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Cloud radio access network (C-RAN) is an evolutionary radio network architecture in which a cloud-computing-based baseband (BB) signal-processing unit is shared among distributed low-cost wireless access points. This architecture offers a number of significant improvements over the traditional RANs, including better network scalability, spectral, and energy efficiency. As such C -RAN has been identified as one of the enabling technologies for the next-generation mobile networks. This chapter focuses on examining the energy-efficient transmission strategies of the C-RAN for cellular systems. In particular, we present optimization algorithms for the problem of transmit beamforming designs maximizing the network energy efficiency. In general, the energy efficiency maximization in C-RANs inherits the difficulty of resource allocation optimizations in interference-limited networks, i.e., it is an intractable non convex optimization problem. We first introduce a globally optimal method based on monotonic optimization (MO) to illustrate the optimal energy efficiency performance of the considered system. While the global optimization method requires extremely high computational effort and, thus, is not suitable for practical implementation, efficient optimization techniques achieving near -optimal performance are desirable in practice. To fulfill this gap, we present three low -complexity approaches based on the state-of-the-art local optimization framework, namely, successive convex approximation (SCA).

We consider uplink multiuser wireless communications systems, where the base station (BS) receiver is equipped with a large-scale antenna array and resolution adaptive analog-to-digital converters (ADCs). The aim is to maximize the energy efficiency (EE) at the BS subject to constraints on the users’ quality-of-service. The approach is to jointly optimize both the number of quantization bits at the ADCs and the on/off modes of the radio frequency (RF) processing chains. The considered problem is a discrete nonlinear program, the optimal solution of which is difficult to find. We develop an efficient algorithm based on the discrete branch-reduce-and-bound (DBRnB) framework. It finds the globally optimal solutions to the problem. In particular, we make some modifications, which significantly improve the convergence performance. The numerical results demonstrate that optimizing jointly the number of quantization bits and on/off mode can achieve remarkable EE gains compared to only optimizing the number of quantization bits.

The rapid evolution of wireless networks to meet the requirements of explosive data traffic demand is escalating energy consumption beyond sustainable limits. Consequently, energy efficiency (EE) has emerged as a key performance indicator for future wireless networks to address the increasing concern over greenhouse gas emissions and sustainable economic growth. This thesis studies energy-efficient transmission strategies for multiantenna wireless systems. The aim is to develop linear beamforming techniques maximizing the bit-per-Joule EE metric, focusing on three appealing scenarios: a coordinated multicell system; a fronthaul-constrained cloud radio access network (C-RAN); and a multi-pair wireless-powered relaying system. The primary emphasis is on suboptimal but efficient optimization approaches which are attractive for practical implementation. The problem of achieving EE fairness in a multicell multiple-input single-output downlink system is studied first. Specifically, coordinated beamforming is designed to maximize the minimum EE among all base stations. Novel efficient iterative optimization methods solving the design problem in both centralized and decentralized fashions are proposed. In a downlink C-RAN with finite-capacity fronthaul links, the network-wide EE performance is explored via a joint design of beamforming and remote radio head-user association. A relatively realistic power consumption model including rate-dependent circuit power and nonlinear power amplifiers’ (PA) efficiency is also considered. To gain an insight into the optimal performance of the design problem, an algorithm achieving globally optimal solutions is devised. Towards practical implementation, two efficient iterative suboptimal methods are proposed aiming at yielding near-optimal performance. Finally, a multi-pair amplify-forward relaying network is considered, in which energy-constrained relays adopting time-switching protocol harvest energy from the radio frequency signals transmitted by users. To maintain EE fairness among all user pairs, joint optimization of system parameters, such as users’ transmit power, relay beamforming, and energy harvesting (EH) time, is studied. Impacts of rate-dependent circuit power, nonlinear PAs’ efficiency and nonlinear EH circuits on the achievable performance are also addressed. Tiivistelmä Langattomat verkot ovat kehittyneet nopeasti räjähdysmäisesti kasvavan dataliikenteen mahdollistamiseksi, minkä seurauksena energiankulutus on kasvanut kestävän kehityksen rajat ylittävällä tavalla. Siksi energiatehokkuudesta (EE, energy efficiency) on tullut uusien langattomien verkkojen keskeinen suunnittelukriteeri vastauksena kasvavaan huoleen kasvihuonepäästöistä ja kestävästä talouskasvusta. Väitöskirjassa tutkitaan moniantennisten langattomien järjestelmien energiatehokkaita tiedonsiirtostrategioita. Tavoitteena on kehittää lineaarisia keilanmuodostustekniikoita, jotka maksimoivat energiatehokkuuden mitattuna bitteinä joulea kohden, keskittymällä kolmeen kiinnostavaan vaihtoehtoon, joita ovat koordinoitu monisolujärjestelmän lähetys laskevalla siirtotiellä, pilvipohjainen radioliityntäverkko (C-RAN, cloud radio access network), jossa laskentayksikön ja varsinaisen radiolähettimen välinen yhteys (fronthaul) on rahoitettu, ja usean parin relejärjestelmiin, joissa releet toimivat paristoilla. Työn pääpaino on alioptimaalisissa, mutta käytännöllisesti tehokkaissa optimointimenetelmissä. Pääpaino on alioptimaalisissa mutta tehokkaissa optimointitavoissa, jotka ovat kiinnostavia käytännön toteutuksen näkökulmasta. Ensiksi tarkastellaan tasapuolisen energiatehokkuuden saavuttamista monisoluisessa laskevan siirtotien moni-tulo yksi-lähtö (MISO, multiple-input single-output) -järjestelmässä. Koordinoitu keilanmuodostus on suunniteltu erityisesti maksimoimaan energiatehokkuuden minimitaso kaikilla tukiasemilla. Tarkemmin sanottuna pyritään maksimoimaan huonoin energiatehokkuus solmujen välillä, kun käytetään yhteistoiminnallista keilanmuodostusta. Muodostetun ongelman ratkaisemiseksi ehdotetaan edistyksellisiä iteratiivisia menetelmiä käyttämällä sekä keskitettyjä että hajautettuja ratkaisuja. Laskevan siirtosuunnan fronthaul-rajoitetussa C-RAN-järjestelmässä selvitetään verkonlaajuista energiatehokkuutta keilanmuodostuksen ja palvelevan tukiaseman yhteisoptimoinnilla. Tässä käytetään verrattain realistista tehonkulutusmallia, joka sisältää datanopeudesta riippuvan prosessointitehon ja epälineaarisen tehovahvistimen (PA, power amplifier) hyötysuhteen. Jotta saadaan käsitys ongelman optimaalisesta suorituskyvystä, siihen kehitetään globaalisti optimaalinen menetelmä. Lisäksi ehdotetaan kaksi käytännöllisempää iteratiivista menetelmää, jotka saavuttavat lähes optimaalisen suorituskyvyn. Lopuksi keskitytään monen parin vahvista-ja-välitä eteenpäin (AF. amplify and forward) verkkoon, jossa aikajakokytkentää käyttävät energiarajoitetut toistimet keräävät energiaa käyttäjien lähettämistä radiosignaaleista. Jotta saavutetaan EE:n oikeudenmukaisuus kaikkien parien välillä, parametrit, kuten käyttäjien lähetysteho, toistimen keilanmuodostus, ja energiankeräysaika suunnitellaan yhdessä. Tässä tutkitaan nopeusriippuvaisen piirin tehon, epälineaarisen tehovahvistimen hyötysuhteen ja epälineaaristen energiankeräyspiirien tehon vaikutusta suorituskykyyn.

We consider downlink transmission of a fronthaul-constrained cloud radio access network. Our aim is to maximize the system sum data rate via jointly designing beamforming and user association. The problem is basically a mixed integer non-convex programs for which a global solution requires a prohibitively high computational effort. The focus is thus on efficient solutions capable of achieving the near optimal performance with low complexity. To this end, we transform the design problem into continuous programs by two approaches: penalty and sparse approximation methods. The resulting continuous nonconvex problems are then solved by the successive convex approximation framework. Numerical results indicate that the proposed methods are near-optimal, and outperform existing suboptimal methods in terms of achieved performances and computational complexity.

We consider a multi-pair amplify-and-forward relay network where the energy-constrained relays adopting the time-switching protocol harvest energy from the radio-frequency signals transmitted by the users for assisting user data transmission. Both one-way and two-way relaying techniques are investigated. Aiming at energy efficiency (EE) fairness among the user pairs, we construct an energy consumption model incorporating rate-dependent signal processing power, the dependence on output power level of power amplifiers’ efficiency, and nonlinear energy harvesting (EH) circuits. Then, we formulate the max-min EE fairness problems in which the data rates, users’ transmit power, relays’ processing coefficient, and EH time are jointly optimized under the constraints on the quality of service and users’ maximum transmit power. To achieve efficient suboptimal solutions to these nonconvex problems, we devise monotonic descent algorithms based on the inner approximation (IA) framework, which solve a second-order-cone program in each iteration. To further simplify the designs, we propose an approach combining IA and zero-forcing beamforming, which eliminates inter-pair interference and reduces the numbers of variables and required iterations. Finally, extensive numerical results are presented to validate the proposed approaches. More specifically, the results demonstrate that ignoring the realistic aspects of power consumption might degrade the performance remarkably, and jointly designing parameters involved could significantly enhance the EE.

We consider downlink transmission in cloud radio access networks with multiple cochannel multicasting groups served by a group of remote radio heads (RRHs), which receive information from a base band unit via finite-capacity fronthaul links. Our aim is to jointly design RRH selection and beamforming vectors such that the minimum weighted data rate among users is maximized under the constraints of maximum transmit power and fronthaul capacity at each specific RRH. The problem is intractable due to the numerical difficulties of combination and nonconvex functions. Based on a semidefinite relaxation technique, bisection search, and a branch-and-bound method, we develop an upper bound, which is also the optimal solution to the original problem if the relaxation is tight. More importantly, we propose a heuristic low-complexity iterative procedure for practical applications based on the state-of-the-art sequential convex approximation. Subsequently, we modify the proposed methods for the uncertain channel state information case. To be specific, the upper bound and its suboptimal solution are altered based on the S-lemma while the low-complexity algorithm is tailored by using two different approximations of intractable robust counterpart. The validity of the proposed methods in the region of limited fronthaul capacity is confirmed by numerical results.

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.

We study downlink of multiantenna cloud radio access networks with finite-capacity fronthaul links. The aim is to propose joint designs of beamforming and remote radio head (RRH)-user association, subject to constraints on users’ quality-of-service, limited capacity of fronthaul links and transmit power, to maximize the system energy efficiency. To cope with the limited-capacity fronthaul we consider the problem of RRH-user association to select a subset of users that can be served by each RRH. Moreover, different to the conventional power consumption models, we take into account the dependence of the baseband signal processing power on the data rate, as well as the dynamics of the efficiency of power amplifiers. The considered problem leads to a mixed binary integer program which is difficult to solve. Our first contribution is to derive a globally optimal solution for the considered problem by customizing a discrete branch-reduce-and-bound approach. Since the global optimization method requires a high computational effort, we further propose two suboptimal solutions able to achieve the near optimal performance but with much reduced complexity. To this end, we transform the design problem into continuous (but inherently nonconvex) programs by two approaches: penalty and ℓ0 -approximation methods. These resulting continuous nonconvex problems are then solved by the successive convex approximation framework. Numerical results are provided to evaluate the effectiveness of the proposed approaches.

A joint beamforming and remote radio head (RRH)-user association design for downlink of cloud radio access networks (CRANs) is considered. The aim is to maximize the system energy efficiency subject to constraints on users’ quality-of-service, capacity of front haul links and transmit power. Different to the conventional power consumption models, we embrace the dependence of baseband signal processing power on the data rate, and the dynamics of the power amplifiers’ efficiency. The considered problem is a mixed Boolean nonconvex program whose optimal solution is difficult to find. As our main contribution, we provide a discrete branch-reduce-and-bound (DBRnB) approach to solve the problem globally. We also make some modifications to the standard DBRnB procedure. Those remarkably improve the convergence performance. Numerical results are provided to confirm the validity of the proposed method.

This paper considers a downlink transmission of cloud radio access network (C-RAN) in which precoded baseband signals at a common baseband unit are compressed before being forwarded to radio units (RUs) through limited fronthaul capacity links. We investigate the joint design of precoding, multivariate compression and RU-user selection which maximizes the energy efficiency of downlink C-RAN networks. The considered problem is inherently a rank-constrained mixed Boolean nonconvex program for which a globally optimal solution is difficult and computationally expensive to find. In order to derive practically appealing solutions, we invoke some useful relaxation and transformation techniques to arrive at a more tractable (but still nonconvex) continuous program. To solve the relaxation problem, we propose an iterative procedure based on DC algorithms which is provably convergent. Numerical results demonstrate the superior of the proposed solution in terms of achievable energy efficiency compared to existing schemes.

This paper considers a multicell downlink channel in which multiple base stations (BSs) cooperatively serve users by jointly precoding shared data transported from a central processor over limited-capacity backhaul links. We jointly design the beamformers and BS-user link selection so as to maximize the sum rate subject to user-specific signal-to-interference-noise (SINR) requirements, per-BS backhaul capacity and per-BS power constraints. As existing solutions for the considered problem are suboptimal and their optimality remains unknown due to the lack of globally optimal solutions, we characterized this gap by proposing an globally optimal algorithm for the problem of interest. Specifically, the proposed method is customized from a generic framework of a branch and bound algorithm applied to discrete monotonic optimization. We show that the proposed algorithm converges after a finite number of iterations, and can serve as a benchmark for existing suboptimal solutions and those that will be developed for similar contexts in the future. In this regard, we numerically compare the proposed optimal solution to a current state-of-the-art, which show that this suboptimal method only attains 70% to 90% of the optimal performance.

Energy efficiency (EE) is becoming one of the important criteria in wireless transmission design. This paper discusses the recently proposed energy-efficient transmit beamforming designs for multicell multiuser multiple-input single-output (MISO) systems, including maximizing overall network EE, sum weighted EE and fairness EE. Generally, the EE optimization problems are NP-hard nonconvex programs for which finding the globally optimal solutions is challenging. For low-complexity suboptimal approaches, there is a class of solutions conventionally developed based on parametric transformations. However, those have been revealed problematic in terms of computational complexity and convergence. To overcome these issues, novel algorithms have been recently developed based on the state-of-the-art successive convex approximation (SCA) framework. Here we sum up the basic concepts of the algorithms and provide numerical results which illustrate the solution quality compared to the existing methods.

This paper aims at guaranteeing the achievable energy efficiency (EE) fairness in a multicell multiuser multiple-input single-output downlink system. The design objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. This results in a max-min fractional program and as such is difficult to solve in general. Our goal is to develop decentralized algorithms for the max-min EE problem based on combining the successive convex approximation (SCA) framework and the alternating direction method of multipliers (ADMMs). Specifically, leveraging the SCA principle, we iteratively approximate the nonconvex design problem by a sequence of convex programs for which two decentralized algorithms are then proposed. In the first approach, the convex program obtained at each step of the SCA procedure is solved optimally by allowing the BSs to exchange the required information until the ADMM converges. The convergence of the first method is analytically guaranteed but the amount of backhaul signaling can be noticeable in some realistic settings. To reduce the backhaul overhead, the second method performs an abstract version of the ADMM where only one variables update is carried out. Numerical results are provided to demonstrate the effectiveness of the two proposed decentralized algorithms.

We consider multicell downlink transmission where multi-antenna base stations (BSs) collaborate in transferring data to multiple cochannel multicast groups. Our aim is to design a joint transmitter selection and cooperative precoding scheme which maximizes the minimum received data rate among all receivers under constraints on the individual BS transmit power budgets as well as backhaul link capacity. The problem is naturally cast as a mixed Boolean nonconvex program whose global optimal solution is difficult to achieve. To solve this problem locally, we first use difference-convex (DC) functions to represent the Boolean variables, and further transform the problem into an equivalent but more tractable formulation. We then propose an iterative algorithm which provably converges to stationary solutions. Numerical results are provided to demonstrate the superior performance of the proposed method.

We investigate the fairness of achievable energy efficiency in a multicell multiuser multiple-input single-output (MISO) downlink system, where a beamforming scheme is designed to maximize the minimum energy efficiency among all base stations. The resulting optimization problem is a nonconvex max-min fractional program, which is generally difficult to solve optimally. We propose an iterative beamformer design based on an inner approximation algorithm which aims at locating a Karush-Kuhn-Tucker solution to the nonconvex program. By novel transformations, we arrive at a convex problem at each iteration of the proposed algorithm, which is amendable for being approximated by a second order cone program. The numerical results demonstrate that the proposed algorithm outperforms the existing schemes in terms of the convergence rate and processing time.