Transceiver optimization for energy-efficient multiantenna cellular networks

Abstract This thesis focuses on the timely problem of energy-efficient transmission for wireless multiantenna cellular systems. The emphasis is on transmit beamforming (BF) and active antenna set optimization to maximize the network-wide energy efficiency (EE) metric, i.e., the number of transmitted bits per energy unit. The fundamental novelty of EE optimization is that it incorporates the transceivers' processing power in addition to the actual transmit power in the BF design. The key features of the thesis are that it focuses on sophisticated power consumption models (PCMs), giving useful insights into the EE of current cellular systems in particular, and provides mathematical tools for EE optimization in future wireless networks generally. The BF problem is first studied in a multiuser multiple-input single-output system by using a PCM scaling with transmit power and the number of active radio frequency (RF) chains. To find the best performance, a globally optimal solution based on a branch-reduce-and-bound (BRB) method is proposed, and two efficient designs based on zero-forcing and successive convex approximation (SCA) are derived for practical applications. Next, joint BF and antenna selection (JBAS) is studied, which can switch off some RF chains for further EE improvements. An optimal BRB method and efficient SCA-based algorithms exploiting continuous relaxation (CR) or sparse BF are proposed to solve the resulting mixed-Boolean nonconvex problem (MBNP). In a multi-cell system, energy-efficient coordinated BF is explored under two optimization targets: 1) the network EE maximization and 2) the weighted sum EEmax (WsumEEmax). A more sophisticated PCM scaling also with the data rate and the associated computational complexity is assumed. The SCA-based methods are derived to solve these problems in a centralized manner, and distributed algorithms relying only on the local channel state information and limited backhaul signaling are then proposed. The WsumEEmax problem is solved using SCA combined with an alternating direction method of multipliers, and iterative closed-form algorithms having easily derivable computational complexity are developed to solve both problems. The work is subsequently extended to a multi-cell multigroup multicasting system, where user groups request multicasting data. For the MBNP, a modeling method to improve the performance of the SCA for solving the CR is proposed, aiming at encouraging the relaxed Boolean variables to converge at the binary values. A second approach based on sparse BF, which introduces no Boolean variables, is also derived. The methods are then modified to solve the EE and sum rate trade-off problem. Finally, the BF design with multiantenna receivers is considered, where the users can receive both unicasting and multicasting data simultaneously. The performances of the developed algorithms are assessed via thorough computer simulations. The results show that the proposed algorithms provide 30-300% EE improvements over various conventional methods in the BF optimization, and that JBAS techniques can offer further gains of more than 100%.Tiivistelmä Tämä väitöskirja keskittyy ajankohtaiseen energiatehokkaaseen lähetinsuunnitteluun langattomissa solukkoverkoissa, joissa suorituskykymittarina käytetään energiatehokkuuden (energy efficiency (EE)) maksimointia, eli kuinka monta bittiä pystytään lähettämään yhtä energiayksikköä kohti. Työn painopiste on lähettimien keilanmuodostuksen (beamforming (BF)) ja aktiivisten lähetinantennien optimoinnissa. EE-optimoinnin uutuusarvo on ottaa lähettimien prosessoinnin tehonkulutus huomioon keilanmuodostuksen suunnittelussa, varsinaisen lähetystehon lisäksi. Työ antaa hyvän käsityksen erityisesti tämänhetkisten solukkoverkkojen energiatehokkuudesta, ja luo työkaluja EE-optimointiin tulevaisuuden järjestelmissä. Ensin suunnitellaan keilanmuodostus yksisolumallissa, jossa tehonkulutus kasvaa lähetystehon ja aktiivisten radiotaajuusketjujen lukumäärän mukana. Ongelmaan johdetaan optimaalinen ratkaisu, ja kaksi käytännöllistä menetelmää perustuen nollaanpakotukseen tai peräkkäinen konveksi approksimaatio (successive convex approximation (SCA)) -ideaan. Seuraavaksi keskitytään keilanmuodostuksen ja antenninvalinnan yhteisoptimointiin (joint beamforming and antenna selection (JBAS)), jossa radiotaajuusketjuja voidaan sulkea EE:n parantamiseksi. Tähän ehdotetaan optimaalinen menetelmä ja kaksi käytännöllistä SCA-menetelmää perustuen binääristen ja jatkuvien muuttujien yhteisoptimointiongelman relaksaatioon, tai harvan vektorin optimointiin. Monisoluverkon EE-optimoinnissa käytetään yksityiskohtaisempaa tehonkulutusmallia, joka skaalautuu myös datanopeuden ja prosessoinnin monimutkaisuuden mukaan. Työssä käytetään kahta suorituskyvyn mittaria: 1) koko verkon energiatehokkuuden, ja 2) painotettujen energiatehokkuuksien summien maksimointia (weighted sum EEmax (WsumEEmax)). Ensin johdetaan keskitetyt ratkaisut SCA-ideaa käyttäen. Tämän jälkeen keskitytään hajautettuun optimointiin, joka pystytään toteuttamaan paikallisen kanavatiedon avulla, kun matalanopeuksinen skalaariarvojen jako on käytettävissä tukiasemien välillä. Ensin WsumEEmax-ongelma ratkaistaan yhdistämällä SCA ja kerrointen vaihtelevan suunnan menetelmä, ja lisäksi ehdotetaan iteratiivinen suljetun muodon ratkaisu molempiin ongelmiin, joka mahdollistaa tarkan laskennallisen monimutkaisuuden määrityksen. Lopussa työ laajennetaan monisoluverkkoon, jossa tukiasemat palvelevat käyttäjäryhmiä ryhmälähetyksenä. Keskittymällä JBAS-ongelmaan, ensin ehdotetaan lähestymistapa parantaa SCA-menetelmän suorituskykyä yhteisoptimointiongelman relaksaation ratkaisemisessa. Toinen yksinkertaisempi lähestymistapa perustuu harvan vektorin optimointiin, joka ei vaadi binäärisiä muuttujia. Lisäksi menetelmiä muunnellaan myös energiatehokkuuden ja summadatanopeuden kompromissin optimointiin. Lopussa työ ottaa huomioon vielä moniantennivastaanottimet, joka mahdollistaa sekä täsmälähetyksen että ryhmälähetyksen samanaikaisesti. Menetelmien suorituskykyä arvioidaan laajamittaisilla tietokonesimulaatioilla. Tulokset näyttävät väitöskirjan menetelmien lisäävän energiatehokkuutta 30-300% verrattuna lukuisiin perinteisiin menetelmiin BF-optimoinnissa, ja JBAS-menetelmät antavat vielä yli 100% lisää suorituskykyä

Effective channel state acquisition in multi-cell multi-user MIMO system

In a cellular network with small cells, where all the communication resources are shared, the inter-cell interference becomes a limiting factor of performance. The strategies for mitigating the inter-cell interference has been quite extensively studied lately. One of the promising candidates is coordinated beamforming/scheduling, where a certain number of cells is allowed to cooperate such that the transmission from each cell takes into account the interference it would cause to the users of other cells. In this thesis, the performances of different signaling strategies which perform the weighted sum rate maximization in time division duplex multi-cell multi-user MIMO downlink system are studied. The strategies consist of iterative decentralized algorithms, aiming at reduced pilot signaling overhead and faster convergence. The required control information between the cells is provided via uplink reference signals and a backhaul. Uplink reference signals include sounding reference signals and busy bursts. Based on the earlier work, the strategies have now been extended to a larger cellular system in which the frequency selectivity and the uncertainty of the channel information are also taken into account. The ability of the strategies to handle the large network can be seen from the simulation results. It is shown that even when there is strong inter-cell interference, the strategies utilizing parallel cell-specific iterations offer practical convergence speed. It is also noticed that the joint optimization over many frequency blocks brings a minor improvement on the sum rate performance, meaning that it could also be utilized with the same order of computational complexity compared to the frequency flat case. Finally, the robustness of the centralized strategy to the imperfect channel state information is shown and the trade-off between the CSI uncertainty and multi-user diversity is stated.Solukkoverkossa, jossa solujen koot ovat pieniä ja kaikki käyttävät samoja taajuuksia, solujen välinen häiriö rajoittaa verkon suorituskykyä. Viime aikoina on laajasti tutkittu strategioita, joilla häiriötä saataisiin vähennettyä. Yksi lupaavista menetelmistä tähän tarkoitukseen on koordinoitu keilanmuodostus/skedulointi, jossa tietty ryhmä soluja voi koordinoida keskenään ja näin ottaa huomioon lähetyksestä aiheutuvan häiriön toisia soluja kohtaan. Tässä diplomityössä tutkitaan erilaisten painotetun summadatanopeuden maksimoivien signalointistrategioiden suorituskykyä aikajakodupleksoidussa usean solun ja käyttäjän moniantenniverkossa, jossa dataa lähetetään tukiasemasta käyttäjille. Strategiat perustuvat iteratiivisiin hajautettuihin algoritmeihin, joiden tarkoituksena on vähentää opetussignaloinnista aiheutuvaa kuormitusta ja nopeuttaa suppenemista. Kontrolli-informaation signaloimiseen verkossa käytetään käyttäjiltä tukiasemille lähetettäviä opetussignaaleja ja taustayhteyttä tukiasemien välillä. Työ perustuu aiemmin tehtyyn tutkimukseen, josta strategiat on nyt laajenettu suurempaan solukkojärjestelmään, ottaen huomioon myös taajuusselektiivisyyden ja kanavainformaation epävarmuuden vaikutukset. Simulointitulosten perusteella voidaan sanoa, että strategiat toimivat usean käyttäjän ja solun verkossa. Tuloksista nähdään, että rinnakaisia solukohtaisia iteraatioita hyödyntävillä strategioilla voidaan saavuttaa käytännöllinen suppenemisnopeus, vaikka solujen välinen häiriö on voimakasta. Taajuusselektiivisen kanavan tuloksista huomataan, että yhteisoptimointi usean taajuuslohkon yli parantaa vähän suorituskykyä verrattuna yhden taajuuden tapaukseen. Yhteisoptimointia voitaisiin siis myös hyödyntää, koska laskennallinen monimutkaisuus on samaa suuruusluokkaa verrattuna yhden taajuuden tilanteeseen. Epävarman kanavatiedon vaikutusta tutkitaan keskitetyllä optimointimenetelmällä, joka selvästi laskee suorituskykyä verrattuna täydellisen kanavan tapaukseen, mutta antaa kuitenkin selkeän parannuksen alkuperäiseen algoritmiin verrattuna. Koska opetussignaalien teho jaetaan käyttäjien kesken, tulokset näyttävät kompromissin kanavatiedon epävarmuuden ja monikäyttäjädiversiteetin välillä

Energy-efficient joint transmit beamforming and subarray selection with non-linear power amplifier efficiency

Abstract We study the problem of energy efficiency maximization (EEmax) with joint beamforming and subarray selection, by taking into account the non-linear power amplifier (PA) efficiency in a multi-user multiple-input single-output system. The subarray selection problem is formulated using the concept of perspective formulation with additional penalty term in the objective function. To tackle the resulting challenging mixed-Boolean non-convex optimization problem, we rely on continuous relaxation and successive convex approximation framework where a convex problem is solved in each iteration. Numerical results demonstrate the achieved energy efficiency gains of the subarray selection and show that non-linear PA efficiency has a significant impact on the optimization. We also observe that on contrast to using linear PA efficiency model, the non-linear PA efficiency model yields the fact that it is better to stay silent rather than transmit with very low transmit power

Energy-efficient beam coordination strategies with rate-dependent processing power

Abstract This paper proposes energy-efficient coordinated beamforming strategies for multicell multiuser multiple-input single-output system. We consider a practical power consumption model, where part of the consumed power depends on the base station or user specific data rates due to coding, decoding, and backhaul. This is different from the existing approaches where the base station power consumption has been assumed to be a convex or linear function of the transmit powers. Two optimization criteria are considered, namely network energy efficiency maximization and weighted sum energy efficiency maximization. We develop successive convex approximation-based algorithms to tackle these difficult nonconvex problems. We further propose decentralized implementations for the considered problems, in which base stations perform parallel and distributed computation based on local channel state information and limited backhaul information exchange. The decentralized approaches admit closed-form solutions and can be implemented without invoking a generic external convex solver. We also show an example of the pilot contamination effect on the energy efficiency using a heuristic pilot allocation strategy. The numerical results are provided to demonstrate that the rate dependent power consumption has a large impact on the system energy efficiency, and, thus, has to be taken into account when devising energy-efficient transmission strategies. The significant gains of the proposed algorithms over the conventional low-complexity beamforming algorithms are also illustrated

Beamforming and transceiver HW design for THz band

Abstract The new spectrum available in the millimeter-wave (mmWave) and Terahertz (THz) bands is a promising frontier for the future wireless communications. Propagation characteristics at these frequencies imply that highly directional transmissions should be used to focus the available power to a specific direction. This is enabled by using tightly packed large-scale antenna arrays to form narrow or so called pencil beams both at the transmitter and the receiver. This type of communication is, however, quite sensitive to imperfections of the transceivers, resulting in beam pointing errors and lost connection in the worst-case. This paper investigates the impact of such errors, originating from the local oscillators in terms of phase noise, which is a major impairment with high center frequencies. We explore the impact of these effects with different transceiver architectures, illustrate the beam shape properties, and quantify their impact on the system performance for different modulation schemes in terms of error rates. Specifically, we model the phase noise both as Wiener and Gaussian distributed to characterize the impact of phase noise on the beam accuracy and system performance

Multi-cell interference coordination for multigroup multicast transmission

Abstract Multicasting has become a particularly important technique in the context of cache-enabled cloud radio access networks proposed for 5G systems, where it can be used to transmit common information to multiple users to improve both spectral and energy efficiency. For the efficient spectrum utilization, the future communications are based on aggressive frequency reuse, where the required data rates can be achieved with multiple-input multiple-output precoding techniques. This approach, however, calls for advanced interference coordination techniques. This paper summarizes some of the core approaches proposed in the literature and discusses the main future challenges

Distributed optimization for coordinated beamforming in multicell multigroup multicast systems:power minimization and SINR balancing

Abstract This paper considers coordinated multicast beamforming in a multicell multigroup multiple-input single-output system. Each base station (BS) serves multiple groups of users by forming a single beam with common information per group. We propose centralized and distributed beamforming algorithms for two different optimization targets. The first objective is to minimize the total transmission power of all the BSs while guaranteeing the user-specific minimum quality-of-service targets. The semidefinite relaxation (SDR) method is used to approximate the nonconvex multicast problem as a semidefinite program (SDP), which is solvable via centralized processing. Subsequently, two alternative distributed methods are proposed. The first approach turns the SDP into a two-level optimization via primal decomposition. At the higher level, intercell interference powers are optimized for fixed beamformers, whereas the lower level locally optimizes the beamformers by minimizing BS-specific transmit powers for the given intercell interference constraints. The second distributed solution is enabled via an alternating direction method of multipliers, where the intercell interference optimization is divided into a local and a global optimization by forcing the equality via consistency constraints. We further propose a centralized and a simple distributed beamforming design for the signal-to-interference-plus-noise ratio (SINR) balancing problem in which the minimum SINR among the users is maximized with given per-BS power constraints. This problem is solved via the bisection method as a series of SDP feasibility problems. The simulation results show the superiority of the proposed coordinated beamforming algorithms over traditional noncoordinated transmission schemes, and illustrate the fast convergence of the distributed methods

Multigroup multicast beamforming and antenna selection with rate-splitting in multicell systems

Abstract This paper studies energy-efficient joint coordinated beamforming and antenna selection in multi-cell multi-user multigroup multicast multiple-input single-output systems. We focus on interference-limited scenarios, e.g., when the number of radio frequency (RF) chains is of the same order as the number of multicasting groups. To tackle the interference, we exploit rate-splitting to divide the group messages into common and group-specific sub-messages. We propose a per-cell rate-splitting approach, where the common message is locally designed to be decoded by the in-cell users, while treated as noise by the out-cell users. We consider the case where the number of RF chains is smaller than that of antennas, and consider a switching architecture, that is, the antenna selection is employed to choose the best antennas for transmission. Numerical results illustrate the potential of the proposed approach to significantly improve the energy efficiency in the interference-limited regime

Energy-efficient multicell multigroup multicasting with joint beamforming and antenna selection

Abstract This paper studies the energy efficiency and sum rate tradeoff for coordinated beamforming in multicell multiuser multigroup multicast multiple-input single-output systems. We first consider a conventional network energy efficiency maximization (EEmax) problem by jointly optimizing the transmit beamformers and antennas selected to be used in transmission. We also account for per-antenna maximum power constraints to avoid nonlinear distortion in power amplifiers and user-specific minimum rate constraints to guarantee certain service levels and fairness. To be energy efficient, transmit antenna selection is employed. It eventually leads to a mixed-Boolean fractional program. We then propose two different approaches to solve this difficult problem. The first solution is based on a novel modeling technique that produces a tight continuous relaxation. The second approach is based on sparsity-inducing method, which does not require the introduction of any Boolean variable. We also investigate the tradeoff between the energy efficiency and sum rate by proposing two different formulations. In the first formulation, we propose a new metric, that is, the ratio of the sum rate and the so-called weighted power. Specifically, this metric reduces to EEmax when the weight is 1, and to sum rate maximization when the weight is 0. In the other method, we treat the tradeoff problem as a multiobjective optimization for which a scalarization approach is adopted. Numerical results illustrate significant achievable energy efficiency gains over the method where the antenna selection is not employed. The effect of antenna selection on the energy efficiency and sum rate tradeoff is also demonstrated

Energy-efficient transmission strategies for CoMP downlink—overview, extension, and numerical comparison

Abstract This paper focuses on energy-efficient coordinated multi-point (CoMP) downlink in multi-antenna multi-cell wireless communications systems. We provide an overview of transmit beamforming designs for various energy efficiency (EE) metrics including maximizing the overall network EE, sum weighted EE, and fairness EE. Generally, an EE optimization problem is a nonconvex program for which finding the globally optimal solutions requires high computational effort. Consequently, several low-complexity suboptimal approaches have been proposed. Here, we sum up the main concepts of the recently proposed algorithms based on the state-of-the-art successive convex approximation (SCA) framework. Moreover, we discuss the application to the newly posted EE problems including new EE metrics and power consumption models. Furthermore, distributed implementation developed based on alternating direction method of multipliers (ADMM) for the provided solutions is also discussed. For the sake of completeness, we provide numerical comparison of the SCA based approaches and the conventional solutions developed based on parametric transformations (PTs). We also demonstrate the differences and roles of different EE objectives and power consumption models