Collaborative beamforming schemes for wireless sensor networks with energy harvesting capabilities

In recent years, wireless sensor networks have attracted considerable attention in the research community. Their development, induced by technological advances in microelectronics, wireless networking and battery fabrication, is mainly motivated by a large number of possible applications such as environmental monitoring, industrial process control, goods tracking, healthcare applications, to name a few. Due to the unattended nature of wireless sensor networks, battery replacement can be either too costly or simply not feasible. In order to cope with this problem and prolong the network lifetime, energy efficient data transmission protocols have to be designed. Motivated by this ultimate goal, this PhD dissertation focuses on the design of collaborative beamforming schemes for wireless sensor networks with energy harvesting capabilities. On the one hand, by resorting to collaborative beamforming, sensors are able to convey a common message to a distant base station, in an energy efficient fashion. On the other, sensor nodes with energy harvesting capabilities promise virtually infinite network lifetime. Nevertheless, in order to realize collaborative beamforming, it is necessary that sensors align their transmitted signals so that they are coherently combined at the destination. Moreover, sensor nodes have to adapt their transmissions according to the amounts of harvested energy over time. First, this dissertation addresses the scenario where two sensor nodes (one of them capable of harvesting ambient energy) collaboratively transmit a common message to a distant base station. In this setting, we show that the optimal power allocation policy at the energy harvesting sensor can be computed independently (i.e., without the knowledge of the optimal policy at the battery operated one). Furthermore, we propose an iterative algorithm that allows us to compute the optimal policy at the battery operated sensor, as well. The insights gained by the aforementioned scenario allow us to generalize the analysis to a system with multiple energy harvesting sensors. In particular, we develop an iterative algorithm which sequentially optimizes the policies for all the sensors until some convergence criterion is satisfied. For the previous scenarios, this PhD dissertation evaluates the impact of total energy harvested, number of sensors and limited energy storage capacity on the system performance. Finally, we consider some practical schemes for carrier synchronization, required in order to implement collaborative beamforming in wireless sensor networks. To that end, we analyze two algorithms for decentralized phase synchronization: (i) the one bit of feedback algorithm previously proposed in the literature; and (ii) a decentralized phase synchronization algorithm that we propose. As for the former, we analyze the impact of additive noise on the beamforming gain and algorithm’s convergence properties, and, subsequently, we propose a variation that performs sidelobe control. As for the latter, the sensors are allowed to choose their respective training timeslots randomly, relieving the base station of the burden associated with centralized coordination. In this context, this PhD dissertation addresses the impact of number of timeslots and additive noise on the achieved received signal strength and throughputEn los últimos años, las redes de sensores inalámbricas han atraído considerable atención en la comunidad investigadora. Su desarrollo, impulsado por recientes avances tecnológicos en microelectrónica y radio comunicaciones, está motivado principalmente por un gran abanico de aplicaciones, tales como: Monitorización ambiental, control de procesos industriales, seguimiento de mercancías, telemedicina, entre otras. En las redes de sensores inalámbricas, es primordial el diseño de protocolos de transmisión energéticamente eficientes ya que no se contempla el reemplazo de baterías debido a su coste y/o complejidad. Motivados por esta problemática, esta tesis doctoral se centra en el diseño de esquemas de conformación de haz distribuidos para redes de sensores, en el que los nodos son capaces de almacenar energía del entorno, lo que en inglés se denomina energy harvesting. En primer lugar, esta tesis doctoral aborda el escenario en el que dos sensores (uno de ellos capaz de almacenar energía del ambiente) transmiten conjuntamente un mensaje a una estación base. En este contexto, se demuestra que la política de asignación de potencia óptima en el sensor con energy harvesting puede ser calculada de forma independiente (es decir, sin el conocimiento de la política óptima del otro sensor). A continuación, se propone un algoritmo iterativo que permite calcular la política óptima en el sensor que funciona con baterías. Este esquema es posteriormente generalizado para el caso de múltiples sensores. En particular, se desarrolla un algoritmo iterativo que optimiza las políticas de todos los sensores secuencialmente. Para los escenarios anteriormente mencionados, esta tesis evalúa el impacto de la energía total cosechada, número de sensores y la capacidad de la batería. Por último, se aborda el problema de sincronización de fase en los sensores con el fin de poder realizar la conformación de haz de forma distribuida. Para ello, se analizan dos algoritmos para la sincronización de fase descentralizados: (i) el algoritmo "one bit of feedback" previamente propuesto en la literatura, y (ii) un algoritmo de sincronización de fase descentralizado que se propone en esta tesis. En el primer caso, se analiza el impacto del ruido aditivo en la ganancia y la convergencia del algoritmo. Además, se propone una variación que realiza el control de lóbulos secundarios. En el segundo esquema, los sensores eligen intervalos de tiempo de forma aleatoria para transmitir y posteriormente reciben información de la estación base para ajustar sus osciladores. En este escenario, esta tesis doctoral aborda el impacto del número de intervalos de tiempo y el ruido aditivo sobre la ganancia de conformación

Optimization of hybrid cache placement for collaborative relaying

Traditional wireless multi-hop relaying systems suffer from inefficient use of bandwidth resources. This letter studies the use of content caching at distributed relays to tackle this problem and improve the performance of collaborative relaying. We propose a hybrid caching scheme that is jointly optimized with the transmission schemes, to achieve a fine balance between the signal cooperation gain and the caching diversity gain. The optimization problem of cache placement to minimize the outage probability is studied and is shown to be convex. Numerical results demonstrate significant outage performance gains over traditional relaying without caching

Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges

As a promising paradigm for fifth generation (5G) wireless communication systems, cloud radio access networks (C-RANs) have been shown to reduce both capital and operating expenditures, as well as to provide high spectral efficiency (SE) and energy efficiency (EE). The fronthaul in such networks, defined as the transmission link between a baseband unit (BBU) and a remote radio head (RRH), requires high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained C-RANs, including system architectures and key techniques. In particular, key techniques for alleviating the impact of constrained fronthaul on SE/EE and quality of service for users, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization, are discussed. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin note: text overlap with arXiv:1407.3855 by other author

Optimising Cooperative Spectrum Sensing in Cognitive Radio Networks Using Interference Alignment and Space-Time Coding

In this thesis, the process of optimizing Cooperative Spectrum Sensing in Cognitive Radio has been investigated in fast-fading environments where simulation results have shown that its performance is limited by the Probability of Reporting Errors. By proposing a transmit diversity scheme using Differential space-time block codes (D-STBC) where channel state information (CSI) is not required and regarding multiple pairs of Cognitive Radios (CR’s) with single antennas as a virtual MIMO antenna arrays in multiple clusters, Differential space-time coding is applied for the purpose of decision reporting over Rayleigh channels. Both Hard and Soft combination schemes were investigated at the fusion center to reveal performance advantages for Hard combination schemes due to their minimal bandwidth requirements and simplistic implementation. The simulations results show that this optimization process achieves full transmit diversity, albeit with slight performance degradation in terms of power with improvements in performance when compared to conventional Cooperative Spectrum Sensing over non-ideal reporting channels. Further research carried out in this thesis shows performance deficits of Cooperative Spectrum Sensing due to interference on sensing channels of Cognitive Radio. Interference Alignment (IA) being a revolutionary wireless transmission strategy that reduces the impact of interference seems well suited as a strategy that can be used to optimize the performance of Cooperative Spectrum Sensing. The idea of IA is to coordinate multiple transmitters so that their mutual interference aligns at their receivers, facilitating simple interference cancellation techniques. Since its inception, research efforts have primarily been focused on verifying IA’s ability to achieve the maximum degrees of freedom (an approximation of sum capacity), developing algorithms for determining alignment solutions and designing transmission strategies that relax the need for perfect alignment but yield better performance. With the increased deployment of wireless services, CR’s ability to opportunistically sense and access the unused licensed frequency spectrum, without causing harmful interference to the licensed users becomes increasingly diminished, making the concept of introducing IA in CR a very attractive proposition. For a multiuser multiple-input–multiple-output (MIMO) overlay CR network, a space-time opportunistic IA (ST-OIA) technique has been proposed that allows spectrum sharing between a single primary user (PU) and multiple secondary users (SU) while ensuring zero interference to the PUs. With local CSI available at both the transmitters and receivers of SUs, the PU employs a space-time WF (STWF) algorithm to optimize its transmission and in the process, frees up unused eigenmodes that can be exploited by the SU. STWF achieves higher performance than other WF algorithms at low to moderate signal-to-noise ratio (SNR) regimes, which makes it ideal for implementation in CR networks. The SUs align their transmitted signals in such a way their interference impairs only the PU’s unused eigenmodes. For the multiple SUs to further exploit the benefits of Cooperative Spectrum Sensing, it was shown in this thesis that IA would only work when a set of conditions were met. The first condition ensures that the SUs satisfy a zero interference constraint at the PU’s receiver by designing their post-processing matrices such that they are orthogonal to the received signal from the PU link. The second condition ensures a zero interference constraint at both the PU and SUs receivers i.e. the constraint ensures that no interference from the SU transmitters is present at the output of the post-processing matrices of its unintended receivers. The third condition caters for the multiple SUs scenario to ensure interference from multiple SUs are aligned along unused eigenmodes. The SU system is assumed to employ a time division multiple access (TDMA) system such that the Principle of Reciprocity is employed towards optimizing the SUs transmission rates. Since aligning multiple SU transmissions at the PU is always limited by availability of spatial dimensions as well as typical user loads, the third condition proposes a user selection algorithm by the fusion centre (FC), where the SUs are grouped into clusters based on their numbers (i.e. two SUs per cluster) and their proximity to the FC, so that they can be aligned at each PU-Rx. This converts the cognitive IA problem into an unconstrained standard IA problem for a general cognitive system. Given the fact that the optimal power allocation algorithms used to optimize the SUs transmission rates turns out to be an optimal beamformer with multiple eigenbeams, this work initially proposes combining the diversity gain property of STBC, the zero-forcing function of IA and beamforming to optimize the SUs transmission rates. However, this solution requires availability of CSI, and to eliminate the need for this, this work then combines the D-STBC scheme with optimal IA precoders (consisting of beamforming and zero-forcing) to maximize the SUs data rates