Energy Efficiency Maximization for Intelligent Surfaces Aided Massive MIMO with Zero

In this work, we address the energy efficiency (EE) maximization problem in a downlink communication system utilizing reconfigurable intelligent surface (RIS) in a multi-user massive multiple-input multiple-output (mMIMO) setup with zero-forcing (ZF) precoding. The channel between the base station (BS) and RIS operates under a Rician fading with Rician factor K1. Since systematically optimizing the RIS phase shifts in each channel coherence time interval is challenging and burdensome, we employ the statistical channel state information (CSI)-based optimization strategy to alleviate this overhead. By treating the RIS phase shifts matrix as a constant over multiple channel coherence time intervals, we can reduce the computational complexity while maintaining an interesting performance. Based on an ergodic rate (ER) lower bound closed-form, the EE optimization problem is formulated. Such a problem is non-convex and challenging to tackle due to the coupled variables. To circumvent such an obstacle, we explore the sequential optimization approach where the power allocation vector p, the number of antennas M, and the RIS phase shifts v are separated and sequentially solved iteratively until convergence. With the help of the Lagrangian dual method, fractional programming (FP) techniques, and Lemma 1, insightful compact closed-form expressions for each of the three optimization variables are derived. Simulation results validate the effectiveness of the proposed method across different generalized channel scenarios, including non-line-of-sight (NLoS) and partially line-of-sight (LoS) conditions. This underscores its potential to significantly reduce power consumption, decrease the number of active antennas at the base station, and effectively incorporate RIS structure in mMIMO communication setup with just statistical CSI knowledge.

Reconfigurable Intelligent Surfaces-Enabled Intra-Cell Pilot Reuse in Massive MIMO Systems

Channel state information (CSI) estimation is a critical issue in the design of modern massive multiple-input multiple-output (mMIMO) networks. With the increasing number of users, assigning orthogonal pilots to everyone incurs a large overhead that strongly penalizes the system's spectral efficiency (SE). It becomes thus necessary to reuse pilots, giving rise to pilot contamination, a vital performance bottleneck of mMIMO networks. Reusing pilots among the users of the same cell is a desirable operation condition from the perspective of reducing training overheads; however, the intra-cell pilot contamination might worsen due to the users' proximity. Reconfigurable intelligent surfaces (RISs), capable of smartly controlling the wireless channel, can be leveraged for intra-cell pilot reuse. In this paper, our main contribution is a RIS-aided approach for intra-cell pilot reuse and the corresponding channel estimation method. Relying upon the knowledge of only statistical CSI, we optimize the RIS phase shifts based on a manifold optimization framework and the RIS positioning based on a deterministic approach. The extensive numerical results highlight the remarkable performance improvements the proposed scheme achieves (for both uplink and downlink transmissions) compared to other alternatives.

Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems

In this work, two machine learning (ML)-based structures for joint detection-channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM), are developed {to provide improved data detection performance} and compared with the conventional matched filter (MF) detector equipped with the minimum mean square error (MMSE) and least square (LS) channel estimators. The bit-error-rate (BER) performance vs. computational complexity trade-off is analyzed, demonstrating the superiority of the proposed DNN-OFDM and ELM-OFDM detectors methodologies.

Improving Random Access with NOMA in mMTC XL-MIMO

The extra-large multiple-input multiple-output (XL-MIMO) architecture has been recognized as a technology for supporting the massive MTC (mMTC), providing very high-data rates in high-user density scenarios. However, the large dimension of the array increases the Rayleigh distance (dRayl), in addition to obstacles and scatters causing spatial non-stationarities and distinct visibility regions (VRs) across the XL array extension. We investigate the random access (RA) problem in crowded XL-MIMO scenarios; the proposed grant-based random access (GB-RA) protocol combining the advantage of non-orthogonal multiple access (NOMA) and strongest user collision resolutions in extra-large arrays (SUCRe-XL) named NOMA-XL can allow access of two or three colliding users in the same XL sub-array (SA) selecting the same pilot sequence. The received signal processing in a SA basis changes the dRayl, enabling the far-field planar wavefront propagation condition, while improving the system performance. The proposed NOMA-XL GB-RA protocol can reduce the number of attempts to access the mMTC network while improving the average sum rate, as the number of SA increases.

OnRMap: An Online Radio Mapping Approach for Large Intelligent Surfaces

We introduce OnRMap, an online radio mapping (RMap) approach for the sensing and localization of active users (AUs), devices that are transmitting radio signals, and passive elements (PEs), elements that are in the environment and are illuminated by the AUs' radio signals. OnRMap processes the signals received by a large intelligent surface and produces a radio map (RM) of the environment based on signal processing techniques. The method then senses and locate the different elements without the need for offline scanning phases, which is important for environments with frequently changing spatial layouts. Empirical results demonstrate that OnRMap presents a higher localization accuracy than an offline method, but the price paid for being an online method is a moderate reduction in the detection rate.

QoS-Aware User Scheduling in Crowded XL-MIMO Systems Under Non-Stationary Multi-State LoS/NLoS Channels

Providing minimum quality-of-service (QoS) in crowded wireless communications systems, with high user density, is challenging due to the network structure with limited transmit power budget and resource blocks. Smart resource allocation methods, such as user scheduling, power allocation, and modulation and coding scheme selection, must be implemented to cope with the challenge. Aiming to enhance the number of served users with minimum QoS in the downlink (DL) channel of crowded extra-large scale massive multiple-input multiple-output (XL-MIMO) systems, in this paper we propose a QoS-aware joint user scheduling and power allocation technique. The proposed technique is constituted by two sequential procedures: the clique search-based scheduling (CBS) algorithm for user scheduling followed by optimal power allocation with transmit power budget and minimum achievable rate per user constraints. To accurately evaluate the proposed technique in the XL-MIMO scenario, we propose a generalized non-stationary multi-state channel model based on spherical-wave propagation assuming that users under LoS and NLoS transmission coexist in the same communication cell. Such model considers that users under different channel states experience different propagation aspects both in the multipath fading model and the path loss rule. Numerical results on the achievable sum-rate, number of scheduled users, and distribution of the scheduled users reveal that the proposed CBS algorithm provides a fair coverage over the whole cell area, achieving remarkable numbers of scheduled users when users under the LoS and NLoS channel states coexist in the communication cell.

Throughput and Latency in the Distributed Q-Learning Random Access mMTC Networks

In mMTC mode, with thousands of devices trying to access network resources sporadically, the problem of random access (RA) and collisions between devices that select the same resources becomes crucial. A promising approach to solve such an RA problem is to use learning mechanisms, especially the Q-learning algorithm, where the devices learn about the best time-slot periods to transmit through rewards sent by the central node. In this work, we propose a distributed packet-based learning method by varying the reward from the central node that favors devices having a larger number of remaining packets to transmit. Our numerical results indicated that the proposed distributed packet-based Q-learning method attains a much better throughput-latency trade-off than the alternative independent and collaborative techniques in practical scenarios of interest. In contrast, the number of payload bits of the packet-based technique is reduced regarding the collaborative Q-learning RA technique for achieving the same normalized throughput.

Quasi-Distributed Antenna Selection for Spectral Efficiency Maximization in Subarray Switching XL-MIMO Systems

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings.