Digital Twin (DT) technology has recently emerged as a powerful tool with the potential to revolutionize wireless systems as it enables accurate simulations, better decision-making, and tangible operational improvements. Prior studies on DT within the context of next generation wireless technologies has primarily focused on identifying potential use cases, application scenarios, and standardization challenges. However, the existing research lacks translating theoretical ideas into real-world applications. Our research, in this paper, contributes to the practical realization of DT technology in the context of 6G wireless networks, demonstrating its potential impact on network planning, performance, and user experience. In particular, we explore the construction, validation, and applications of DTs utilizing an indoor over-the-air (OTA) testbed powered by an in-house developed Next Generation Radio Access Network (NGRAN) that is fully compliant with 3rd Generation Partnership Project (3GPP) and Open RAN standards. First, we describe the steps that were followed to construct and validate a high-fidelity DT of our OTA testbed modeling both Radio Frequency (RF) environment and system components. Then, we demonstrate two pre-deployment use cases by explaining our extensive coverage estimation and network capacity planning OTA tests. Lastly, we also explore how DT enables practical machine learning solutions for post-deployment use cases and share our comprehensive OTA performance results, highlighting that our proposed mobility technique outperforms the classical approaches in terms of throughput and number of undesired handovers.

In this paper, we propose MAMBA, a restless multi-armed bandit framework for beam tracking in directional millimeter-wave (mmW) cellular systems. Instead of relying on explicit control messages, MAMBA utilizes the ACK/NACK packets transmitted by user equipments (UEs) to the base station (BS) as a part of the hybrid automatic repeat request (HARQ) procedure. These packets are used to measure the quality of the currently operating downlink beam, and select a new downlink beam along with an appropriate modulation and coding scheme (MCS) for future transmissions. At its core, MAMBA implements an online reinforcement learning technique called adaptive Thompson sampling (ATS), which determines a good beam and associated MCS to be used for the upcoming transmissions. To evaluate MAMBA's performance, we conduct extensive simulations and over-the-air (OTA) experiments over the 28 GHz band using phased-array antennas. We study fixed- as well as adaptive-rate variants of MAMBA, and contrast it with four other beam tracking strategies: a beam selection scheme similar to the one used in 5G NR (called ‘static oracle’), a theoretically optimal but practically infeasible beam tracking scheme (called ‘dynamic oracle’), an$\epsilon$-greedy algorithm [1], and the Unimodal Beam Alignment (UBA) algorithm [2]. Our results show that MAMBA achieves 182% throughput gain over the ‘static oracle’ and is reasonably close to the throughput of the ‘dynamic oracle’. Compared to UBA, MAMBA achieves 25-35% gain in throughput, depending on UE mobility. Finally, when operated at a fixed MCS, MAMBA/ATS achieves 21% gain over the$\epsilon$-greedy algorithm at the lowest applied MCS index, and 255% gain at the highest MCS index.

The use of highly directional antennas in millimeter wave (mmWave) cellular networks necessitates precise beam alignment between a base station (BS) and a user equipment (UE), which requires beam sweeping over a large number of directions and causes high initial access (IA) delay. Intuitively, such delay can be lowered by using wider beams, as fewer directions need to be swept. However, this results in a weak received signal and higher misdetection probability, which in turn increases the IA delay as more rounds of beam sweeping would be required to discover a UE. In this paper, we propose a multi-armed bandit approach for beamwidth optimization in 5G New Radio (NR) mmWave cellular networks. We aim to find the optimal beamwidths at the BS and the UE that minimize the beam sweeping delay for a successful IA. We first formulate the beamwidth optimization problem based on analyzing the interplay among beamwidth, beam sweeping overhead, and misdetection probability. Then, we propose a two-stage solution framework based on a multi-armed bandit approach. In the first stage, an initial solution of the BS beamwidth and the optimal solution of UE beamwidth are derived. In the second stage, each BS learns its optimal beamwidth by solving a multi-armed bandit problem with a Thompson sampling-based algorithm. Our extensive simulation results show that, the proposed algorithms can decrease the IA delay by more than 50% compared to the traditional fixed-beamwidth schemes.

Millimeter-wave (mmW) spectrum is a major candidate to support the high data rates of 5G systems. However, due to directionality of mmW communication systems, misalignments between the transmit and receive beams occur frequently, making link maintenance particularly challenging and motivating the need for fast and efficient beam tracking. In this paper, we propose a multi-armed bandit framework, called MAMBA, for beam tracking in mmW systems. We develop a reinforcement learning algorithm, called adaptive Thompson sampling (ATS), that MAMBA embodies for the selection of appropriate beams and transmission rates along these beams. ATS uses prior beam-quality information collected through the initial access and updates it whenever an ACK/NACK feedback is obtained from the user. The beam and the rate to be used during next downlink transmission are then selected based on the updated posterior distributions. Due to its model-free nature, ATS can accurately estimate the best beam/rate pair, without making assumptions regarding the temporal channel and/or user mobility. We conduct extensive experiments over the 28 GHz band using a 4x8 phased- array antenna to validate the efficiency of ATS, and show that it improves the link throughput by up to 182%, compared to the beam management scheme proposed for 5G.

Millimeter wave (mmW) communications have recently attracted considerable attention as a key element of next-generation (5G) wireless systems. Despite significant efforts in this domain, establishing and maintaining directional mmW links in a dynamic environment are still quite challenging, largely due to the search-time overhead of beam scanning, and the vulnerability of directional links to beam misalignment, blockage, and outages. In this paper, we propose SmartLink, a protocol that exploits the multi-cluster scattering phenomenon at mmW frequencies to establish a multi-directional link between a base station and a user. By exploiting multiple clusters, SmartLink enables fast initial access and link maintenance, along with sustained throughput. A search algorithm called multi-lobe beam search (MLBS) is used to discover multiple channel clusters by probing several directions simultaneously using carefully designed multilobe beam patterns. MLBS reduces the search time from linear to logarithmic with respect to the number of directions. We provide detailed analysis of the false alarm and misdetection probabilities for the designed beam patterns. Following cluster discovery, SmartLink divides antennas into sub-arrays to generate the optimal multi-lobe pattern with respect to cluster powers and blockage probabilities. Finally, extensive trace-driven simulations at 29 GHz frequency using phased-array antennas verify the efficiency of SmartLink.

Directionality in millimeter-wave (mmW) systems make link establishment and maintenance challenging, due to the search-time overhead of beam scanning and the vulnerability of directional links to blockages. In this paper, we propose a communication protocol called SmartLink, which exploits the clustering phenomenon at mmW frequencies to establish a multi-beam link between a base station and a user. By utilizing multiple clusters, SmartLink enables efficient link maintenance and sustained throughput. We develop a logarithmic-time search algorithm called multi-lobe beam search (MLBS), which is used in SmartLink to discover the clusters. MLBS probes several directions simultaneously, using multi-lobe beam patterns. The number of simultaneous lobes is selected to minimize the search time of the clusters. We provide detailed analysis of the false alarm and misdetection probabilities for the designed beam patterns. Following cluster discovery, SmartLink divides antennas into sub-arrays to generate the optimal multi-lobe beam pattern that maximizes the average data rate under blockage. Extensive simulations using actual channel traces obtained by utilizing phased-array antennas at 29 GHz are used to verify the efficiency of SmartLink. MLBS decreases the discovery time by up to 88 compared to common existing search schemes, and exploiting multiple clusters improves the average data rate by 10.

We consider a single-cell massive multiple-input multiple-output (MIMO) system in which a base station (BS) with a large number of antennas transmits simultaneously to several single-antenna users. The BS acquires the channel state information (CSI) for various receivers using uplink pilot transmissions. We demonstrate the vulnerability of the CSI estimation process to pilot-contamination (PC) attacks. In our attack model, the attacker aims at minimizing the sum rate of downlink transmissions by contaminating the uplink pilots. We first study these attacks for two downlink power allocation strategies under the assumption that the attacker knows the locations of the BS and its users. Later on, we relax this assumption and consider the case when such knowledge is probabilistic. The formulated problems are solved using stochastic optimization, Lagrangian minimization, and game-theoretic methods. A closed-form solution for a special case of the problem is obtained. Furthermore, we analyze the achievable individual secrecy rates under PC attacks and provide an upper bound on these rates. We also study this scenario without a priori knowledge of user locations at the attacker by introducing chance constraints. Our results indicate that such attacks can degrade the throughput of a massive MIMO system by more than 50%.

We consider a broadcast channel in which a multi-antenna transmitter (Alice) sends K confidential information signals to K legitimate users (Bobs) in the presence of L eavesdroppers (Eves). Alice uses multiple-input multiple-output (MIMO) precoding to generate the information signals along with her own (Tx-based) friendly jamming (FJ). Interference at each Bob is removed by MIMO zero-forcing. This, however, leaves a “vulnerability region” around each Bob, which can be exploited by a nearby Eve. We address this problem by augmenting Tx-based FJ (TxFJ) with Rx-based FJ (RxFJ), generated by each Bob. Specifically, each Bob uses self-interference suppression to transmit a friendly jamming signal, while simultaneously receiving an information signal over the same channel. We minimize the powers allocated to the information, TxFJ, and RxFJ signals under given guarantees on the individual secrecy rate for each Bob. The problem is solved for the cases when the eavesdropper's channel state information is known/unknown. Simulations show the effectiveness of the proposed solution. Furthermore, we discuss how to schedule transmissions when the rate requirements need to be satisfied on average rather than instantaneously. Under special cases, a scheduling algorithm that serves only the strongest receivers is shown to outperform the one that schedules all receivers.

This paper considers a broadcast channel with a multi-antenna transmitter (Alice) sending two independent confidential data streams to two legitimate users (Bob and Charlie) in the presence of a passive eavesdropper (Eve). To enhance their secrecy rates, Bob and Charlie are assumed to be capable of self-interference suppression (SIS). Alice, on the other hand, uses MIMO precoding to generate the two confidential information signals along with its own (Tx-based) friendly jamming. The interfering signals at Bob and Charlie are removed by employing the zero-forcing technique. This, however, leaves ``vulnerability regions'' around Bob and Charlie, which can be exploited by a nearby eavesdropper. We address this problem by augmenting Tx-based friendly jamming with Rx-based friendly jamming, generated by Bob and Charlie. For the resulting broadcast channel, a secrecy encoding scheme is developed to construct the signals intended to Bob and Charlie. The corresponding achievable secrecy sum-rate is characterized, and an optimization problem is formulated. A special case of this problem is investigated. Simulation results show the effectiveness of utilizing (Tx- and/or Rx-based) jamming, and the impact of the degree of SIS on physical-layer security.