19 research outputs found
Integrerad avkänning och kommunikation i cellfri massiv MIMO
No full textFuture mobile networks are anticipated to not only enhance communication performance but also facilitate new sensing-based applications. This highlights the essential role of integrated sensing and communication (ISAC) in sixth-generation (6G) and beyond mobile networks. The seamless integration of sensing and communication poses challenges in deployment and resource allocation. Cell-free massive multiple-input multiple-output (MIMO) networks, characterized by multiple distributed access points, offer a promising infrastructure for ISAC implementation. However, the effective realization of ISAC necessitates joint design and resource allocation optimization. In this thesis, we study ISAC within cell-free massive MIMO systems, with a particular emphasis on developing power allocation algorithms under various scenarios. In this thesis, we explore two scenarios: utilizing existing communication signals and incorporating additional sensing signals. We propose power allocation algorithms aiming to maximize the sensing performance while meeting communication and power constraints. In addition, we develop two maximum a posteriori ratio test (MAPRT) target detectors under clutter-free and cluttered scenarios. Results indicate that employing additional sensing signals enhances sensing performance, particularly in scenarios where the target has low reflectivity. Moreover, although the clutter-aware detector requires more advanced processing, it leads to better sensing performance. Furthermore, we introduced sensing spectral efficiency (SE) to measure the effect of resource block utilization, highlighting the integration advantages of ISAC over orthogonal resource sharing approaches. In the next part of the thesis, we study the energy efficiency aspects of ISAC in cell-free massive MIMO systems with ultra-reliable low-latency communications (URLLC) users. We propose a power allocation algorithm aiming to maximize energy efficiency of the system while meeting communication and sensing requirements. We conduct a comparative analysis between the proposed power allocation algorithms and a URLLC-only approach which takes into account only URLLC and power requirements. The results reveal that while the URLLC-only algorithm excels in energy efficiency, it is not able to support sensing requirements. Moreover, we study the impact of ISAC on end-to-end (including radio and processing) energy consumption. Particularly, we present giga-operations per second (GOPS) analysis for both communication and sensing tasks. Two optimization problems are formulated and solved to minimize transmission and end-to-end energy through blocklength and power optimization. Results indicate that while end-to-end energy minimization offers substantial energy savings, its efficacy diminishes with sensing integration due to processing energy requirements.Framtida mobila nätverk förväntas inte bara förbättra kommunikations-prestanda utan även mögliggöra nya applikationer baserade på sensorer. Dettaunderstryker den avgörande rollen för Integrerad avkänning och kommunika-tion (ISAC) i sjätte generationens (6G) och efterföljande mobila nätverk. Densömlösa integrationen av sensorer och kommunikation medför utmaningar iutrullning och resursallokering. Cellfria massiva flerantennsystem (MIMO-nätverk), kännetecknade av flera distribuerade åtkomstpunkter, erbjuder enlovande infrastruktur för implementering av ISAC. Dock kräver den effektivarealiseringen av ISAC samverkande design och optimering av resursallokering.I denna avhandling studerar vi ISAC inom cellfria massiva MIMO-system,med särskild tonvikt på att utveckla effektallokeringsalgoritmer under olikascenarier.Vi utforskar två scenarier: att utnyttja befintliga kommunikationssignaleroch att inkludera ytterligare sensorssignaler. Vi föreslår effektallokeringsalgo-ritmer med målet att maximera sensorsprestandan samtidigt som kommunika-tions och effektbegränsningar uppfylls. Dessutom utvecklar vi två detektorerbaserade på maximum a posteriori ratio test (MAPRT) under störningsfriaoch störda scenarier. Resultaten visar att användning av ytterligare sensors-signaler förbättrar sensorsprestandan, särskilt i scenarier där målet har lågreflektivitet. Dessutom, även om den störkänsliga detektorn kräver mer avan-cerad bearbetning, leder den till bättre sensorsprestanda. Vidare introducerarvi sensorerspektral effektivitet (SE) för att mäta effekten av resursblocksan-vändning och framhäva integrationsfördelarna med ISAC över ortogonala re-sursdelningsmetoder.I den andra delen av avhandlingen studerar vi energieffektivitetsaspek-terna av ISAC i cellfria massiva MIMO-system med användare med ultra-tillförlitlig låg-latens (URLLC) kommunikation. Vi föreslår en effektalloke-ringsalgoritm med syfte att maximera systemets energieffektivitet samtidigtsom kommunikations- och sensorskraven uppfylls. Vi utför en jämförande ana-lys mellan de föreslagna effektallokeringsalgoritmerna och ett URLLC-ensamttillvägagångssätt som tar hänsyn enbart till URLLC- och effektkrav. Resul-taten avslöjar att medan URLLC-ensamma algoritmen utmärker sig i energi-effektivitet, kan den inte stödja sensorskraven. Dessutom studerar vi effektenav ISAC på slut till slut (inklusive radios och bearbetning) energiförbruk-ning. Särskilt presenterar vi giga-operationer per sekund (GOPS) analys förbåde kommunikations- och sensorsuppgifter. Två optimeringsproblem formu-leras och löses för att minimera överförings- och slut till slut energi genomblocklängd- och effektoptimering. Resultaten indikerar att medan slut till slutenergiminimering erbjuder betydande energibesparingar, minskar dess effek-tivitet med sensorintegrationen på grund av bearbetningsenergikrav.QC 20240513</p
Integrerad avkänning och kommunikation i cellfri massiv MIMO
No full textFuture mobile networks are anticipated to not only enhance communication performance but also facilitate new sensing-based applications. This highlights the essential role of integrated sensing and communication (ISAC) in sixth-generation (6G) and beyond mobile networks. The seamless integration of sensing and communication poses challenges in deployment and resource allocation. Cell-free massive multiple-input multiple-output (MIMO) networks, characterized by multiple distributed access points, offer a promising infrastructure for ISAC implementation. However, the effective realization of ISAC necessitates joint design and resource allocation optimization. In this thesis, we study ISAC within cell-free massive MIMO systems, with a particular emphasis on developing power allocation algorithms under various scenarios. In this thesis, we explore two scenarios: utilizing existing communication signals and incorporating additional sensing signals. We propose power allocation algorithms aiming to maximize the sensing performance while meeting communication and power constraints. In addition, we develop two maximum a posteriori ratio test (MAPRT) target detectors under clutter-free and cluttered scenarios. Results indicate that employing additional sensing signals enhances sensing performance, particularly in scenarios where the target has low reflectivity. Moreover, although the clutter-aware detector requires more advanced processing, it leads to better sensing performance. Furthermore, we introduced sensing spectral efficiency (SE) to measure the effect of resource block utilization, highlighting the integration advantages of ISAC over orthogonal resource sharing approaches. In the next part of the thesis, we study the energy efficiency aspects of ISAC in cell-free massive MIMO systems with ultra-reliable low-latency communications (URLLC) users. We propose a power allocation algorithm aiming to maximize energy efficiency of the system while meeting communication and sensing requirements. We conduct a comparative analysis between the proposed power allocation algorithms and a URLLC-only approach which takes into account only URLLC and power requirements. The results reveal that while the URLLC-only algorithm excels in energy efficiency, it is not able to support sensing requirements. Moreover, we study the impact of ISAC on end-to-end (including radio and processing) energy consumption. Particularly, we present giga-operations per second (GOPS) analysis for both communication and sensing tasks. Two optimization problems are formulated and solved to minimize transmission and end-to-end energy through blocklength and power optimization. Results indicate that while end-to-end energy minimization offers substantial energy savings, its efficacy diminishes with sensing integration due to processing energy requirements.Framtida mobila nätverk förväntas inte bara förbättra kommunikations-prestanda utan även mögliggöra nya applikationer baserade på sensorer. Dettaunderstryker den avgörande rollen för Integrerad avkänning och kommunika-tion (ISAC) i sjätte generationens (6G) och efterföljande mobila nätverk. Densömlösa integrationen av sensorer och kommunikation medför utmaningar iutrullning och resursallokering. Cellfria massiva flerantennsystem (MIMO-nätverk), kännetecknade av flera distribuerade åtkomstpunkter, erbjuder enlovande infrastruktur för implementering av ISAC. Dock kräver den effektivarealiseringen av ISAC samverkande design och optimering av resursallokering.I denna avhandling studerar vi ISAC inom cellfria massiva MIMO-system,med särskild tonvikt på att utveckla effektallokeringsalgoritmer under olikascenarier.Vi utforskar två scenarier: att utnyttja befintliga kommunikationssignaleroch att inkludera ytterligare sensorssignaler. Vi föreslår effektallokeringsalgo-ritmer med målet att maximera sensorsprestandan samtidigt som kommunika-tions och effektbegränsningar uppfylls. Dessutom utvecklar vi två detektorerbaserade på maximum a posteriori ratio test (MAPRT) under störningsfriaoch störda scenarier. Resultaten visar att användning av ytterligare sensors-signaler förbättrar sensorsprestandan, särskilt i scenarier där målet har lågreflektivitet. Dessutom, även om den störkänsliga detektorn kräver mer avan-cerad bearbetning, leder den till bättre sensorsprestanda. Vidare introducerarvi sensorerspektral effektivitet (SE) för att mäta effekten av resursblocksan-vändning och framhäva integrationsfördelarna med ISAC över ortogonala re-sursdelningsmetoder.I den andra delen av avhandlingen studerar vi energieffektivitetsaspek-terna av ISAC i cellfria massiva MIMO-system med användare med ultra-tillförlitlig låg-latens (URLLC) kommunikation. Vi föreslår en effektalloke-ringsalgoritm med syfte att maximera systemets energieffektivitet samtidigtsom kommunikations- och sensorskraven uppfylls. Vi utför en jämförande ana-lys mellan de föreslagna effektallokeringsalgoritmerna och ett URLLC-ensamttillvägagångssätt som tar hänsyn enbart till URLLC- och effektkrav. Resul-taten avslöjar att medan URLLC-ensamma algoritmen utmärker sig i energi-effektivitet, kan den inte stödja sensorskraven. Dessutom studerar vi effektenav ISAC på slut till slut (inklusive radios och bearbetning) energiförbruk-ning. Särskilt presenterar vi giga-operationer per sekund (GOPS) analys förbåde kommunikations- och sensorsuppgifter. Två optimeringsproblem formu-leras och löses för att minimera överförings- och slut till slut energi genomblocklängd- och effektoptimering. Resultaten indikerar att medan slut till slutenergiminimering erbjuder betydande energibesparingar, minskar dess effek-tivitet med sensorintegrationen på grund av bearbetningsenergikrav.QC 20240513</p
Distributed Versus Centralized Sensing in Cell-Free Massive MIMO
No full textThis letter investigates single-target detection in an integrated sensing and communication (ISAC) system, implemented within a cell-free massive multiple-input multiple-output (MIMO) setup, based on a cloud radio access network (C-RAN) architecture. Unlike previous centralized approaches where sensing is processed in the central cloud, we propose a distributed approach where sensing partially occurs at the receive access points (APs). We consider two scenarios based on the knowledge available at receive APs: i) fully-informed, with complete access to transmitted signal information, and ii) partly-informed, with access only to transmitted signal statistics. We introduce a maximum a posteriori ratio test detector for both distributed sensing scenarios and assess the signaling load for sensing. The fully-informed scenario's performance aligns with the centralized approach in terms of target detection probability. However, the partly-informed scenario requires an additional 13 dBsm variance on the target's radar cross section (RCS) for a detection probability above 0.9. Distributed sensing significantly reduces signaling load, especially in the partly-informed scenario, achieving a 70% reduction under our system setup.This work was supported in part by the ECSEL Joint Undertaking (JU) under Grant 876124; in part by the CELTIC-NEXT Project, and in part by the Robust and AI Native 6G for Green Networks (RAI- 6Green) under Grant 2020-1.2.3-EUREKA-2021-000006. The JU is supported by EU Horizon 2020 and Vinnova in Sweden, while RAI-6Green is funded by Vinnova, the Swedish Innovation Agency. The work of OEzlem Tugfe Demir was supported by the Scientific and Technological Research Council of Turkiye.ECSEL Joint Undertaking (JU) [876124]; CELTIC-NEXT Project; Robust and AI Native 6G for Green Networks (RAI- 6Green) [2020-1.2.3-EUREKA-2021-000006]; EU Horizon 2020; Vinnova in Sweden; Vinnova; Swedish Innovation Agency; Scientific and Technological Research Council of Turkiy
Distributed Versus Centralized Sensing in Cell-Free Massive MIMO
No full textThis letter investigates single-target detection in an integrated sensing and communication (ISAC) system, implemented within a cell-free massive multiple-input multiple-output (MIMO) setup, based on a cloud radio access network (C-RAN) architecture. Unlike previous centralized approaches where sensing is processed in the central cloud, we propose a distributed approach where sensing partially occurs at the receive access points (APs). We consider two scenarios based on the knowledge available at receive APs: i) fully-informed, with complete access to transmitted signal information, and ii) partly-informed, with access only to transmitted signal statistics. We introduce a maximum a posteriori ratio test detector for both distributed sensing scenarios and assess the signaling load for sensing. The fully-informed scenario's performance aligns with the centralized approach in terms of target detection probability. However, the partly-informed scenario requires an additional 13 dBsm variance on the target's radar cross section (RCS) for a detection probability above 0.9. Distributed sensing significantly reduces signaling load, especially in the partly-informed scenario, achieving a 70% reduction under our system setup.</p
Joint Processing and Transmission Energy Optimization for ISAC in Cell-Free Massive MIMO with URLLC
No full textIn this paper, we explore the concept of integrated sensing and communication
(ISAC) within a downlink cell-free massive MIMO (multiple-input
multiple-output) system featuring multi-static sensing and users requiring
ultra-reliable low-latency communications (URLLC). Our focus involves the
formulation of two non-convex algorithms that jointly solve power and
blocklength allocation for end-to-end (E2E) minimization. The objectives are to
jointly minimize sensing/communication processing and transmission energy
consumption, while simultaneously meeting the requirements for sensing and
URLLC. To address the inherent non-convexity of these optimization problems, we
utilize techniques such as the Feasible Point Pursuit - Successive Convex
Approximation (FPP-SCA), Concave-Convex Programming (CCP), and fractional
programming. We conduct a comparative analysis of the performance of these
algorithms in ISAC scenarios and against a URLLC-only scenario where sensing is
not integrated. Our numerical results highlight the superior performance of the
E2E energy minimization algorithm, especially in scenarios without sensing
capability. Additionally, our study underscores the increasing prominence of
energy consumption associated with sensing processing tasks as the number of
sensing receive access points rises. Furthermore, the results emphasize that a
higher sensing signal-to-interference-plus-noise ratio threshold is associated
with an escalation in E2E energy consumption, thereby narrowing the performance
gap between the two proposed algorithms.Comment: 13 pages, 8 figures. arXiv admin note: text overlap with
arXiv:2401.1013
Interplay Between Sensing and Communication in Cell-Free Massive MIMO with URLLC Users
No full textThis paper studies integrated sensing and communication (ISAC) in the downlink of a cell-free massive multiple-input multiple-output (MIMO) system with multi-static sensing and ultra-reliable low-latency communication (URLLC) users. We propose a successive convex approximation-based power allocation algorithm that maximizes energy efficiency while satisfying the sensing and URLLC requirements. In addition, we provide a new definition for network availability, which accounts for both sensing and URLLC requirements. The impact of blocklength, sensing requirement, and required reliability as a function of decoding error probability on network availability and energy ef-ficiency is investigated. The proposed power allocation algorithm is compared to a communication-centric approach where only the URLLC requirement is considered. It is shown that the URLLC-only approach is incapable of meeting sensing requirements, while the proposed ISAC algorithm fulfills both sensing and URLLC requirements, albeit with an associated increase in energy consumption. This increment can be reduced up to 75% by utilizing additional symbols for sensing. It is also demonstrated that larger blocklengths enhance network availability and offer greater robustness against stringent reliability requirements.</p
Interplay between Sensing and Communication in Cell-Free Massive MIMO with URLLC Users
No full textThis paper studies integrated sensing and communication (ISAC) in the
downlink of a cell-free massive multiple-input multiple-output (MIMO) system
with multi-static sensing and ultra-reliable low-latency communication (URLLC)
users. We propose a successive convex approximation-based power allocation
algorithm that maximizes energy efficiency while satisfying the sensing and
URLLC requirements. In addition, we provide a new definition for network
availability, which accounts for both sensing and URLLC requirements. The
impact of blocklength, sensing requirement, and required reliability as a
function of decoding error probability on network availability and energy
efficiency is investigated. The proposed power allocation algorithm is compared
to a communication-centric approach where only the URLLC requirement is
considered. It is shown that the URLLC-only approach is incapable of meeting
sensing requirements, while the proposed ISAC algorithm fulfills both sensing
and URLLC requirements, albeit with an associated increase in energy
consumption. This increment can be reduced up to 75% by utilizing additional
symbols for sensing. It is also demonstrated that larger blocklengths enhance
network availability and offer greater robustness against stringent reliability
requirements.Comment: 6 pages, 3 figure
Joint Processing and Transmission Energy Optimization for ISAC in Cell-Free Massive MIMO with URLLC
No full textVocational rehabilitation programs for the adult legally blinded in Alexanderia, United Arab Republic compared with programs available for the adult legally blinded in Boston, Massachusetts
No full textThesis (M.S.)--Boston UniversityPLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at [email protected]. Thank you.2999-01-0
