This thesis explores the optimization of energy efficiency (EE) and practical configuration overhead in wireless communication systems, focusing on both the user equipment (UE) and the base station (BS).

The first part of the study examines minimizing the UE's energy consumption when transmitting short data payloads via a BS-controlled reconfigurable intelligent surface (RIS). To balance the energy cost of the additional pilot signals needed to configure the RIS against the energy savings during data transmission, we propose dividing the RIS into controllable subarrays. This yields a unique energy-minimizing configuration determined by payload size and path loss conditions. Building on the practical challenges of RIS implementation, we then address the wireless control signaling overhead required to feed back these phase-shift configurations to the RIS. We propose a novel quantization codebook that guarantees minimal signal-to-noise ratio (SNR) loss while reducing feedback overhead from linear to logarithmic scaling with the number of RIS elements, and we introduce an efficient differential feedback scheme for mobility scenarios.

The second part focuses on the EE of multi-antenna BSs using an active and passive transceiver model. By jointly optimizing transmit power, bandwidth, and the number of antennas, we derive novel closed-form solutions that uncover a fundamental relationship between radiated power and passive circuitry consumption. This optimization framework is extended to incorporate Quality-of-Service constraints and advanced sleep modes, leading to a dynamic scheduling algorithm that optimizes hardware configurations to minimize total energy consumption for bursty traffic.

Finally, the thesis investigates the fundamental EE limits of a dual-band BS site combining a coverage-oriented sub-6 GHz carrier with a high-bandwidth, capacity-oriented secondary carrier. By jointly optimizing hardware parameters and sleep-mode activity across both bands, we identify four distinct operational regions that govern EE-optimal behavior. We derive analytical thresholds dictating precisely when the secondary capacity band should awaken from sleep and how much traffic it should carry, demonstrating that its utilization becomes EE-optimal only when the power cost of the bandwidth-limited primary band exceeds the static power penalty of activating the secondary circuitry.

Together, these studies provide a comprehensive view of EE optimization and resource management, offering novel theoretical insights and actionable design guidelines for UE and BS configurations, RIS control signaling, sleep-mode management, and multi-band integration.

Denna avhandling utforskar optimeringen av energieffektivitet (EE) och den signaleringsoverhead som krävs i trådlösa kommunikationssystem, med fokus på både användaren (UE) och basstationen (BS).

Den första delen av studien undersöker hur energiförbrukningen hos en UE kan minimeras vid överföring av små datamängder via en rekonfigurerbar intelligent yta (RIS) styrd av BS. För att balansera energikostnaden för de extra pilotsignaler som krävs för att konfigurera ytan mot energibesparingarna under dataöverföringen föreslår vi att RIS:ens element delas in i styrbara grupper (subarrays). Detta ger en unik energiminimerande konfiguration som bestäms av datamängdens storlek och radiokanalens dämpning. Med utgångspunkt i de praktiska utmaningarna vid RIS-implementering behandlar vi därefter den trådlösa kontrollsignalering som krävs för att återkoppla dessa faskonfigurationer till RIS:en. Vi föreslår en ny kvantiseringskodbok som garanterar en minimal förlust i signal-brusförhållande (SNR) samtidigt som signaleringsoverheaden reduceras från att skala linjärt till att skala logaritmiskt med antalet RIS-element. Dessutom introducerar vi ett effektivt differentiellt kontrollsignaleringsschema för mobilitetsscenarier.

Den andra delen fokuserar på EE hos basstationer med flera antenner, med hjälp av en effektförbrukningsmodell som tar hänsyn till sändarens aktiva och passiva komponenter. Genom att gemensamt optimera sändareffekt, bandbredd och antal antenner härleder vi nya analytiska lösningar som visar ett samband mellan utstrålad effekt och passiv kretsförbrukning. Detta optimeringsramverk utvidgas vidare till att omfatta tjänstekvalitetskrav (QoS) och avancerade vilolägen, vilket leder till en dynamisk schemaläggningsalgoritm som anpassar hårdvarukonfigurationen för att minimera den totala energiförbrukningen vid skurvis trafik.

Slutligen undersöker avhandlingen de grundläggande gränserna för energieffektivitet hos en tvåbandsbasstation som kombinerar en täckningsorienterad primär bärvåg under 6 GHz med en kapacitetsorienterad sekundär bärvåg med hög bandbredd. Genom att gemensamt optimera hårdvaruparametrar och vilolägenas aktivitet över de båda banden identifierar vi fyra driftsområden för EE överföring. Vi härleder analytiska tröskelvärden som exakt anger när det sekundära kapacitetsbandet bör väckas ur sitt viloläge, och visar att aktiveringen är optimal ur energieffektivitetssynpunkt först när kostnaden att tvinga ytterligare trafik genom det bandbreddsbegränsade primära bandet överstiger den statiska effektkostnaden för att aktivera det sekundära bandet.

Sammantaget ger dessa studier en samlad bild av energieffektiv optimering och resurshantering. De bidrar med nya teoretiska insikter och praktiska riktlinjer för utformningen av BS-konfigurationer, RIS-kontrollsignalering, hantering av vilolägen och multibandsintegration.

As 6G wireless networks transition toward sub-Terahertz (sub-THz) frequencies to satisfy extreme capacity demands, managing the trade-off between massive bandwidth and power consumption becomes a critical design challenge. In this paper, we investigate the fundamental energy efficiency (EE) limits of a dual-band base station site combining a coverage-oriented sub-6 GHz carrier with a capacity-oriented sub-THz carrier. By jointly optimizing hardware parameters and advanced sleep modes via activity factors, we identify four distinct operational regions that govern the EE-optimal behavior across the complete range of data rates. We derive closed-form analytical thresholds that dictate precisely when the sub-THz band should awaken from sleep and how to allocate traffic between the bands in that case. Our results demonstrate that utilizing the sub-THz band is EE-optimal when the power cost of the bandwidth-limited sub-6 GHz band surpasses the static power penalty of activating the sub-THz circuitry. Ultimately, this framework provides mathematically rigorous guidelines for power consumption minimization and sleep-mode management in future green networks.

Reconfigurable intelligent surfaces (RISs) can greatly improve the signal quality of future communication systems by reflecting transmitted signals toward the receiver. However, even when the base station (BS) has perfect channel knowledge and can compute the optimal RIS phase-shift configuration, implementing this configuration requires feedback signaling over a control channel from the BS to the RIS. This feedback must be kept minimal, as it is transmitted wirelessly every time the channel changes. In this paper, we examine how the feedback load, measured in bits, affects the performance of an RIS-aided system. Specifically, we investigate the trade-offs between codebook-based and element-wise feedback schemes, and how these influence the achievable signal-to-noise ratio (SNR). We propose a novel quantization codebook, tailored for line-of-sight scenarios, that guarantees minimal SNR loss while reducing feedback overhead from linear to logarithmic scaling with the number of RIS elements. We demonstrate the codebook’s usefulness over Rician fading channels and extend it to 3D channel geometries with a uniform planar array through joint quantization of elevation and azimuth angles, including scenarios with a non-zero static path. Furthermore, we also analyze the SNR impact of discrete phase shifts and implement an efficient differential feedback scheme that leverages temporal correlation for mobility scenarios. Numerical simulations and analytical analysis are performed to quantify the performance degradation caused by reduced feedback load, shedding light on how efficiently RIS configurations can be fed back in practical systems.

Reconfigurable intelligent surfaces (RISs) can greatly improve the signal quality of future communication systems by reflecting transmitted signals toward the receiver. However, even when the base station (BS) has perfect channel knowledge and can compute the optimal RIS phase-shift configuration, implementing this configuration requires feedback signaling over a control channel from the BS to the RIS. This feedback must be kept minimal, as it is transmitted wirelessly every time the channel changes. In this paper, we examine how the feedback load, measured in bits, affects the performance of an RIS-aided system. Specifically, we investigate the trade-offs between codebook-based and element-wise feedback schemes, and how these influence the signal-to-noise ratio (SNR). We propose a novel quantization codebook tailored for line-of-sight (LoS) that guarantees a minimal SNR loss using a number of feedback bits that scale logarithmically with the number of RIS elements. We demonstrate the codebook's usefulness over Rician fading channels and how to extend it to handle a non-zero static path. Numerical simulations and analytical analysis are performed to quantify the performance degradation that results from a reduced feedback load, shedding light on how efficiently RIS configurations can be fed back in practical systems.

In this paper, we examine the energy efficiency (EE) of a base station with multiple antennas. We use a state-of-theart power consumption model, taking into account the passive and active parts of the transceiver circuitry, including the effects of radiated power, signal processing, and passive consumption. The paper treats the transmit power, bandwidth, and number ofantennas as the optimization variables. We provide novel closed form solutions for the optimal ratios of power per unit bandwidth and power per transmit antenna. We present a novel algorithm that jointly optimizes these variables to achieve maximum EE, while fulfilling constraints on the variable ranges. We also discover a new relationship between the radiated power and the passive transceiver power consumption. We provide analytical insight into whether using maximum power or bandwidth is optimal.

In this paper, we examine the energy efficiency (EE) of a base station (BS) with multiple antennas. We use a state-of-the-art power consumption model, taking into account the passive and active parts of the transceiver circuitry, including the effects of radiated power, signal processing, and passive consumption. The paper treats the transmit power, bandwidth, and number of antennas as the optimization variables. We provide novel closed-form solutions for the optimal ratios of power per unit bandwidth and power per transmit antenna. We present a novel algorithm that jointly optimizes these variables to achieve maximum EE, while fulfilling constraints on the variable ranges. We also discover a new relationship between the radiated power and the passive transceiver power consumption. We provide analytical insight into whether using maximum power or bandwidth is optimal and how many antennas a BS should utilize.

This thesis explores the optimization of energy efficiency (EE) in the radiolink of wireless communication systems, focusing on both the user equipment (UE) and the base station (BS). The first part of the study examines strategies to minimize the energy consumption of the UE when transmitting short data payloads, utilizing a reconfigurable intelligent surface (RIS) controlled by the BS, to improve the channel conditions. The challenge lies in balancing the increased energy consumption due to additional pilot signals needed toconfigure the RIS against the energy savings during data transmission. We propose an innovative approach where the RIS is divided into subarrays of controllable sizes to shorten the pilot length. The analytical results provide a unique energy-minimizing solution in terms of pilot length and power which depends on an interplay between the payload size and path loss conditions between the UE, BS, and RIS. In the second part, the focus shifts to the EE of a multi-antenna BS. A comprehensive power consumption model is employed, accounting for both active and passive components of the transceiver circuitry. By treating the transmit power, bandwidth, and number of antennas as optimization variables, we derive novel closed-form solutions to the optimal value of these variables and propose an algorithm for their joint optimization. This part of the study not only optimizes the variables for maximum EE but also uncovers a new relationship between radiated power and passive transceiver power consumption, offering insights into the trade-offs between using maximum power and bandwidth. Together, these studies provide an updated view of EE optimization in wireless communication systems, offering novel theoretical insights for both UE and BS configurations.

Denna avhandling utforskar optimeringen av energieffektivitet (EE) i trådlösa kommunikationssystem, med fokus på både användarenheten (UE) och basstationen (BS). Den första delen av studien undersöker strategier för att minimera energiförbrukningen hos UEn vid överföring av korta datapaket, genom att använda en rekonfigurerbar intelligent yta (RIS) som styrs av basstationen för att förbättra radiokanalen. Utmaningen ligger i att balansera den ökade energiförbrukningen på grund av de ytterligare pilotsignalerna som krävs för att konfigurera en RIS mot energibesparingarna som sker underdataöverföringen. Vi föreslår ett tillvägagångssätt där en RIS delas upp i delarrayer av kontrollerbar storlek för att förkorta pilotlängden. De analytiska resultaten ger en unik lösning för minimering av energikonsumptionen i termer av pilotlängd och pilotenergi och beror på paketstorleken och de relativastyrkeförhållandena i radiokanalen mellan UE, BS och RIS. I den andra delen flyttas fokus till EE hos en basstation med flera antenner. En omfattande modell för effektförbrukning som tar hänsyn till både aktiva och passiva komponenter i transceivermodulen används. Genom attbehandla utsänd effekt, bandbredd och antalet antenner som optimeringsvariabler, härleder vi nya lösningar för det optimala värdet av dessa variabler och vi föreslår en algoritm för deras gemensamma optimering. Denna del av studien optimerar inte bara variablerna för maximal EE, utan avslöjar också en ny relation mellan utstrålad effekt och passiv effektförbrukning i transceivern, vilket leder till nya insikter kring avvägningarna mellan att använda maximal effekt och bandbredd. Tillsammans ger dessa studier en uppdaterad syn på energioptimering i trådlösa kommunikationssystem, och nya teoretiska insikter för både UE- och basstationskonfigurationer.

This paper examines how to minimize the energy consumption of a user equipment (UE) when transmitting short data payloads. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS), which requires additional pilot signals to be configured, to improve the channel conditions. The challenge is that the pilot signals increase the energy consumption and must be balanced against energy savings during data transmission. We derive a formula for the energy consumption, including both pilot and data transmission powers and the effects of imperfect channel state information and discrete phase-shifts. To shorten the pilot length, we propose dividing the RIS into subarrays of multiple elements using the same reflection coefficient. The pilot power and subarray size are tuned to the payload length to minimize the energy consumption. Analytical results show that there exists a unique energy-minimizing solution. For small payloads and when the direct path loss between the BS and UE is weak compared to the path loss via the RIS, the solution is using subarrays with many elements and low pilot power and vice versa. The optimal percentage of energy spent on pilot signaling is in the order of 10-40%.

In this paper, we examine the energy consumption of a user equipment (UE) when it transmits a finite-sized data packet. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS) that can be utilized to improve the channel conditions, if additional pilot signals are transmitted to configure the RIS. We derive a formula for the energy consumption taking both the pilot and data transmission powers into account. By dividing the RIS into subarrays consisting of multiple RIS elements using the same reflection coefficient, the pilot overhead can be tuned to minimize the energy consumption while maintaining parts of the aperture gain. Our analytical results show that there exists an energy-minimizing subarray size. For small data blocks and when the channel conditions between the BS and UE are favorable compared to the path to the RIS, the energy consumption is minimized using large subarrays. When the channel conditions to the RIS are better and the data blocks are large, it is preferable to use fewer elements per subarray and potentially configure the elements individually.

In this paper, we examine the energy efficiency (EE) of a base station (BS) with multiple antennas. We use a state-of-the-art power consumption (PC) model that captures the passive and active parts of the transceiver circuitry, including the effects of radiated power, signal processing, and passive consumption.The paper treats the transmit power, bandwidth, and number of antennas as the optimization variables. We provide novel closed-form solutions for the optimal ratios of power per unit bandwidth and power per transmit antenna, and discover a new relationshipin which the radiated power equals the total transceiver powerat the EE-optimal operating point. A central finding is that the EE-optimal signal-to-noise ratio (SNR) collapses to a universal numerical constant of approximately 5.93 dB, independent of channel and hardware parameters. We present an algorithm that jointly optimizes the three design variables to achieve maximum EE under practical constraints, and provide analytical insight into whether maximum power or maximum bandwidth is optimal and how many antennas a BS should utilize. We further extend the optimization framework to incorporate quality-of-service(QoS) requirements and three advanced sleep modes of varying depth: absolute sleep, deep sleep, and idle mode. We characterize the optimal hardware configuration for each mode and determine when the rush-to-sleep strategy, which transmits briefly at the EE-optimal active configuration and sleeps the rest of the time, is optimal. Incorporating wake-up transition delays, we reveal how latency constraints and sleep-mode-specific transition times jointly dictate the optimal sleep mode for data packets with absolute deadlines. Together, these results indicate that energy-efficient operation requires treating transmission and sleep as a single coupled optimization.