Throughput optimization for data collection in wireless sensor networks

Wireless sensor networks are widely used in many application domains in recent years. Data collection is a fundamental function provided by wireless sensor networks. How to efficiently collect sensing data from all sensor nodes is critical to the performance of sensor networks. In this dissertation, we aim to study the theoretical limits of data collection in a TDMA-based sensor network in terms of possible and achievable maximum capacity. Various communication scenarios are considered in our analysis, such as with a single sink or multiple sinks, randomly-deployed or arbitrarily- deployed sensors, and different communication models. For both randomly-deployed and arbitrarily-deployed sensor networks, an efficient collection algorithm has been proposed under protocol interference model and physical interference model respec- tively. We can prove that its performance is within a constant factor of the optimal for both single sink and regularly-deployed multiple sinks cases. We also study the capacity bounds of data collection under a general graph model, where two nearby nodes may be unable to communicate due to barriers or path fading, and discuss per- formance implications. In addition, we further discuss the problem of data collection capacity under Gaussian channel model

Interference pricing mechanism for downlink multicell coordinated beamforming

We consider the downlink coordinated beamforming problem in a cellular network in which the base stations (BSs) are equipped with multiple antennas and each user is equipped with a single antenna. The BSs cooperate in sharing their local interference information, and they aim to maximize the sum-rate of the users in the network. A decentralized interference pricing beamforming (IPBF) algorithm is proposed to identify the coordinated beamformer, where a BS is penalized according to the interference it creates to its peers. We show that the decentralized pricing mechanism converges to an interference equilibrium, which is a KKT point of the sum-rate maximization problem. The proofs rely on the identification of rank-1 solutions of each BSs' interference-penalized rate maximization problem. Numerical results show that the proposed iterative mechanism reduces significantly the exchanged information with respect to other state-of-the-art beamforming algorithms with very little sum-rate loss. The version of the algorithm that limits the coordination to a cluster of base stations (IPBF-L) is shown to have very small sum-rate loss with respect to the full coordinated algorithm with much less backhaul information exchange.The work was partially supported by NSF grant CCF-1017982 and SICCNALS project (TEC2011-28219). The work of A. García was partially supported by NSF grant CCF-1017982. A. García-Armada’s work has been partially funded by research projects COMONSENS (CSD2008-00010) and GRE3N (TEC2011-29006-C03-02)Publicad

Digital Acoustic Software Radio

The paradigm for developing communication systems has begun to shift away from traditional analog hardware designs towards systems implemented in software and reconfigurable digital hardware. The Digital Acoustic Software Radio, developed in this project, uses the software radio paradigm and acoustic signals to transmit data wirelessly between two computers. Two fully functioning non-coherent 300 baud communication systems were implemented using Matlab, and their performance was evaluated in various acoustic environments. Reliable communication was achieved at distances up to 15 feet

Locally Decodable Index Codes

An index code for broadcast channel with receiver side information is locally decodable if each receiver can decode its demand by observing only a subset of the transmitted codeword symbols instead of the entire codeword. Local decodability in index coding is known to reduce receiver complexity, improve user privacy and decrease decoding error probability in wireless fading channels. Conventional index coding solutions assume that the receivers observe the entire codeword, and as a result, for these codes the number of codeword symbols queried by a user per decoded message symbol, which we refer to as locality, could be large. In this paper, we pose the index coding problem as that of minimizing the broadcast rate for a given value of locality (or vice versa) and designing codes that achieve the optimal trade-off between locality and rate. We identify the optimal broadcast rate corresponding to the minimum possible value of locality for all single unicast problems. We present new structural properties of index codes which allow us to characterize the optimal trade-off achieved by: vector linear codes when the side information graph is a directed cycle; and scalar linear codes when the minrank of the side information graph is one less than the order of the problem. We also identify the optimal trade-off among all codes, including non-linear codes, when the side information graph is a directed 3-cycle. Finally, we present techniques to design locally decodable index codes for arbitrary single unicast problems and arbitrary values of locality.Comment: Accepted for publication in the IEEE Transactions on Information Theory. Parts of this manuscript were presented at IEEE ISIT 2018 and IEEE ISIT 2019. This arXiv manuscript subsumes the contents of arXiv:1801.03895 and arXiv:1901.0590

Instantly Decodable Network Coding: From Point to Multi-Point to Device-to-Device Communications

The network coding paradigm enhances transmission efficiency by combining information flows and has drawn significant attention in information theory, networking, communications and data storage. Instantly decodable network coding (IDNC), a subclass of network coding, has demonstrated its ability to improve the quality of service of time critical applications thanks to its attractive properties, namely the throughput enhancement, delay reduction, simple XOR-based encoding and decoding, and small coefficient overhead. Nonetheless, for point to multi-point (PMP) networks, IDNC cannot guarantee the decoding of a specific new packet at individual devices in each transmission. Furthermore, for device-to-device (D2D) networks, the transmitting devices may possess only a subset of packets, which can be used to form coded packets. These challenges require the optimization of IDNC algorithms to be suitable for different application requirements and network configurations. In this thesis, we first study a scalable live video broadcast over a wireless PMP network, where the devices receive video packets from a base station. Such layered live video has a hard deadline and imposes a decoding order on the video layers. We design two prioritized IDNC algorithms that provide a high level of priority to the most important video layer before considering additional video layers in coding decisions. These prioritized algorithms are shown to increase the number of decoded video layers at the devices compared to the existing network coding schemes. We then study video distribution over a partially connected D2D network, where a group of devices cooperate with each other to recover their missing video content. We introduce a cooperation aware IDNC graph that defines all feasible coding and transmission conflictfree decisions. Using this graph, we propose an IDNC solution that avoids coding and transmission conflicts, and meets the hard deadline for high importance video packets. It is demonstrated that the proposed solution delivers an improved video quality to the devices compared to the video and cooperation oblivious coding schemes. We also consider a heterogeneous network wherein devices use two wireless interfaces to receive packets from the base station and another device concurrently. For such network, we are interested in applications with reliable in-order packet delivery requirements. We represent all feasible coding opportunities and conflict-free transmissions using a dual interface IDNC graph. We select a maximal independent set over the graph by considering dual interfaces of individual devices, in-order delivery requirements of packets and lossy channel conditions. This graph based solution is shown to reduce the in-order delivery delay compared to the existing network coding schemes. Finally, we consider a D2D network with a group of devices experiencing heterogeneous channel capacities. For such cooperative scenarios, we address the problem of minimizing the completion time required for recovering all missing packets at the devices using IDNC and physical layer rate adaptation. Our proposed IDNC algorithm balances between the adopted transmission rate and the number of targeted devices that can successfully receive the transmitted packet. We show that the proposed rate aware IDNC algorithm reduces the completion time compared to the rate oblivious coding scheme

Signal design for Multiple-Antenna Systems and Wireless Networks

This dissertation is concerned with the signal design problems for Multiple Input and Multiple Output (MIMO) antenna systems and wireless networks. Three related but distinct problems are considered.The first problem considered is the design of space time codes for MIMO systems in the case when neither the transmitter nor the receiver knows the channel. We present the theoretical concept of communicating over block fading channel using Layered Unitary Space Time Codes (LUSTC), where the input signal is formed as a product of a series of unitary matrices with corresponding dimensionality. We show the channel capacity using isotropically distributed (i.d.) input signaling and optimal decoding can be achieved by layered i.d. signaling scheme along with a low complexity successive decoding. The closed form layered channel capacity is obtained, which serves as a design guideline for practical LUSTC. In the design of LUSTC, a successive design method is applied to leverage the problem of optimizing over lots of parameters.The feedback of channel state information (CSI) to the transmitter in MIMO systems is known to increase the forward channel capacity. A suboptimal power allocation scheme for MIMO systems is then proposed for limited rate feedback of CSI. We find that the capacity loss of this simple scheme is rather small compared to the optimal water-filling solution. This knowledge is applied for the design of the feedback codebook. In the codebook design, a generalized Lloyd algorithm is employed, in which the computation of the centroid is formulated as an optimization problem and solved optimally. Numerical results show that the proposed codebook design outperforms the existing algorithms in the literature.While it is not feasible to deploy multiple antennas in a wireless node due to the space limitation, user cooperation is an alternative to increase performance of the wireless networks. To this end, a coded user cooperation scheme is considered in the dissertation, which is shown to be equivalent to a coding scheme with the encoding done in a distributive manner. Utilizing the coding theoretic bound and simulation results, we show that the coded user cooperation scheme has great advantage over the non-cooperative scheme

Application-Aware Cross-Layer Framework for Wireless Multihop Networks

Current and future mobile and social communications require a rethinking in the development of wireless communication. Optimizing the radio transmission method is not going to scale with the ever increasing user demands. The future internet requires a wireless communication network which can adapt seamlessly to changing environments and service requirements. Especially, service requirements driven by user demand and expanding user device diversity raise a key challenge with respect to content distribution. In this work, research is conducted to improve wireless communication by considering four main aspects: The first aspect is to build a multi layer solution, instead of a conventional single layer solution to achieve higher throughput gains. Here, the physical layer, the medium access layer and the network layer are studied together to utilize capabilities across all these three layers. Thus, a unified graph model is formulated to adapt available mechanisms on the lower three layers in a joint manner. The second aspect is to envision a wireless multihop network which can scale with the increasing number of mobile devices. On the one hand, the number of mobile devices is ever increasing and so is the density of mobile devices in any given network. On the other hand, the requirements and capabilities of mobile devices are becoming more diverse and hence the heterogeneity in a wireless network is growing. This leads to the conclusion that a wireless multihop network is more future proof compared to a wireless network composed only of several base stations. Therefore, the research is focused on wireless multihop scenarios where multiple wireless devices form the network and communication between them occurs over multiple hops. The third aspect is to incorporate different requirements of applications and capabilities of applications. The plethora of applications used in wireless networks come with different sets of requirements, e.g. bandwidth, and capabilities, e.g. adaption of the video quality. Taking into account these requirements and capabilities in addition to a multi layer solution can further increase the performance. In this work, the requirements and capabilities of adaptive video streaming are integrated into an application-aware cross-layer framework. More precisely, scalable video coding and dynamic adaptive streaming over HTTP are integrated into the aforementioned framework. The novel application-aware cross-layer framework adapts network support structures at the network layer, performs resource allocation at the medium access layer, switches between communication types at the physical layer and takes into account the capabilities and requirements of applications, e.g. adaptive video-streaming, at the application layer. The fourth aspect is to utilize aggregation of distributed content, where content is cached over the whole network and can than be aggregated to be consumed by users in the network. Recent research shows promising gains achievable when content is cached at mobile devices, but mostly for single hop wireless networks. Hence, the impact of mobile content caching where popular content is cached and aggregated over multiple devices in a network is investigated in this work. In more detail, a content delivery framework which jointly exploits content already cached at mobile devices as well as switching between mechanisms at the physical layer and the network layer in order to optimally deliver the content to all destinations under changing network conditions is proposed

Locally Decodable Index Codes

An index code for broadcast channel with receiver side information is locally decodable if each receiver can decode its demand by observing only a subset of the transmitted codeword symbols instead of the entire codeword. Local decodability in index coding is known to reduce receiver complexity, improve user privacy and decrease decoding error probability in wireless fading channels. Conventional index coding solutions assume that the receivers observe the entire codeword, and as a result, for these codes the number of codeword symbols queried by a user per decoded message symbol, which we refer to as locality, could be large. In this paper, we pose the index coding problem as that of minimizing the broadcast rate for a given value of locality (or vice versa) and designing codes that achieve the optimal trade-off between locality and rate. We identify the optimal broadcast rate corresponding to the minimum possible value of locality for all single unicast problems. We present new structural properties of index codes which allow us to characterize the optimal trade-off achieved by: vector linear codes when the side information graph is a directed cycle; and scalar linear codes when the minrank of the side information graph is one less than the order of the problem. We also identify the optimal trade-off among all codes, including non-linear codes, when the side information graph is a directed 3-cycle. Finally, we present techniques to design locally decodable index codes for arbitrary single unicast problems and arbitrary values of locality