Visible Light Communications (VLC) has recently emerged as a viable solution for solving the spectrum shortage problem. The idea is to use artificial light sources as medium to communicate with portable devices. In particular, the light sources can be switched on and off with a very high-frequency corresponding to 1s and 0s of digital communication. The high-frequency on-off switching can be detected by electronic devices but not the human eyes, and hence will not affect the light sources' illumination functions. In VLC, if a receiver is equipped with photodiodes that count the number of arriving photons, the channels can be modeled as Poisson channels. Unlike Gaussian channels that are suitable for radio spectrum and have been intensively investigated, Poisson channels are more challenging and are not that well understood. The goal of this thesis is to characterize the fundamental limits of various Poisson channels that models different scenarios in VLC. We first focus on single user Poisson fading channels with time-varying background lights. Our model is motivated by indoor optical wireless communication systems, in which the noise level is affected by the strength of the background light. We study both the single-input single-output (SISO) and the multiple-input and multiple-output (MIMO) channels. For each channel, we consider scenarios with and without delay constraints. For the case without a delay constraint, we characterize the optimal power allocation scheme that maximizes the ergodic capacity. For the case with a strict delay constraint, we characterize the optimal power allocation scheme that minimizes the outage probability. We then extend the study to the multi-user Poisson channels and analyze the sum-rate capacity of two-user Poisson multiple access channels (MAC). We first characterize the sum-rate capacity of the non-symmetric Poisson MAC when each transmitter has a single antenna. We show that, for certain channel parameters, it is optimal for a single-user to transmit to achieve the sum-rate capacity. This is in sharp contrast to the Gaussian MAC, in which both users must transmit, either simultaneously or at different times, in order to achieve the sum-rate capacity. We then characterize the sum-rate capacity of the Poisson MAC with multiple antennas at each transmitter. By converting a non-convex optimization problem with a large number of variables into a non-convex optimization problem with two variables, we show that the sum-rate capacity of the Poisson MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter. We further analyze the sum-rate capacity of two-user Poisson MIMO multiple-access channels (MAC), when both the transmitters and the receiver are equipped with multiple antennas. We first characterize the sum-rate capacity of the Poisson MAC when each transmitter has a single antenna and the receiver has multiple antennas. We show that similar to Poisson MISO-MAC channels, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-rate capacity, and for certain channel parameters, it is optimal for both users to transmit. We then characterize the sum-rate capacity of the channel where both the transmitters and the receiver are equipped with multiple antennas. We show that the sum-rate capacity of the Poisson MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter.

In conventional wireless channel models, there is no control on the gains of different subchannels. In such channels, the transmitted signal undergoes attenuation and phase shift and is subject to multi-path propagation effects. We herein refer to such channels as passive channels. In this dissertation, we study the problem of joint power allocation and channel design for a parallel channel which conveys information from a source to a destination through multiple orthogonal subchannels. In such a link, the power over each subchannel can be adjusted not only at the source but also at each subchannel. We refer to this link as an active parallel channel. For such a channel, we study the problem of sum-rate maximization under the assumption that the source power as well as the energy of the active channel are constrained. This problem is investigated for equal and unequal noise power at different subchannels. For equal noise power over different subchannels, although the sum-rate maximization problem is not convex, we propose a closed-form solution to this maximization problem. An interesting aspect of this solution is that it requires only a subset of the subchannels to be active and the remaining subchannels should be switched off. This is in contrast with passive parallel channels with equal subchannel signal-tonoise- ratios (SNRs), where water-filling solution to the sum-rate maximization under a total source power constraint leads to an equal power allocation among all subchannels. Furthermore, we prove that the number of active channels depends on the product of the source and channel powers. We also prove that if the total power available to the source and to the channel is limited, then in order to maximize the sum-rate via optimal power allocation to the source and to the active channel, half viii ix of the total available power should be allocated to the source and the remaining half should be allocated to the active channel. We extend our analysis to the case where the noise powers are unequal over different subchannels. we show that the sum-rate maximization problem is not convex. Nevertheless, with the aid of Karush-Kuhn-Tucker (KKT) conditions, we propose a computationally efficient algorithm for optimal source and channel power allocation. To this end, first, we obtain the feasible number of active subchannels. Then, we show that the optimal solution can be obtained by comparing a finite number of points in the feasible set and by choosing the best point which yields the best sum-rate performance. The worst-case computational complexity of this solution is linear in terms of number of subchannels. / UOIT

The idea of utilizing multiple antennas (MIMO) has emerged as one of the significant breakthroughs in modern wireless communications. MIMO techniques can improve the spectral efficiency of wireless systems and provide significant throughput gains. As such, MIMO will be increasingly deployed in future wireless systems. On the other hand, in order to meet the increasing demand for high data rate multimedia wireless services, future wireless systems are evolving towards universal frequency reuse, where neighboring cells may utilize the same radio spectrum. As such, the performance of future wireless systems will be mainly limited by inter-cell interference (ICI). It has been shown that the throughput gains promised by conventional MIMO techniques degrade severely in multi-cell systems. This definitely attributes to the existence of the ICI. A lot of related work has been performed on the ICI mitigation or cancellation strategies, in multi-cell MIMO systems. Most of them assume that the channel and even data information is available at the collaborating base stations (BSs). Different from the previous work, we are looking into certain low-complexity codebook-based multi-cell multi-user MIMO strategies. For most of our work, we derive the statistics of the selected user's signal-to-interference-and-noise-ratio (SINR), which enable us to calculate the achieved sum-rate accurately and e ciently. With the derived sum-rate expressions, we evaluate and compare the sum-rate performance for several proposed low-complexity ICI-mitigation systems with various system parameters for single-user per-cell scheduling case. Furthermore, in order to fully exploit spatial multiplexing gain, we are considering multi-user per-cell scheduling case. Based on the assumption that all CSI including intra-cell and inter-cell channels are available at each BS, we rstly look into the centralized optimization approach. Typically, since the sum-rate maximization problem is mostly non-convex, it is generally di cult to obtain the globally optimum solution. Through certain approximation and relaxations, we successfully investigate an iterative optimization algorithm which exploits the second-order cone programming (SOCP) approach. From the simulation results, we will observe that the iterative option can provide near-optimum sum capacity, although only locally optimized. Afterwards, inspired by the successful application of Per-User Unitary Rate Control (PU2RC) scheme, we manage to extend it into dual-cell environment, with limited coordination between two cells. / Graduate

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20032号 / 情博第627号 / 新制||情||109(附属図書館) / 33128 / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 原田 博司, 教授 守倉 正博, 教授 梅野 健 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

While the channel capacity of an isolated noise-limited wireless link is well-understood, the same is not true for the interference-limited wireless links that coexist in the same area and occupy the same frequency band(s). The performance of these wireless systems is coupled to each other due to the mutual interference. One such wireless scenario is modeled as a network of simultaneously communicating node pairs and is generally referred to as an interference channel (IC). The problem of characterizing the capacity of an IC is one of the most interesting and long-standing open problems in information theory. A popular way of characterizing the capacity of an IC is to maximize the achievable sum-rate by treating interference as Gaussian noise, which is considered optimal in low-interference scenarios. While the sum-rate of the single-band SISO IC is relatively well understood, it is not so when the users have multiple-bands and multiple-antennas for transmission. Therefore, the study of the optimal sum-rate of the multi-band MIMO IC is the main goal of this thesis. The sum-rate maximization problem for these ICs is formulated and is shown to be quite similar to the one already known for single-band MIMO ICs. This problem is reduced to the problem of finding the optimal fraction of power to be transmitted over each spatial channel in each frequency band. The underlying optimization problem, being non-linear and non-convex, is difficult to solve analytically or by employing local optimization techniques. Therefore, we develop a global optimization algorithm by extending the Reformulation and Linearization Technique (RLT) based Branch and Bound (BB) strategy to find the provably optimal solution to this problem. We further show that the spatial and spectral channels are surprisingly similar in a multi-band multi-antenna IC from a sum-rate maximization perspective. This result is especially interesting because of the dissimilarity in the way the spatial and frequency channels affect the perceived interference. As a part of this study, we also develop some rules-of-thumb regarding the optimal power allocation strategies in multi-band MIMO ICs in various interference regimes. Due to the recent popularity of Interference Alignment (IA) as a means of approaching capacity in an IC (in high-interference regime), we also compare the sum-rates achievable by our technique to the ones achievable by IA. The results indicate that the proposed power control technique performs better than IA in the low and intermediate interference regimes. Interestingly, the performance of the power control technique improves further relative to IA with an increase in the number of orthogonal spatial or frequency channels. / Master of Science

This thesis is targeted at precoding methods and resource allocation for the downlink of fixed multi-user multi-antenna broadband wireless systems. We explore different utilizations of precoders in transmission over frequency-selective channels. We first consider the weighted sum-rate (WSR) maximization problem for multi-carrier systems using linear precoding and propose a low complexity algorithm which exhibits near-optimal performance. Moreover, we offer a novel rate allocation method that utilizes the signalto- noise-ratio (SNR) gap to capacity concept to choose the rates to allocate to each data stream. We then study a single-carrier transmission scheme that overcomes known impairments associated with multi-carrier systems. The proposed scheme utilizes timereversal space-time block coding (TR-STBC) to orthogonalize the downlink receivers and performs the required pre-equalization using Tomlinson-Harashima precoding (THP).We finally discuss the strengths and weaknesses of the proposed method.

This thesis is targeted at precoding methods and resource allocation for the downlink of fixed multi-user multi-antenna broadband wireless systems. We explore different utilizations of precoders in transmission over frequency-selective channels. We first consider the weighted sum-rate (WSR) maximization problem for multi-carrier systems using linear precoding and propose a low complexity algorithm which exhibits near-optimal performance. Moreover, we offer a novel rate allocation method that utilizes the signalto- noise-ratio (SNR) gap to capacity concept to choose the rates to allocate to each data stream. We then study a single-carrier transmission scheme that overcomes known impairments associated with multi-carrier systems. The proposed scheme utilizes timereversal space-time block coding (TR-STBC) to orthogonalize the downlink receivers and performs the required pre-equalization using Tomlinson-Harashima precoding (THP).We finally discuss the strengths and weaknesses of the proposed method.

Abstract The application of optimization techniques for resource management in wireless communication networks is considered in this thesis. It is understood that a wide variety of resource management problems of recent interest, including power/rate control, link scheduling, cross-layer control, network utility maximization, beamformer design of multiple-input multiple-output networks, and many others are directly or indirectly reliant on the general weighted sum-rate maximization (WSRMax) problem. Thus, in this dissertation a greater emphasis is placed on the WSRMax problem, which is known to be NP-hard. A general method, based on the branch and bound technique, is developed, which solves globally the nonconvex WSRMax problem with an optimality certificate. Efficient analytic bounding techniques are derived as well. More broadly, the proposed method is not restricted to WSRMax. It can also be used to maximize any system performance metric, which is Lipschitz continuous and increasing on signal-to-interference-plus-noise ratio. The method can be used to find the optimum performance of any network design method, which relies on WSRMax, and therefore it is also useful for evaluating the performance loss encountered by any heuristic algorithm. The considered link-interference model is general enough to accommodate a wide range of network topologies with various node capabilities, such as singlepacket transmission, multipacket transmission, simultaneous transmission and reception, and many others. Since global methods become slow in large-scale problems, fast local optimization methods for the WSRMax problem are also developed. First, a general multicommodity, multichannel wireless multihop network where all receivers perform singleuser detection is considered. Algorithms based on homotopy methods and complementary geometric programming are developed for WSRMax. They are able to exploit efficiently the available multichannel diversity. The proposed algorithm, based on homotopy methods, handles efficiently the self interference problem that arises when a node transmits and receives simultaneously in the same frequency band. This is very important, since the use of supplementary combinatorial constraints to prevent simultaneous transmissions and receptions of any node is circumvented. In addition, the algorithm together with the considered interference model, provide a mechanism for evaluating the gains when the network nodes employ self interference cancelation techniques with different degrees of accuracy. Next, a similar multicommodity wireless multihop network is considered, but all receivers perform multiuser detection. Solutions for the WSRMax problem are obtained by imposing additional constraints, such as that only one node can transmit to others at a time or that only one node can receive from others at a time. The WSRMax problem of downlink OFDMA systems is also considered. A fast algorithm based on primal decomposition techniques is developed to jointly optimize the multiuser subcarrier assignment and power allocation to maximize the weighted sum-rate (WSR). Numerical results show that the proposed algorithm converges faster than Lagrange relaxation based methods. Finally, a distributed algorithm for WSRMax is derived in multiple-input single-output multicell downlink systems. The proposed method is based on classical primal decomposition methods and subgradient methods. It does not rely on zero forcing beamforming or high signal-to-interference-plus-noise ratio approximation like many other distributed variants. The algorithm essentially involves coordinating many local subproblems (one for each base station) to resolve the inter-cell interference such that the WSR is maximized. The numerical results show that significant gains can be achieved by only a small amount of message passing between the coordinating base stations, though the global optimality of the solution cannot be guaranteed. / Tiivistelmä Tässä työssä tutkitaan optimointimenetelmien käyttöä resurssienhallintaan langattomissa tiedonsiirtoverkoissa. Monet ajankohtaiset resurssienhallintaongelmat, kuten esimerkiksi tehonsäätö, datanopeuden säätö, radiolinkkien ajastus, protokollakerrosten välinen optimointi, verkon hyötyfunktion maksimointi ja keilanmuodostus moniantenniverkoissa, liittyvät joko suoraan tai epäsuorasti painotetun summadatanopeuden maksimointiongelmaan (weighted sum-rate maximization, WSRMax). Tästä syystä tämä työ keskittyy erityisesti WSRMax-ongelmaan, joka on tunnetusti NP-kova. Työssä kehitetään yleinen branch and bound -tekniikkaan perustuva menetelmä, joka ratkaisee epäkonveksin WSRMax-ongelman globaalisti ja tuottaa todistuksen ratkaisun optimaalisuudesta. Työssä johdetaan myös tehokkaita analyyttisiä suorituskykyrajojen laskentatekniikoita. Ehdotetun menetelmän käyttö ei rajoitu vain WSRMax-ongelmaan, vaan sitä voidaan soveltaa minkä tahansa suorituskykymetriikan maksimointiin, kunhan se on Lipschitz-jatkuva ja kasvava signaali-häiriö-plus-kohinasuhteen funktiona. Menetelmää voidaan käyttää minkä tahansa WSRMax-ongelmaan perustuvan verkkosuunnittelumenetelmän optimaalisen suorituskyvyn määrittämiseen, ja siksi sitä voidaan hyödyntää myös minkä tahansa heuristisen algoritmin aiheuttaman suorituskykytappion arvioimiseen. Tutkittava linkki-häiriömalli on riittävän yleinen monien erilaisten verkkotopologioiden ja verkkosolmujen kyvykkyyksien mallintamiseen, kuten esimerkiksi yhden tai useamman datapaketin siirtoon sekä yhtäaikaiseen lähetykseen ja vastaanottoon. Koska globaalit menetelmät ovat hitaita suurien ongelmien ratkaisussa, työssä kehitetään WSRMax-ongelmalle myös nopeita paikallisia optimointimenetelmiä. Ensiksi käsitellään yleistä useaa eri yhteyspalvelua tukevaa monikanavaista langatonta monihyppyverkkoa, jossa kaikki vastaanottimet suorittavat yhden käyttäjän ilmaisun, ja kehitetään algoritmeja, joiden perustana ovat homotopiamenetelmät ja komplementaarinen geometrinen optimointi. Ne hyödyntävät tehokkaasti saatavilla olevan monikanavadiversiteetin. Esitetty homotopiamenetelmiin perustuva algoritmi käsittelee tehokkaasti itsehäiriöongelman, joka syntyy, kun laite lähettää ja vastaanottaa samanaikaisesti samalla taajuuskaistalla. Tämä on tärkeää, koska näin voidaan välttää lisäehtojen käyttö yhtäaikaisen lähetyksen ja vastaanoton estämiseksi. Lisäksi algoritmi yhdessä tutkittavan häiriömallin kanssa auttaa arvioimaan, paljonko etua saadaan, kun laitteet käyttävät itsehäiriön poistomenetelmiä erilaisilla tarkkuuksilla. Seuraavaksi tutkitaan vastaavaa langatonta monihyppyverkkoa, jossa kaikki vastaanottimet suorittavat monen käyttäjän ilmaisun. Ratkaisuja WSRMax-ongelmalle saadaan asettamalla lisäehtoja, kuten että vain yksi lähetin kerrallaan voi lähettää tai että vain yksi vastaanotin kerrallaan voi vastaanottaa. Edelleen tutkitaan WSRMax-ongelmaa laskevalla siirtotiellä OFDMA-järjestelmässä, ja johdetaan primaalihajotelmaan perustuva nopea algoritmi, joka yhteisoptimoi monen käyttäjän alikantoaalto- ja tehoallokaation maksimoiden painotetun summadatanopeuden. Numeeriset tulokset osoittavat, että esitetty algoritmi suppenee nopeammin kuin Lagrangen relaksaatioon perustuvat menetelmät. Lopuksi johdetaan hajautettu algoritmi WSRMax-ongelmalle monisoluisissa moniantennilähetystä käyttävissä järjestelmissä laskevaa siirtotietä varten. Esitetty menetelmä perustuu klassisiin primaalihajotelma- ja aligradienttimenetelmiin. Se ei turvaudu nollaanpakotus-keilanmuodostukseen tai korkean signaali-häiriö-plus-kohinasuhteen approksimaatioon, kuten monet muut hajautetut muunnelmat. Algoritmi koordinoi monta paikallista aliongelmaa (yhden kutakin tukiasemaa kohti) ratkaistakseen solujen välisen häiriön siten, että WSR maksimoituu. Numeeriset tulokset osoittavat, että merkittävää etua saadaan jo vähäisellä yhdessä toimivien tukiasemien välisellä viestinvaihdolla, vaikka globaalisti optimaalista ratkaisua ei voidakaan taata.

In this thesis we investigate how to improve the performance of MU-MIMO wireless system in terms of achieving Shannon capacity limit and efficient use of precious resource of radio spectrum in wireless communication. First a new suboptimal volume-based scheduling algorithm is presented, which can be applied in MU-MIMO downlink system to transmit signals concurrently to multiple users under the assumption of perfect channel information at transmitter and receiver. The volume-based scheduling algorithm utilises Block Diagonalisation precoding and Householder reduction procedure of QR factorisation. In comparison with capacity-based suboptimal scheduling algorithm, the volume-based algorithm has much reduced computational complexity with only a fraction of sum-rate capacity penalty from the upper bound of system capacity limit. In comparison with semi-orthogonal user selection suboptimal scheduling algorithm, the volume-based scheduling algorithm can be implemented with less computational complexity. Furthermore, the sum-rate capacity achieved via volume-based scheduling algorithm is higher than that achieved by SUS scheduling algorithm in the MIMO case. Then, a two-step scheduling algorithm is proposed, which can be used in the MU-MIMO system and under the assumption that channel state information is known to the receiver, but it is not known to the transmitter and the system under the feedback resource constraint. Assume that low bits codebook and high bits codebook are stored at the transmitter and receiver. The users are selected by using the low bits codebook; subsequently the BD precoding vectors for selected users are designed by employing high bits codebook. The first step of the algorithm can alleviate the load on feedback uplink channel in the MU-MIMO wireless system while the second step can aid precoding design to improve system sum-rate capacity. Next, a MU-MIMO cognitive radio (CR) wireless system has been studied. In such system, a primary wireless network and secondary wireless network coexist and the transmitters and receivers are equipped with multiple antennas. Spectrum sensing methods by which a portion of spectrum can be utilised by a secondary user when the spectrum is detected not in use by a primary user were investigated. A Free Probability Theory (FPT) spectrum sensing method that is a blind spectrum sensing method is proposed. By utilizing the asymptotic behaviour of random matrix based on FPT, the covariance matrix of transmitted signals can be estimated through a large number of observations of the received signals. The method performs better than traditional energy spectrum sensing method. We also consider cooperative spectrum sensing by using the FPT method in MU-MIMO CR system. Cooperative spectrum sensing can improve the performance of signal detection. Furthermore, with the selective cooperative spectrum sensing approach, high probability of detection can be achieved when the system is under false alarm constraint. Finally, spectrum sensing method based on the bispectrum of high-order statistics (HOS) and receive diversity in SIMO CR system is proposed. Multiple antennas on the receiver can improve received SNR value and therefore enhance spectrum sensing performance in terms of increase of system-level probability of detection. Discussions on cooperative spectrum sensing by using the spectrum sensing method based on HOS and receive diversity are presented.

Wireless communication systems have exploded in popularity over the past few decades. Due to their popularity, the demand for higher data rates by the users, and the high cost of wireless spectrum, wireless providers are actively seeking ways to improve the spectral efficiency of their networks. One promising technique to improve spectral efficiency is to equip the wireless devices with multiple antennas. If both the transmitter and receiver of a link are equipped with multiple antennas, they form a multiple-input multiple-output (MIMO) link. The multiple antennas at the nodes provide degrees-of-freedom that can be used for either sending multiple streams of data simultaneously (a technique known as spatial multiplexing), or for suppressing interference through linear combining, but not both. Due to this trade-off, careful allocation of how many streams each link should carry is important to ensure that each node has enough degrees-of-freedom available to suppress the interference and support its desired streams. How the streams are sent and received and how interference is suppressed is ultimately determined by the beamforming weights at the transmitters and the combining weights at the receivers. Determining these weights is, however, made difficult by their inherent interdependency. Our focus is on unplanned and/or dense single-hop networks, such as WLANs and femtocells, where each single-hop network is composed of an access point serving several associated clients. The objective of this research is to design algorithms for maximizing the performance of dense single-hop wireless networks of MIMO links. We address the problems of determining which links to schedule together at each time slot, how many streams to allocate to each link (if any), and the beamforming and combining weights that support those streams. This dissertation describes four key contributions as follows: - We classify any interference suppression technique as either unilateral interference suppression or bilateral interference suppression. We show that a simple bilateral interference suppression approach outperforms all known unilateral interference suppression approaches, even after searching for the best unilateral solution. - We propose an algorithm based on bilateral interference suppression whose goal is to maximize the sum rate of a set of interfering MIMO links by jointly optimizing which subset of transmitters should transmit, the number of streams for each transmitter (if any), and the beamforming and combining weights that support those streams. - We propose a framework for optimizing dense single-hop wireless networks. The framework implements techniques to address several practical issues that arise when implementing interference suppression, such as the overhead of performing channel measurements and communicating channel state information, the overhead of computing the beamforming and combining weights, and the overhead of cooperation between the access points. - We derive the optimal scheduler that maximizes the sum rate subject to proportional fairness. Simulations in ns-3 show that the framework, using the optimal scheduler, increases the proportionally fair aggregate goodput by up to 165% as compared to the aggregate goodput of 802.11n for the case of four interfering single-hop wireless networks with two clients each.