To overcome the limitations of using one RMTS layer, a recent proposal is to cascade multiple layers, a structure called stacked intelligent metasurfaces (SIMs) where the desired precoder and combiner is formed in the wave domain. However, while proposing the antenna structure, the analysis did not account for the attendant limitations imposed by the hardware used which has a significant impact on the performance of SIMs. In this paper, we study the achievable sum-rate of a SIM antenna in an uplink wireless communication scenario accounting for hardware limitations. We begin by proposing a system model that captures both the effect of noise and hardware limitations in these systems. We then formulate the achievable sum-rate problem; since optimizing the rate is non-convex, we propose two approaches: a gradient ascent algorithm and an interior point method to find a close-to-optimum combiner. To show the efficiency of using SIMs at a basestation, we compare the achievable sum-rate with that of a digital phased array (DPA). We provide two comparisons: first, in Rayleigh fading and realistic 3GPP channels and then under a constraint of an equal number of radio frequency (RF) chains and equal physical aperture size constraint. Our results show that SIM antennas can surpass DPA performance under an equal number of RF chains but has inferior performance under equal aperture size.

Hardware Limitations of Dynamic Metasurface Antennas in the Uplink: A Comparative Study

Dynamic Metasurface Antennas (DMAs) have emerged as promising candidates for basestation deployment in the next generation of wireless communications. While overlooking the practical and hardware limitations of DMA, previous studies have highlighted DMAs' potential to deliver high data rates while maintaining low power consumption. In this paper, we address this oversight by analyzing the impact of practical hardware limitations such as antenna efficiency, power consumed in required components, processing limitations, etc. Specifically, we investigate DMA-assisted wireless communications in the uplink and propose a model which accounts for these hardware limitations. To do so, we propose a concise model to characterize the power consumption of a DMA. For a fair assessment, we propose a wave-domain combiner, based on holography theory, to maximize the achievable sum rate of DMA-assisted antennas. We compare the achievable sum rate and energy efficiency of DMA antennas with that of a partially connected hybrid phased array. Our findings reveal the true potential of DMAs when accounting for the limitations of both designs.

Uplink resource allocation optimization for user-centric cell-free MIMO networks

We examine the problem of optimizing resource allocation in the uplink for a user-centric, cell-free, multi-input multi-output network. We start by modeling and developing resource allocation algorithms for two standard network operation modes. The centralized mode provides high data rates but suffers multiple issues, including scalability. On the other hand, the distributed mode has the opposite problem: relatively low rates, but is scalable. To address these challenges, we combine the strength of the two standard modes, creating a new semi-distributed operation mode. To avoid the need for information exchange between access points, we introduce a new quality of service metric to decentralize the resource allocation algorithms. Our results show that we can eliminate the need for information exchange with a relatively small penalty on data rates.

Handoffs in User-Centric Cell-Free MIMO Networks: A POMDP Framework

We study the problem of managing handoffs (HOs) in user-centric cell-free massive MIMO (UC-mMIMO) networks. Motivated by the importance of controlling the number of HOs and by the correlation between efficient HO decisions and the temporal evolution of the channel conditions, we formulate a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading and the action space representing the association decisions of the user with the access points (APs). We develop a novel algorithm that employs this model to derive a HO policy for a mobile user based on current and future rewards. To alleviate the high complexity of our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved separately. Then, the policy and the candidate pool of APs for the sub-problem that produced the best total expected reward are used to perform HOs within a specific time horizon. We then introduce modifications to our algorithm to decrease the number of HOs. The results show that half of the number of HOs in the UC-mMIMO networks can be eliminated. Namely, our novel solution can control the number of HOs while maintaining a rate guarantee, where a 47%-70% reduction of the cumulative number of HOs is observed in networks with a density of 125 APs per km2. Most importantly, our results show that a POMDP-based HO scheme is promising to control HOs.

Terahertz Induced Protein Interactions in a Random Medium

Folding of proteins into their correct native structure is key to their function. Simultaneously, the intricate interplay between cell movement and protein conformation highlights the complex nature of cellular processes. In this work, we demonstrate the impact of Terahertz (THz) signaling on controlling protein conformational changes in a random medium. Our system of interest consists of a communication link that involves a nanoantenna transmitter, a protein receiver, and a channel composed of moving red blood cells. Due to the system dynamics, we investigate the influence of both the fast and slow channel variations on protein folding. Specifically, we analyze the system's selectivity to asses the effectiveness of the induced THz interaction in targeting a specific group of proteins under fading conditions. By optimizing the selectivity metric with respect to the nanoantenna power and frequency, it is possible to enhance the controllability of protein interactions. Our probabilistic analysis provides a new perspective regarding electromagnetically triggered protein molecules, their microenvironment and their interaction with surrounding particles. It helps elucidate how external conditions impact the protein folding kinetics and pathways. This results in not only understanding the mechanisms underlying THz-induced protein interactions but also engineering these still-emerging tools.

POMDP-based Handoffs for User-Centric Cell-Free MIMO Networks

We propose to control handoffs (HOs) in user-centric cell-free massive MIMO networks through a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading (LSF) and the action space representing the association decisions of the user with the access points. Our proposed formulation accounts for the temporal evolution and the partial observability of the channel states. This allows us to consider future rewards when performing HO decisions, and hence obtain a robust HO policy. To alleviate the high complexity of solving our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved individually. Then, the policy and the candidate cluster of access points for the best solved sub-problem is used to perform HOs within a specific time horizon. We control the number of HOs by determining when to use the HO policy. Our simulation results show that our proposed solution reduces HOs by 47% compared to time-triggered LSF-based HOs and by 70% compared to data rate threshold-triggered LSF-based HOs. This amount can be further reduced through increasing the time horizon of the POMDP.

Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems

It is well accepted that acquiring downlink channel state information in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems is challenging because of the large overhead in training and feedback. In this paper, we propose a deep generative model (DGM)-based technique to address this challenge. Exploiting the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent underlying channel parameters, i.e., the magnitudes of path gains, delays, angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training, since these parameters are common in both uplink and downlink. Then, the frequency-specific underlying channel parameters, namely, the phase of each propagation path, are estimated via downlink training using a very short training signal. In the first step, we incorporate the underlying distribution of the channel parameters as a prior into our channel estimation algorithm. We use DGMs to learn this distribution. Simulation results indicate that our proposed DGM-based channel estimation technique outperforms, by a large gap, the conventional channel estimation techniques in practical ranges of signal-to-noise ratio (SNR). In addition, a near-optimal performance is achieved using only few downlink pilot measurements.

Analysis and Design of Distributed MIMO Networks with a Wireless Fronthaul

We consider the analysis and design of distributed wireless networks wherein remote radio heads (RRHs) coordinate transmissions to serve multiple users on the same resource block (RB). Specifically, we analyze two possible multiple-input multiple-output wireless fronthaul solutions: multicast and zero forcing (ZF) beamforming. We develop a statistical model for the fronthaul rate and, coupled with an analysis of the user access rate, we optimize the placement of the RRHs. This model allows us to formulate the location optimization problem with a statistical constraint on fronthaul outage. Our results are cautionary, showing that the fronthaul requires considerable bandwidth to enable joint service to users. This requirement can be relaxed by serving a low number of users on the same RB. Additionally, we show that, with a fixed number of antennas, for the multicast fronthaul, it is prudent to concentrate these antennas on a few RRHs. However, for the ZF beamforming fronthaul, it is better to distribute the antennas on more RRHs. For the parameters chosen, using a ZF beamforming fronthaul improves the typical access rate by approximately 8% compared to multicast. Crucially, our work quantifies the effect of these fronthaul solutions and provides an effective tool for the design of distributed networks.

Distributed Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

We develop two distributed downlink resource allocation algorithms for user-centric, cell-free, spatially-distributed, multiple-input multiple-output (MIMO) networks. In such networks, each user is served by a subset of nearby transmitters that we call distributed units or DUs. The operation of the DUs in a region is controlled by a central unit (CU). Our first scheme is implemented at the DUs, while the second is implemented at the CUs controlling these DUs. We define a hybrid quality of service metric that enables distributed optimization of system resources in a proportional fair manner. Specifically, each of our algorithms performs user scheduling, beamforming, and power control while accounting for channel estimation errors. Importantly, our algorithm does not require information exchange amongst DUs (CUs) for the DU-distributed (CU-distributed) system, while also smoothly converging. Our results show that our CU-distributed system provides 1.3- to 1.8-fold network throughput compared to the DU-distributed system, with minor increases in complexity and front-haul load - and substantial gains over benchmark schemes like local zero-forcing. We also analyze the trade-offs provided by the CU-distributed system, hence highlighting the significance of deploying multiple CUs in user-centric cell-free networks.

Downlink Resource Allocation in Multiuser Cell-free MIMO Networks with User-centric Clustering

In this paper, we optimize user scheduling, power allocation and beamforming in distributed multiple-input multiple-output (MIMO) networks implementing user-centric clustering. We study both the coherent and non-coherent transmission modes, formulating a weighted sum rate maximization problem for each; finding the optimal solution to these problems is known to be NP-hard. We use tools from fractional programming, block coordinate descent, and compressive sensing to construct an algorithm that optimizes the beamforming weights and user scheduling and converges in a smooth non-decreasing pattern. Channel state information (CSI) being crucial for optimization, we highlight the importance of employing a low-overhead pilot assignment policy for scheduling problems. In this regard, we use a variant of hierarchical agglomerative clustering, which provides a suboptimal, but feasible, pilot assignment scheme; for our cell-free case, we formulate an area-based pilot reuse factor. Our results show that our scheme provides large gains in the long-term network sum spectral efficiency compared to benchmark schemes such as zero-forcing and conjugate beamforming (with round-robin scheduling) respectively. Furthermore, the results show the superiority of coherent transmission compared to the non-coherent mode under ideal and imperfect CSI for the area-based pilot-reuse factors we consider.