Network optimization in RIS-assisted communications

Abstract This thesis presents new user association (UA) schemes that take cell interference into account for a multi-cell network aided with multiple reconfigurable intelligent surfaces (RISs). We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming at the base stations (BSs), passive beamforming at the RISs, and user-BS association with consideration to the impact of RISs. We then propose a computationally efficient iterative algorithm based on alternating optimization to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the RISs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to achieve an optimal solution. Additionally, we balance BS loads and maximize the network utility by optimizing the user association with a matching game in another scheme. Finally, a crucial aspect of 6G is that localization and sensing will not be a by-product of communications development but will instead be integrated into the system from the start, and thus is a main design target of 6G. Toward this, a vision for how location and sensing information can be used to support, enable, and enrich novel applications will be sketched. In addition, the potential benefits of location and sensing information for improving communications are investigated as use cases. Therefore, taking advantage of sensing with radio waves and localization, we propose a novel environment-aware joint active/passive beamforming approach for RIS-aided wireless communication based on the new concept of channel knowledge map (CKM). In the proposed scheme, the user equipment location information is combined with the radio environment information provided by CKM to achieve efficient beamforming without real-time training. Simulation results show the proposed scheme’s superior performance over training-based beamforming, which is also quite robust to errors related to the UE’s location in practice.Tiivistelmä Tässä väitöskirjassa esitellään uusia käyttäjäyhteys (user association, UA)- järjestelmiä, joissa otetaan huomioon solujen häiriöt monisoluverkossa, jossa on useita uudelleenkonfiguroitavia älykkäitä pintoja (reconfigurable intelligent surfaces, RIS). Muotoilemme verkon spektritehokkuuden maksimoinnin ongelman optimoimalla yhdessä aktiivisen keilanmuodostuksen tukiasemilla, passiivisen keilanmuodostuksen RIS-pinnoilla ja käyttäjän tukiasemayhteyden ottaen huomioon RIS-pintojen vaikutuksen. Tämän jälkeen ehdotamme laskennallisesti tehokasta iteratiivista algoritmia, joka perustuu vuorottelevaan optimointiin, jotta tämä hankala ei-konveksi sekakokonaislukuongelma saadaan ratkaistua. Tukiasemien aktiivisen keilanmuodostuksen ja RIS-pintojen passiivisen keilanmuodostuksen optimointiin käytetään fraktionaalista ohjelmointitekniikkaa, ja peräkkäiseen konveksiin ohjelmointiin yhdistettyä rangaistusmenetelmää sovelletaan käyttäjäyhteyden optimointiin. Sen avulla osoitetaan päästävän optimaaliseen ratkaisuun. Lisäksi tasapainotamme tukiasemien kuormia ja maksimoimme verkon käytön optimoimalla käyttäjäyhteyden vastaavalla pelillä toisessa järjestelmässä. 6G:n keskeinen näkökohta on, että paikantaminen ja tunnistaminen eivät ole viestinnän kehityksen sivutuotteita, vaan ne integroidaan järjestelmään alusta alkaen, ja ne ovat siten 6G:n suunnittelun päätavoitteita. Tätä varten hahmotellaan visio siitä, miten sijainti- ja tunnistamistietoja voidaan käyttää uusien sovellusten tukemiseen, mahdollistamiseen ja rikastamiseen. Lisäksi käyttötapauksina tutkitaan paikannus- ja tunnistamistietojen mahdollisia hyötyjä viestinnän parantamisessa. Tämän vuoksi ja radioaaltojen avulla tapahtuvaa tunnistamista ja paikannusta hyödyntämällä ehdotamme uutta ympäristötietoista yhdistettyä aktiivista/passiivista keilanmuodostusta RIS-avusteiseen langattomaan viestintään uuden kanavatietokartan konseptin perusteella. Ehdotetussa järjestelmässä käyttäjien laitteiden sijaintitiedot yhdistetään kanavatietokartan radioympäristötietoihin, jotta keilanmuodostus olisi tehokasta ilman reaaliaikaista koulutusta. Simulaatiotulokset osoittavat ehdotetun järjestelmän ylivertaisen suorituskyvyn verrattuna koulutukseen perustuvaan keilanmuodostukseen, ja se kestää myös erittäin hyvin käyttäjälaitteiden sijaintiin liittyviä virheitä käytännössä

A Two Step Secure Spectrum Sensing Algorithm Using Fuzzy Logic for Cognitive Radio Networks

In this paper, a two step secure spectrum sensing algorithm is proposed for cognitive radio networks. In this algorithm, the sensing results of secondary users are pre-filtered and applying fuzzy logic, so, the overall sensing performance of the network is improved. To determine pre-filter parameters, statistical parameters of the sensing results are used to remove those sensing results which are far from the majority sensing results. However, to obtain a better performance in the spectrum sensing, we propose a fuzzy logic to nullify the ef-fects of malicious users who transmit false sensing data to the fusion center. We further propose a Fuzzy Trust Level for each user as to weight the sensing result of the corresponding user before combining all sensing results in the fusion center. Simulation results demonstrate that our proposed algorithm yield signifi-cant improvement in the performance of the spectrum sensing and identifying malicious users

Environment-aware joint active/passive beamforming for RIS-aided communications leveraging channel knowledge map

Abstract Reconfigurable intelligent surface (RIS)-aided communication is a promising technology for 6G systems to reconfigure the propagation environment proactively. However, it requires efficient real-time channel training, which suffers excessive overhead. To resolve this challenge, taking advantage of sensing with radio waves and localization, we propose a novel environment-aware joint active/passive beamforming for RIS-aided wireless communication based on the new concept of channel knowledge map (CKM). In the proposed scheme, the user equipments (UEs) location information is combined with the radio environment information provided by CKM to achieve efficient beamforming without real-time training. Simulation results show the proposed scheme’s superior performance over training-based beamforming, which is also quite robust to errors related to UE’s location in practice

User association in millimeter wave cellular networks with intelligent reflecting surfaces

Abstract In this paper, we introduce a new load balancing user association scheme for millimeter wave (mmWave) cellular networks in which intelligent reflecting surface (IRS) is applied in the cellular network to improve the coverage region of each cell and mitigate mmWave vulnerability to non-line of sight (N-LoS) paths. The user association scheme improves network performance significantly by adjusting the interference according to the association. We study the IRS-assisted mmWave cellular network where one IRS is deployed to assist in the communication from the base station (BS) to mobile users (MUs) in each cell. We balance BS loads and maximize a network utility by optimizing the user association with a matching game. Simulation results show that the proposed scheme significantly improves the throughput compared to conventional user association techniques

Deep reinforcement learning for practical phase shift optimization in RIS-assisted networks over short packet communications

Abstract We study the practical phase shift design in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and low-latency communication (URLLC) system under finite blocklength (FBL) regime by leveraging a novel deep reinforcement learning (DRL) algorithm named as twin-delayed deep deterministic policy gradient (TD3). First, assuming industrial automation system with multiple actuators, the signal-to-interference-plus-noise ratio (SINR) and achievable rate in FBL regime are identified for each actuator in terms of the phase shift configuration matrix at the RIS. The channel state information (CSI) variations due to feedback delay are also considered that result in channel coefficients’ obsolescence. Then, the problem framework is proposed where the objective is to maximize the total achievable FBL rate in all ACs, subject to the practical phase shift constraint at the RIS elements. Since the problem is intractable to solve using conventional optimization methods, we resort to employing an actor-critic policy gradient DRL algorithm based on TD3, which relies on interacting RIS with FA environment by taking actions which are the phase shifts at the RIS elements, to maximize the expected observed reward, which is defined as the total FBL rate. The numerical results show that optimizing the practical phase shifts in the RIS via the proposed TD3 method is highly beneficial to improve the network total FBL rate in comparison with typical DRL methods

Joint active-passive beamforming and user association in IRS-assisted mmWave cellular networks

Abstract Intelligent reflecting surfaces (IRSs) are a promising technology for future-generation wireless networks by extending coverage region to blind spots and increasing mmWave propagation paths in non-line of sight environments. User association (UA) in dense millimeter wave (mmWave) networks is vital to characterizing connections among base stations (BSs) and mobile users for load balancing, interference management, and maximizing network utility. However, it has yet to be examined thoroughly in a multi-IRS-aided network. This paper presents a new UA scheme that takes cell interference into account for a multi-cell mmWave cellular network aided with multiple IRSs. We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming (AB) at the BSs, passive beamforming (PB) at the IRSs, and user-BS association with consideration of the impact of IRSs. We then propose a computationally efficient iterative algorithm based on alternating optimization (AO) to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the IRSs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to reach the optimal solution. Simulation results show significant performance improvements obtained by the proposed algorithm, providing higher spectral efficiency compared to several benchmark algorithms, while having a low computational complexity