Integrated sensing and communication (ISAC) is a cornerstone technology for 6G networks, offering unified support for high-rate communication and high-accuracy sensing. While existing literature extensively covers link-level designs, the transition toward large-scale deployment necessitates a fundamental understanding of network-level performance. This paper investigates a network ISAC model where a source node communicates with a destination via a relay network, while intermediate nodes concurrently perform cooperative sensing over specific spatial regions. We formulate a novel optimization framework that captures the interplay between multi-node routing and sensing coverage. For a one-dimensional path network, we provide an analytical characterization of the complete sensing-throughput region. Extending this to general network topologies, we establish that the sensing-throughput Pareto boundary is piecewise linear and provide physical interpretations for each segment. Our results reveal the fundamental trade-offs between sensing coverage and communication routing, offering key insights for the design of future 6G heterogeneous networks.

The problem of bistatic integrated sensing and communications over memoryless relay channels is considered, where destination concurrently decodes the message sent by the source and estimates unknown parameters from received signals with the help of a relay. A state-dependent discrete memoryless relay channel is considered to model this setup, and the fundamental limits of the communication-sensing performance tradeoff are characterized by the capacity-distortion function. An upper bound on the capacity-distortion function is derived, extending the cut-set bound results to address the sensing operation at the destination. A hybrid-partial-decode-and-compress-forward coding scheme is also proposed to facilitate source-relay cooperation for both message transmission and sensing, establishing a lower bound on the capacity-distortion function. It is found that the hybrid-partial-decode-and-compress-forward scheme achieves optimal sensing performance when the communication task is ignored. Furthermore, the upper and lower bounds are shown to coincide for three specific classes of relay channels. Numerical examples are provided to illustrate the communication-sensing tradeoff and demonstrate the benefits of integrated design.

We characterise bounds on the optimal broadcast rate for a few classes of pliable-index-coding instances. Unlike the majority of currently solved instances, which belong to a special class where all receivers with a certain side-information cardinality are either present or absent, we consider more general instances without this constraint. We devise a novel algorithm that constructs a decoding chain by iteratively adding a message that can be decoded by a receiver whose side information is already in the chain. If the decoding chain cannot proceed due to the absence of a receiver with the required messages, weskipa message by adding it to the chain regardless. We prove that a lower bound on the optimal broadcast rate is a function of the number of skipped messages, across all possible decoding choices of the receivers and any realisation of the algorithm for each decoding choice. While this result is not computationally feasible in isolation, it serves as a basis for deriving explicit lower bounds on the broadcast rate for specific classes of pliable-index-coding instances. These lower bounds depend on the number of absent receivers or the pattern of their side-information sets. Specifically, we explicitly characterise the optimal broadcast rate for instances with up to and including four absent receivers with any side-information pattern, as well as instances where the side-information sets are nested in particular ways.

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results.

It was shown that by operating over the integer linear combinations (ILCs) ofKusers’ messages, lattice-code based multiple-access (LCMA) offers increased system load and improved error-rate performance over non-lattice based schemes. This paper advances the existing LCMA system in two aspects. 1) We formulate the spreading sequences optimization problem based on the achievable symmetric rate of LCMA. To solve this problem, we develop three new methods, namely target-switching steepest descent (TS-SD), particle swarm (PS) optimization, and Hadamard concatenation (HC). The TS-SD method always targets on the ILC with the lowest computation rate in the SD process. The PS method treats the spreading matrix as a particle and iteratively updates a swamp of particles’ positions and velocities, based on the relative distance to the best position that are currently known. To further reduce the complexity, we first obtain a solution in a lower dimension, and then apply Hadamard concatenation (HC) which yields a solution for the required dimension. The PS and HC methods are shown to approach the capacity of the MA channel. 2) We put forth a new multi-stage LCMA receiver. In each stage, the receiver attempts to compute as many ILCs as possible. Then, from these ILCs, generalized matrix inversion (GMI) is introduced to recover a subset ofKusers’ messages. These recovered messages are cancelled from the original received signal, yielding an equivalent system with less users for the next stage. Such operation continues successively until allKusers’ messages are recovered. System loads of up to 400% and near capacity performance are demonstrated for various MA models.

Integrated sensing and communication (ISAC) emerges as a critical technology for future 6G cellular networks. The distinct nature of sensing-centric and communication-centric waveforms poses a challenge, as a unified design can lead to a performance tradeoff between sensing and communication. Most of the previous studies focused on single ISAC base station (BS) scenarios and explored capacity-distortion tradeoffs for various ISAC channels. However, practical scenarios involve multiple ISAC BSs in the same region, sharing time-frequency resources and causing interference, which impacts both sensing and communication functions. To address this challenge, we propose an information-theoretic modeling of monostatic ISAC over interference channels. In the model, two interfering ISAC BSs seek to communicate with their users while performing sensing estimation through received echo signals. An achievable scheme is developed for the considered model, utilizing superposition coding and joint compression of past transmitted codewords and echo signals via distributed Wyner-Ziv coding. The corresponding achievable rate-distortion region is characterized, and a specific example is provided to demonstrate the advantages of the proposed scheme. Our results highlight that interference links, in conjunction with echo signals, can be strategically leveraged to achieve unified cooperative sensing and communication between the two BS-user pairs.

A relay-aided bistatic integrated sensing and communication (ISAC) system is considered, where the destination concurrently decodes a message and estimates unknown state parameters from its received signals. This system is modeled by a generalized state-dependent relay channel. Its fundamental limits of the communication-sensing performance tradeoff, characterized by the capacity-distortion function, are established. Specifically, an upper bound on the capacity-distortion function extending the cutset bound for relay channels to address the state estimation at the destination is developed. Additionally, a hybrid partial-decode-and-compress-forward coding scheme is proposed to facilitate source-relay cooperation for both message transmission and state estimation, establishing a lower bound on the capacity-distortion function. It is found that partial-decode-and-compress-forward scheme achieves optimal sensing performance when the communication task is ignored. Furthermore, the upper and lower bounds are shown to coincide for some special classes of channels. Two numerical examples are provided to illustrate the communication-sensing tradeoff in the considered ISAC system.

This paper introduces a novel class of PICOD$(t)$problems referred to as g-group complete-S PICOD$(t)$problems. It constructs a multi-stage achievability scheme to generate pliable index codes for group complete PICOD problems when$S=\{s\}$is a singleton set. Using the maximum acyclic induced sub graph bound, lower bounds on the broadcast rate are derived for singleton$S$, which establishes the optimality of the achievable scheme for a range of values for$t$and for any$g$and$s$. For all other values, it is shown that the achievability scheme is optimal among a restricted class of broadcast codes.

Pliable index coding studies flexible communication networks where each receiver just needs to receive any message that it does not already have. In this work, we consider a more practical but restricted scenario where each receiver wants any message it does not have from a particular subset of messages. We first adapt coding schemes from pliable index coding to this new restricted pliable index coding setting. We show that the adapted scheme is optimal under certain conditions. We simplify the computational complexity when constructing coding schemes for restricted pliable index coding from exponential to linear. We also construct two new coding schemes for the restricted setting, which can outperform the adapted scheme.

Integrated sensing and communication (ISAC) is a promising technology for future 6G networks that enables the joint utilization of hardware and spectrum resources for sensing and communication systems. However, the co-sharing of resources leads to a fundamental tradeoff between sensing and communication performance, which is not well understood in multiple-access ISAC scenarios with perfect or imperfect channel state information at the receiver (CSIR). In this paper, we address this challenge by considering a state-dependent multiple access channel model that accounts for correlated sensing and channel states, as well as imperfect CSIR. We propose an achievable scheme that combines message cooperation and joint compression via distributed Wyner-Ziv coding at each user, resulting in unified cooperative communication and sensing. Our scheme always achieves a communication-rate-distortion region which includes that achieved by state-of-the-art coding scheme. In addition, a numerical example is provided to demonstrate strict inclusion. It is found that the compressed information not only enhances communication (especially in scenarios with imperfect CSIR) but also improves sensing performance.

We propose and study a variant of pliable index coding (PICOD) where receivers have preferences for their unknown messages and give each unknown message a preference ranking. We call this the preferential pliable index-coding (PPICOD) problem and study the Pareto trade-off between the code length and overall satisfaction metric among all receivers. We derive theoretical characteristics of the PPICOD problem in terms of interactions between achievable code length and satisfaction metric. We also conceptually characterise two methods for computation of the Pareto boundary of the set of all achievable code length-satisfaction pairs. As for a coding scheme, we extend the Greedy Cover Algorithm for PICOD by Brahma and Fragouli, 2015, to balance the number of satisfied receivers and average satisfaction metric in each iteration. We present numerical results which show the efficacy of our proposed algorithm in approaching the Pareto boundary, found via brute-force computation.

Information leakage to a guessing adversary in index coding is studied, where some messages in the system are sensitive and others are not. The non-sensitive messages can be used by the server like secret keys to mitigate leakage of the sensitive messages to the adversary. We construct a deterministic linear coding scheme, developed from the rank minimization method based on fitting matrices (Bar-Yossef et al. 2011). The linear scheme leads to a novel upper bound on the optimal information leakage rate, which is proved to be tight over all deterministic scalar linear codes. We also derive a converse result from a graph-theoretic perspective, which holds in general over all deterministic and stochastic coding schemes.

In the pliable variant of index coding, receivers are allowed to decode any new message not known a priori. Optimal code design for this variant involves identifying each receiver’s choice of a new message that minimises the overall transmission rate. This paper proposes a formulation that further relaxes the decoding requirements of pliable index coding by allowing receivers to decode different new messages depending on message realisations. Such relaxation is shown to offer no rate benefit when linear codes are used, but can achieve strictly better rates in general. Scenarios are demonstrated for which the transmission rates are better when the message size is finite than when it is asymptotically large. This is in stark contrast to traditional communication setups.

Two main privacy models for sanitising datasets are differential privacy (DP) and syntactic privacy. The former restricts individual values’ impact on the output based on the dataset while the latter restructures the dataset before publication to link any record to multiple sensitive data values. Besides both providing mechanisms to sanitise data, these models are often applied independently of each other and very little is known regarding how they relate. Knowing how privacy models are related can help us develop a deeper understanding of privacy and can inform how a single privacy mechanism can fulfil multiple privacy models. In this paper, we introduce a framework that determines if the privacy mechanisms of one privacy model can also guarantee privacy for another privacy model. We apply our framework to understand the relationship between DP and a form of syntactic privacy called t-closeness. We demonstrate, for the first time, how DP and t-closeness can be interpreted in terms of each other by introducing generalisations and extensions of both models to explain the transition from one model to the other. Finally, we show how applying one mechanism to guarantee multiple privacy models increases data utility compared to applying separate mechanisms for each privacy model.

We study the information leakage to a guessing adversary in zero-error source coding. The source coding problem is defined by a confusion graph capturing the distinguishability between source symbols. The information leakage is measured by the ratio of the adversary's successful guessing probability after and before eavesdropping the codeword, maximized over all possible source distributions. Such measurement under the basic adversarial model where the adversary makes a single guess and the guess is regarded successful if and only if the estimator sequence equals to the true source sequence is known as the maximum min-entropy leakage or the maximal leakage in the literature. We develop a single-letter characterization of the optimal normalized leakage under the basic adversarial model, together with an optimum-achieving memoryless stochastic mapping scheme. An interesting observation is that the optimal normalized leakage is equal to the optimal compression rate with fixed-length source codes, both of which can be simultaneously achieved by some deterministic coding schemes. We then extend the leakage measurement to generalized adversarial models where the adversary makes multiple guesses and allows a certain level of distortion, for which we derive single-letter lower and upper bounds.

In this work, we study the secure index coding problem where there are security constraints on both legitimate receivers and eavesdroppers. We develop two performance bounds (i.e., converse results) on the symmetric secure capacity. The first one is an extended version of the basic acyclic chain bound (Liu and Sadeghi, 2019) that takes security constraints into account. The second converse result is a novel information-theoretic lower bound on the symmetric secure capacity, which is interesting as all the existing converse results in the literature for secure index coding give upper bounds on the capacity.

We study the information leakage to a guessing adversary in index coding with a general message distribution. Under both vanishing-error and zero-error decoding assumptions, we develop lower and upper bounds on the optimal leakage rate, which are based on the broadcast rate of the subproblem induced by the set of messages the adversary tries to guess. When the messages are independent and uniformly distributed, the lower and upper bounds match, establishing an equivalence between the two rates.

An equivalence was shown between network coding and index coding. The equivalence allows for a network code for any given network-coding instance to be translated to an index code for a suitably constructed index-coding instance, and vice versa. The equivalence also holds for the opposite direction. A secure version of the equivalence in the presence of eavesdroppers was proven for the case where there is no decoding error and no information leakage to the eavesdroppers. For the case of non-zero decoding error and non-zero leakage, three out of the four directions required for an equivalence were proven. This paper proves the last direction, thereby completing the equivalence between secure network coding and secure index coding.

We characterise the optimal broadcast rate for a few classes of pliable-index-coding problems. This is achieved by devising new lower bounds that utilise the set of absent receivers to construct decoding chains with skipped messages. This work complements existing works by considering problems that are not complete-S, i.e., problems considered in this work do not require that all receivers with a certain side-information cardinality to be either present or absent from the problem. We show that for a certain class, the set of receivers is critical in the sense that adding any receiver strictly increases the broadcast rate.

For communications in the presence of eavesdroppers, random components are often used in code design to camouflage information from eavesdroppers. In broadcast channels without eavesdroppers, Marton coding comprises random components which allow correlation between auxiliary random variables representing independent messages. In this paper, we study if Marton coding alone can ensure individual secrecy in the two-receiver discrete memoryless broadcast channel with a passive eavesdropper. Our results show that this is possible and Marton coding guarantees individual secrecy in accordance to the principle of Wyner secrecy coding. However, this comes with a penalty of requiring stricter channel conditions.

We consider a Multi-Sender Unicast Index-Coding (MSUIC) problem, where in a broadcast network, multiple senders collaboratively send distinct messages to multiple receivers, each having some subset of the messages a priori. The aim is to find the shortest index code that minimizes the total number of coded bits sent by the senders. In this paper, built on the classic single-sender minrank concept, we develop a new rank-minimization framework for MSUIC that explicitly takes into account the sender message constraints and minimizes the sum of the ranks of encoding matrices subject to the receiver decoding requirements. This framework provides a systematic way to construct multi-sender linear index codes and to study their capability in achieving the shortest index codelength per message length (i.e., the optimal broadcast rate). In particular, we establish the optimal broadcast rate for all critical MSUIC instances with up to four receivers and show that a binary linear index code is optimal for all, except 15 instances with four receivers. We also propose a heuristic algorithm (in lieu of exhaustive search) to solve the rank-minimization problem. The effectiveness of the algorithm is validated by numerical studies of MSUIC instances with four or more receivers.

In this paper, we propose a new coding scheme and establish new bounds on the capacity region for the multi-sender unicast index-coding problem. We revisit existing partitioned distributed composite coding (DCC) proposed by Sadeghi et al. and identify its limitations in the implementation of multi-sender composite coding and in the strategy of sender partitioning. We then propose two new coding components to overcome these limitations and develop a multi-sender cooperative composite coding (CCC). We show that CCC can strictly improve upon partitioned DCC, and is the key to achieve optimality for a number of index-coding instances. The usefulness of CCC and its special cases is illuminated via non-trivial examples, and the capacity region is established for each example. Comparisons between CCC and other non-cooperative schemes in recent works are also provided to further demonstrate the advantage of CCC.

The upcoming ESA Fluorescence Explorer (FLEX) mission will incorporate ground-based validations for fluorescence parameters and reflectance indices, drawing on an international network of sensors located at eddy covariance tower sites. A program has been initiated by the OPTIMISE program to develop methods and protocols for this network. A sensor system suite under evaluation by OPTIMISE includes the FLoX hyperspectral spectroradiometers. The NASA team at GSFC is participating in this experiment and we report first results from the 2017 summer measurements made above the canopy at the USDA/ARS Beltsville cornfield using the DFLoX and two other leaf-level measurement systems, the MONI-PAM and the FluoWat.

A linear code equivalence between index coding and network coding was shown by El Rouayheb et al., which establishes that for any index-coding instance, there exists a network-coding instance for which any index code can be mapped to a suitable network code, and vice versa. Similarly, for any network-coding instance, there exists an index-coding instance for which a similar code equivalence can be constructed. Effros et al. extended the equivalence to include non-linear codes. Subsequently, we extended the code equivalence to the secure communication setting in the presence of an eavesdropper, in which we impose perfect decodability and secrecy. In this paper, we generalise the equivalence between secure index coding and secure network coding to include non-zero decoding error and non-zero leakage.

We address a centralized caching problem with unequal cache sizes. We consider a system with a server of files connected via a shared error-free link to a group of cache-enabled users, where one subgroup has a larger cache size than the other, and the number of files in the server is at least as large as the number of users. We propose a caching scheme for the considered system aimed at minimizing the load of worst-case demands over the shared link. Numerical evaluations show that our scheme improves upon the best existing explicit scheme by having a lower worst-case load, and performs within a multiplicative factor of 1.11 from the optimal scheme with uncoded placement and linear coded delivery. Unlike the optimal scheme-for which the placement, the delivery, and the load can be obtained by solving an optimisation problem, and become intractable as the number of users grows-our proposed scheme is an explicit scheme.

This paper studies the problem of secure communication over the two-receiver discrete memoryless broadcast channel with one-sided receiver side information and with a passive eavesdropper. We proposed a coding scheme which is based upon the superposition-Marton framework. Secrecy techniques such as the one-time pad, Carleial-Hellman secrecy coding and Wyner secrecy coding are applied to ensure individual secrecy. This scheme is shown to be capacity achieving for some cases of the degraded broadcast channel. We also notice that one-sided receiver side information provides the advantage of rate region improvement, in particular when it is available at the weaker legitimate receiver.

We provide a correction to[1]in response to an error reported by Vaddi and Rajan[2]. To this effect, we add one extra condition for the definition of an$\mathsf {IC}$structure on page 3696.

This paper considers the downlink of a cache- enabled fog radio access network (F-RAN) with limited fronthaul capacity, where user association (UA), data delivery rate (DDR) and signal precoding are jointly optimized. We formulate a mixed-integer nonlinear programming problem in which the weighted difference of network throughput and total power consumption is maximized, subject to the predefined DDR requirements and the maximum transmit power at each eRRH. To address this challenging problem, we first apply the l0-norm approximation and l1-norm minimization techniques to deal with the UA. After this key step, we arrive at an approximated problem that only involves the joint optimization of DDR and precoding. By using the alternating descent method, we further decompose this problem into a convex subproblem for DDR allocation and a nonconvex subproblem for precoding design. While the former is globally solved by the interior-point method, the latter is solved by a specifically tailored successive convex quadratic programming method. Finally, we propose an iterative algorithm for the original joint optimization that is guaranteed to converge. Importantly, each iteration of the developed algorithm only involves solving simple convex problems. Numerical examples demonstrate that the proposed design significantly improves both throughput and power performances, especially in practical F-RANs with limited fronthaul capacity. Compared to the sole precoder design for a given cache placement, our joint design is shown to improve the throughput by 50% while saving at least half of the total power consumption in the considered examples.

In February 2017, the Earth Observing One (EO-1) satellite mission successfully completed sixteen years and three months of Earth imaging by its two unique instruments, the Hyperion and the Advanced Land Imager (ALI). Both instruments have served as prototypes for new orbital sensors. Hyperion has provided the only available global sample of the Earth's surface with: (i) passive optical mid-morning observations at moderate spatial resolution (30 m) to match the Landsat series; and (ii) spectral coverage over almost the full optical spectrum in 10 nm contiguous bands, in visible through shortwave infrared (VSWIR, 0.4-2.5 μm) wavelengths. Consequently, Hyperion is a heritage platform for future full-spectrum VSWIR orbital spectrometers, including the German mission, EnMAP (2019), and the NASA pre-Phase A (yet unscheduled) mission, the Hyperspectral InfraRed Imager (HyspIRI), defined by the 2007 Decadal Survey conducted by the US National Research Council. We provide an overview of the mission's lifetime and Hyperion's scientific and application accomplishments, including calibration & validation activities, data quality evaluations during end of mission precession changes to the orbit and overpass time, and the development of a user-friendly science quality archive.

Earth Observation (EO) satellite data are the single most important and richest source of environmental information for Australia. It is expected that future imaging spectroscopy satellites will be important for Australia for accessing critical information for managing natural and non-renewable resources, such as dry plant matter and mineralogy, which are currently difficult to access. Research and development has been undertaken by CSIRO to find an appropriate site to set up a vicarious calibration site aligned with Radiometric Calibration Network (RadCalNet) principles in response to a national need for calibration and validation of EO sensors. This paper reports specifically on the spatial and temporal compositional characteristics of the Pinnacles site using ASTER and Hyperion data.

We establish new capacity bounds for the multi-sender unicast index-coding problem. We first revisit existing bounds proposed by Sadeghi et al. and identify the suboptimality of their inner bounds in general. We then present a simplified version of the existing multi-sender maximal-acyclic-induced-subgraph outer bound. For the inner bound, we propose joint link-and-sender partitioning to replace sender partitioning in partitioned Distributed Composite Coding (DCC). This leads to a modified DCC (mDCC) that outperforms partitioned DCC and suffices to achieve optimality for some index-coding instances. We also propose cooperative compression of composite messages in composite coding to exploit messages common to different senders to support larger composite rates than those by point-to-point compression in the existing schemes. We then develop a new multi-sender Cooperative Composite Coding (CCC) scheme. CCC further improves upon mDCC in general, and is instrumental to achieve optimality for a number of index-coding instances.

This study considers the optimisation of multi‐edge type low‐density parity‐check (MET‐LDPC) codes to maximise the decoding threshold. The authors propose an algorithm to jointly optimise the node degree distribution and the multi‐edge structure of MET‐LDPC codes for given values of the maximum number of edge‐types and maximum node degrees. This joint optimisation is particularly important for MET‐LDPC codes as it is not clear a priori which structures will be good. Using several examples, they demonstrate that the MET‐LDPC codes designed by the proposed joint optimisation algorithm exhibit improved decoding thresholds compared with previously reported MET‐LDPC codes.

We consider the three-receiver Gaussian multiple-input multiple-output broadcast channel with an arbitrary number of antennas at the transmitter and the receivers. We investigate the degrees-of-freedom (DoF) region of the channel when each receiver requests a private message, and may know some of the messages requested by the other receivers as receiver message side information (RMSI). We establish the DoF region of the channel for all 16 possible non-isomorphic RMSI configurations by deriving tight inner and outer bounds on the region. To derive the inner bounds, we first propose a scheme for each RMSI configuration, which exploits both the null space and the side information of the receivers. We then use these schemes in conjunction with time sharing for 15 RMSI configurations, and with time sharing and two-symbol extension for the remaining one. To derive the outer bounds, we construct enhanced versions of the channel for each RMSI configuration, and upper bound their DoF region. After establishing the DoF region, in the case where all the nodes have the same number of antennas, we introduce some common properties of the DoF region, and the capacity region of the index coding problem.

The focus of this paper is on the analysis and design of Raptor codes using a multi-edge framework. In this regard, we first represent Raptor codes as multi-edge type (MET) low-density parity-check codes. This MET representation gives a general framework to analyze and design Raptor codes over a binary input additive white Gaussian noise channel using MET density evolution (MET-DE). We then consider a joint decoding scheme based on the belief propagation (BP) decoding for Raptor codes in the multi-edge framework, and analyze the convergence behavior of the BP decoder using MET-DE. In joint decoding of Raptor codes, the component codes corresponding to the inner code and the precode are decoded in parallel and provide information to each other. We also derive an exact expression for the stability of Raptor codes with joint decoding. We then propose an efficient Raptor code design method using the multi-edge framework, where we simultaneously optimize the inner code and the precode. Through density evolution analysis we show that the designed Raptor codes using the multi-edge framework outperform the existing Raptor codes in literature in terms of realized rates.

Index coding models broadcast networks in which a sender sends different messages to different receivers simultaneously, where each receiver may know some of the messages a priori. The aim is to find the minimum (normalized) index codelength that the sender sends. This paper considers unicast index coding, where each receiver requests exactly one message, and each message is requested by exactly one receiver. Each unicast index-coding instances can be fully described by a directed graph and vice versa, where each vertex corresponds to one receiver. For any directed graph representing a unicast index-coding instance, we show that if a maximum acyclic induced subgraph (MAIS) is obtained by removing two or fewer vertices from the graph, then the minimum index codelength equals the number of vertices in the MAIS, and linear codes are optimal for the corresponding index-coding instance. Using this result, we solved all unicast index-coding instances with up to five receivers, which correspond to all graphs with up to five vertices. For 9818 non-isomorphic graphs among all graphs up to five vertices, we obtained the minimum index codelength for all message alphabet sizes; for the remaining 28 graphs, we obtained the minimum index codelength if the message alphabet size is k2for any positive integer k. This work complements the result by Arbabjolfaei et al. (ISIT 2013), who solved all unicast index-coding instances with up to five receivers in the asymptotic regime, where the message alphabet size tends to infinity.

We consider a graphical approach to index coding. As cycles have been shown to provide coding gain, cycles and cliques (a specific type of overlapping cycles) have been exploited in an existing literature. In this paper, we define a more general form of overlapping cycles, called the interlinked-cycle (IC) structure, that generalizes cycles and cliques. We propose a scheme, called the interlinked-cycle-cover (ICC) scheme, that leverages IC structures in digraphs to construct scalar linear index codes. We characterize a class of infinitely many digraphs where our proposed scheme is optimal over all linear and nonlinear index codes. Consequently, for this class of digraphs, we indirectly prove that scalar linear index codes are optimal. Furthermore, we show that the ICC scheme can outperform all the existing graph-based schemes (including partial-clique-cover and fractional-local-chromatic number schemes), and a random coding scheme (namely, composite coding) for certain graphs.

We consider the two-receiver memoryless broadcast channel with states where each receiver requests both common and private messages, and may know part of the private message requested by the other receiver as receiver message side information (RMSI). We address two categories of the channel (i) channel with states known causally to the transmitter, and (ii) channel with states known non-causally to the transmitter. Starting with the channel without RMSI, we first propose a transmission scheme and derive an inner bound for the causal category. We then unify our inner bound for the causal category and the best-known inner bound for the non-causal category, although their transmission schemes are different. Moving on to the channel with RMSI, we first apply a pre-coding to the transmission schemes of the causal and non-causal categories without RMSI. We then derive a unified inner bound as a result of having a unified inner bound when there is no RMSI, and applying the same pre-coding to both categories. We show that our inner bound is tight for some new cases as well as the cases whose capacity region was known previously.

We investigate the construction of weakly-secure index codes for a sender to send messages to multiple receivers with side information in the presence of an eavesdropper. We derive a sufficient and necessary condition for the existence of index codes that are secure against an eavesdropper with access to any subset of messages of cardinality t, for any fixed t. In contrast to the benefits of using random keys in secure network coding, we prove that random keys do not promote security in three classes of index-coding instances.

Spaceborne spectrometers require spectral-temporal stability characterization to aid in validation of derived data products. Earth Observation 1 (EO-1) began orbital precession in 2011 after exhausting onboard fuel resources. In the Libya-4 pseudoinvariant calibration site (PICS), this resulted in a progressive shift from a mean local equatorial crossing time of ~10:00 A.M. in 2011 to ~8:30 A.M. in late 2015. Here, we studied precession impacts to Hyperion surface reflectance products using three atmospheric correction approaches from 2004 to 2015. Combined difference estimates of surface reflectance were2) in VNIR from 0.25 to 0.94 and in SWIR from 0.12 to 0.88 (p <; 0.01). The uncertainties in all the models increased with a terrain slope up to 15° and selecting dune flats could reduce errors. We conclude that these data remain a valuable resource over this period for sensor intercalibration despite orbital decay.

This paper considers density evolution for low-density parity-check (LDPC) and multi-edge type LDPC (MET-LDPC) codes over the binary input additive white Gaussian noise channel. We first analyze three single-parameter Gaussian approximations for density evolution and discuss their accuracy under several conditions, namely, at low rates, with punctured and degree-one variable nodes. We observe that the assumption of symmetric Gaussian distribution for the density-evolution messages is not accurate in the early decoding iterations, particularly at low rates and with punctured variable nodes. Thus, single-parameter Gaussian approximation methods produce very poor results in these cases. Based on these observations, we then introduce a new density evolution approximation algorithm for LDPC and MET-LDPC codes. Our method is a combination of full density evolution and a single-parameter Gaussian approximation, where we assume a symmetric Gaussian distribution only after density-evolution messages closely follow a symmetric Gaussian distribution. Our method significantly improves the accuracy of the code threshold estimation. Additionally, the proposed method significantly reduces the computational time of evaluating the code threshold compared with full density evolution thereby making it more suitable for code design.

Index coding studies multiterminal source-coding problems where a set of receivers are required to decode multiple (possibly different) messages from a common broadcast, and they each know some messages a priori. In this paper, at the receiver end, we consider a special setting where each receiver knows only one message a priori, and each message is known to only one receiver. At the broadcasting end, we consider a generalized setting where there could be multiple senders, and each sender knows a subset of the messages. The senders collaborate to transmit an index code. This paper looks at minimizing the number of total coded bits the senders are required to transmit. When there is only one sender, we propose a pruning algorithm to find a lower bound on the optimal (i.e., the shortest) index codelength, and show that it is achievable by linear index codes. When there are two or more senders, we propose an appending technique to be used in conjunction with the pruning technique to give a lower bound on the optimal index codelength; we also derive an upper bound based on cyclic codes. While the two bounds do not match in general, for the special case where no two distinct senders know any message in common, the bounds match, giving the optimal index codelength. The results are expressed in terms of strongly connected components in directed graphs that represent the index-coding problems.

This paper investigates the capacity regions of two-receiver broadcast channels where each receiver (i) has both common and private-message requests, and (ii) knows part of the private message requested by the other receiver as side information. We first propose a transmission scheme and derive an inner bound for the two-receiver memoryless broadcast channel. We next prove that this inner bound is tight for the deterministic channel and the more capable channel, thereby establishing their capacity regions.We show that this inner bound is also tight for all classes of two-receiver broadcast channels whose capacity regions were known prior to this work. Our proposed scheme is consequently a unified capacity-achieving scheme for these classes of broadcast channels.

We consider the multi-way relay channel with common messages and full data exchange, where multiple users exchange correlated messages through a relay. We propose an optimal coding scheme and show that it achieves (i) the capacity region if the uplink is a finite-field channel and the downlink is any arbitrary channel and (ii) the full degrees-of-freedom region if the channel is a multiple-input multiple-output (MIMO) additive white Gaussian noise (AWGN) channel.

We study the half-duplex Gaussian two-way relay channel with direct user-to-user links. In this setup, two users exchange data via a relay and via direct user-to-user links. Due to the half-duplex constraint, the channel can be in one of eight different states at any time (two of which are useless: no node transmitting and no node listening). Restricting to only four states, we propose a scheme that utilizes lattice codes to improve upon existing four-state schemes. Using all six states, we propose another scheme that utilizes lattice codes and coherent combining, and show that it can outperform existing schemes.

We study the index coding problem in the unicast message setting, i.e., where each message is requested by one unique receiver. This problem can be modeled by a directed graph. We propose a new scheme called interlinked cycle cover, which exploits interlinked cycles in the directed graph, for designing index codes. This new scheme generalizes the existing clique cover and cycle cover schemes. We prove that for a class of infinitely many digraphs with messages of any length, interlinked cycle cover provides an optimal index code. Furthermore, the index code is linear with linear time encoding complexity.

We consider a joint source-channel coding problem on a finite-field multiway relay channel, and we give closed-form lower and upper bounds on the optimal source-channel rate. These bounds are shown to be tight for all discrete memoryless sources in a certain class P*, and we demonstrate that strict source-channel separation is optimal within this class. We show how to test whether a given source belongs to P*, we give a balanced-information regularity condition for P*, and we express P* in terms of conditional multiple-mutual information. Finally, we show that P* is useful for a centralized storage problem.

This paper investigates the capacity region of the three-receiver AWGN broadcast channel where the receivers: 1) have private-message requests and 2) may know some of the messages requested by other receivers as side information. We first classify all 64 possible side information configurations into eight groups, each consisting of eight members. We next construct transmission schemes, and derive new inner and outer bounds for the groups. This establishes the capacity region for 52 out of 64 possible side information configurations. For six groups (i.e., groups 1, 2, 3, 5, 6, and 8 in our terminology), we establish the capacity region for all their members, and show that it tightens both the best-known inner and outer bounds. For group 4, our inner and outer bounds tighten the best-known inner bound and/or outer bound for all the group members. Moreover, our bounds coincide at certain regions, which can be characterized by two thresholds. For group 7, our inner and outer bounds coincide for four members, and thereby establishing the capacity region. For the remaining four members, our bounds tighten both the best-known inner and outer bounds.

This paper considers the finite-field multi-way relay channel with pairwise message sharing, where multiple users exchange messages through a single relay and where the users may share parts of their source messages (meaning that some message parts are known/common to more than one user). In this paper, we design an optimal functional-decode-forward coding scheme that takes the shared messages into account. More specifically, we design an optimal function for the relay to decode (from the users on the uplink) and forward (back to the users on the downlink). We then show that this proposed function-decode-forward coding scheme can achieve the capacity region of the finite-field multi-way relay channel with pairwise message sharing. This paper generalizes our previous result for the case of three users to any number of users.

This paper investigates the capacity region of three-receiver AWGN broadcast channels where the receivers (i) have private-message requests and (ii) know the messages requested by some other receivers as side information. We classify these channels based on their side information into eight groups, and construct different transmission schemes for the groups. For six groups, we characterize the capacity region, and show that it improves both the best known inner and outer bounds. For the remaining two groups, we improve the best known inner bound by using side information during channel decoding at the receivers.

We study zero-error unicast index-coding instances, where each receiver must perfectly decode its requested message set, and the message sets requested by any two receivers do not overlap. We show that for all these instances with up to five receivers, linear index codes are optimal. Although this class contains 9847 non-isomorphic instances, by using our recent results and by properly categorizing the instances based on their graphical representations, we need to consider only 13 non-trivial instances to solve the entire class. This work complements the result by Arbabjolfaei et al. (ISIT 2013), who derived the asymptotic capacity region of all unicast index-coding problems with up to five receivers in the diminishing-error setup. They employed random-coding arguments, which require infinitely-long messages. We consider the zero-error setup; our approach uses graph theory and combinatorics, and does not require long messages.

Abstract—This paper considers the three-receiver AWGN broadcast channel where the receivers (i) have private-message requests and (ii) know some of the messages requested by other receivers as side information. For this setup, all possible side information configurations have been recently classified into eight groups and the capacity of the channel has been established for six groups (Asadi et al., ISIT 2014). We propose inner and outer bounds for the two remaining groups, groups 4 and 7. A distinguishing feature of these two groups is that the weakest receiver knows the requested message of the strongest receiver as side information while the in-between receiver does not. For group 4, the inner and outer bounds coincide at certain regions. For group 7, the inner and outer bounds coincide, thereby establishing the capacity, for four members out of all eight members of the group; for the remaining four members, the proposed bounds reduce the gap between the best known inner and outer bounds. I.

A low-density parity-check (LDPC) code is a linear block code described by a sparse parity-check matrix, which can be efficiently represented by a bipartite Tanner graph. The standard iterative decoding algorithm, known as belief propagation, passes messages along the edges of this Tanner graph. Density evolution is an efficient method to analyze the performance of the belief propagation decoding algorithm for a particular LDPC code ensemble, enabling the determination of a decoding threshold. The basic problem addressed in this work is how to optimize the Tanner graph so that the decoding threshold is as large as possible. We introduce a new code optimization technique which involves the search space range which can be thought of as minimizing randomness in differential evolution or limiting the search range in exhaustive search. This technique is applied to the design of good irregular LDPC codes and multi-edge type LDPC codes.

We focus on the following instance of an index coding problem, where a set of receivers are required to decode multiple messages, whilst each knows one of the messages a priori. In particular, here we consider a generalized setting where they are multiple senders, each sender only knows a subset of messages, and all senders are required to collectively transmit the index code. For a single sender, Ong and Ho (ICC, 2012) have established the optimal index codelength, where the lower bound was obtained using a pruning algorithm. In this paper, the pruning algorithm is simplified, and used in conjunction with an appending technique to give a lower bound to the multi-sender case. An upper bound is derived based on network coding. While the two bounds do not match in general, for the special case where no two senders know any message bit in common, the bounds match, giving the optimal index codelength. The results are derived based on graph theory, and are expressed in terms of strongly connected components.

This paper studies the three-user finite-field multi-way relay channel, where the users exchange messages via a relay. The messages are arbitrarily correlated, and the finite-field channel is linear and is subject to additive noise of arbitrary distribution. The problem is to determine the minimum achievable source-channel rate, defined as channel uses per source symbol needed for reliable communication. We combine Slepian-Wolf source coding and functional-decode-forward channel coding to obtain the solution for two classes of source and channel combinations. Furthermore, for correlated sources that have their common information equal their mutual information, we propose a new coding scheme to achieve the minimum source-channel rate.

We consider a multi-way relay network with an orthogonal uplink and correlated sources, and we characterise reliable communication (in the usual Shannon sense) with a single-letter expression. The characterisation is obtained using a joint source-channel random-coding argument, which is based on a combination of Wyner et al.'s Cascaded Slepian-Wolf Source Coding and Tuncel's Slepian-Wolf Coding over Broadcast Channels. We prove a separation theorem for the special case of two nodes; that is, we show that a modular code architecture with separate source and channel coding functions is (asymptotically) optimal. Finally, we propose a practical coding scheme based on low-density parity-check codes, and we analyse its performance using multi-edge density evolution.

This paper studies a special class of multicast index coding problems where a sender transmits messages to multiple receivers, each with some side information. Here, each receiver knows a unique message a priori, and there is no restriction on how many messages each receiver requests from the sender. For this class of multicast index coding problems, we obtain the optimal index code, which has the shortest codelength for which the sender needs to send in order for all receivers to obtain their (respective) requested messages. This is the first class of index coding problems where the optimal index codes are found. In addition, linear index codes are shown to be optimal for this class of index coding problems.

This paper considers the multi-way relay channel (MWRC) where multiple users exchange messages via a single relay. The capacity region is derived for a special class of MWRCs where (i) the uplink and the downlink are separated in the sense that there is no direct user-to-user links, (ii) the channel is restricted in the sense that each user's transmitted channel symbols can depend on only its own message, but not on its received channel symbols, and (iii) the uplink is any deterministic function.

The multi-way relay channel (MWRC) models cooperative communication networks in which many users exchange messages via a relay. In this paper, we consider the finite field MWRC with correlated messages. The problem is to find all achievable rates, defined as the number of channel uses required per reliable exchange of message tuple. For the case of three users, we have previously established that for a special class of source distributions, the set of all achievable rates can be found [Ong et al., ISIT 2010]. The class is specified by an almost balanced conditional mutual information (ABCMI) condition. In this paper, we first generalize the ABCMI condition to the case of more than three users. We then show that if the sources satisfy the ABCMI condition, then the set of all achievable rates is found and can be attained using a separate source-channel coding architecture.

This paper compares the partial-decode-forward and the complete-decode-forward coding strategies for the half-duplex Gaussian single-relay channel. We analytically show that the rate achievable by partial-decode-forward outperforms that of the more straightforward complete-decode-forward by at most 12.5%. Furthermore, in the following asymptotic cases, the gap between the partial-decode-forward and the complete-decode-forward rates diminishes: (i) when the relay is close to the source, (ii) when the relay is close to the destination, and (iii) when the SNR is low. In addition, when the SNR increases, this gap, when normalized to the complete-decode-forward rate, also diminishes. Consequently, significant performance improvements are not achieved by optimizing the fraction of data the relay should decode and forward, over simply decoding the entire source message.

The$L$-user additive white Gaussian noise multiway relay channel is investigated, where$L$users exchange information at the same rate through a single relay. A new achievable rate region, based on the functional-decode-forward coding strategy, is derived. For the case where there are three or more users, and all nodes transmit at the same power, the capacity is obtained. For the case where the relay power scales with the number of users, it is shown that both compress-forward and functional-decode-forward achieve rates within a constant number of bits of the capacity at all SNR levels; in addition, functional-decode-forward outperforms compress-forward and complete-decode-forward at high SNR levels.

We study the half-duplex multiple-relay channel (HD-MRC) where every node can either transmit or listen but cannot do both at the same time. We obtain a capacity upper bound based on a max-flow min-cut argument and achievable transmission rates based on the decode-forward (DF) coding strategy, for both the discrete memoryless HD-MRC and the phase-fading HD-MRC. We discover that both the upper bound and the achievable rates are functions of the transmit/listen state (a description of which nodes transmit and which receive). More precisely, they are functions of the time fraction of the different states, which we term a schedule. We formulate the optimal scheduling problem to find an optimal schedule that maximizes the DF rate. The optimal scheduling problem turns out to be a maximin optimization, for which we propose an algorithmic solution. We demonstrate our approach on a four-node multiple-relay channel, obtaining closed-form solutions in certain scenarios. Furthermore, we show that for the received signal-to-noise ratio degraded phase-fading HD-MRC, the optimal scheduling problem can be simplified to a max optimization.

We consider the two-way relay network where two nodes communicate via a relay. We assume that the data at the nodes are correlated (e.g., measurements in a sensor network) and that there is no direct communication between the nodes. The nodes communicate via the relay using a two-phase protocol consisting of an uplink part over an orthogonal multiple access channel and a downlink part over a broadcast channel. The individual codes as well as the overall system can be represented by a joint factor graph consisting of a source code at each node, a channel code for the each uplink and a channel code for the downlink. The optimality of separation of source and channel coding implies that it is optimal to individually design these codes. We focus on low-density parity-check codes where code design corresponds to the optimisation of their degree distributions.

The problem of loss less joint source-channel coding for the two-way relay network with an orthogonal multiple access channel is studied. Necessary and sufficient conditions for reliable communication are given, and a separation theorem for source and channel coding is proved.

This paper investigates the additive white Gaussian noise two-way relay channel, where two users exchange messages through a relay. Asymmetrical channels are considered where the users can transmit data at different rates and at different power levels. We modify and improve existing coding schemes to obtain three new achievable rate regions. Comparing four downlink-optimal coding schemes, we show that the scheme that gives the best sum-rate performance is (i) complete-decode-forward, when both users transmit at low signal-to-noise ratio (SNR); (ii) functional-decode-forward with nested lattice codes, when both users transmit at high SNR; (iii) functional-decode-forward with rate splitting and time-division multiplexing, when one user transmits at low SNR and another user at medium-high SNR.

The three-user finite field multi-way relay channel with correlated sources is considered. The three users generate possibly correlated messages, and each user is to transmit its message to the two other users reliably in the Shannon sense. As there is no direct link among the users, communication is carried out via a relay, and the link from the users to the relay and those from the relay to the users are finite field adder channels with additive noise of arbitrary distribution. The problem is to determine the set of all possible achievable rates, defined as channel uses per source symbol for reliable communication. For two classes of source/channel combinations, the solution is obtained using Slepian-Wolf source coding combined with functional-decode-forward channel coding.

The multiway relay channel is a multicast network whereLusers exchange data through a relay. In this paper, the capacity region of a class of multiway relay channels is derived, where the channel inputs and outputs take values over finite fields. The cut-set upper bound to the capacity region is derived and is shown to be achievable by our proposed functional-decode-forward coding strategy. More specifically, for the general case where the users can transmit at possibly different rates, functional-decode-forward, combined with rate splitting and joint source-channel decoding, is proved to achieve the capacity region; while for the case where all users transmit at a common rate, rate splitting and joint source-channel decoding are not required to achieve the capacity. That the capacity-achieving coding strategies do not utilize the users' received signals in the users' encoding functions implies that feedback does not increase the capacity region of this class of multiway relay channels.

In November 2010, the Earth Observing One (EO-1) Satellite Mission will successfully complete a decade of Earth imaging by its two unique instruments, the Hyperion and the Advanced Land Imager (ALI). Both instruments are serving as prototypes for new orbital sensors, and the EO-1 is a heritage platform for the upcoming German mission, EnMAP. We provide an overview of the mission's lifetime. We briefly describe calibration & validation activities and overview the technical and scientific accomplishments of this mission. Some examples of the Mission Science Office (MSO) products are provided, as is an example of a image collected for disaster monitoring.

We present capacity results of the binary-symmetric parallel-relay network, where there is one source, one destination, and K relays in parallel. We show that forwarding relays, where the relays merely transmit their received signals, achieve the capacity in two ways: with coded transmission at the source and a finite number of relays, or uncoded transmission at the source and a sufficiently large number of relays. On the other hand, decoding relays, where the relays decode the source message, re-encode, and forward it to the destination, achieve the capacity when the number of relays is small.

The L-user additive white Gaussian noise multi-way relay channel is considered, where multiple users exchange information through a single relay at a common rate. Existing coding strategies, i.e., complete-decode-forward and compress-forward are shown to be bounded away from the cut-set upper bound at high signal-to-noise ratios (SNR). It is known that the gap between the compress-forward rate and the capacity upper bound is a constant at high SNR, and that between the complete-decode-forward rate and the upper bound increases with SNR at high SNR. In this paper, a functional-decode-forward coding strategy is proposed. It is shown that for L ≥ 3, complete-decode-forward achieves the capacity when SNR ≤ 0 dB, and functional-decode-forward achieves the capacity when SNR ≥ 0 dB. For L = 2, functional-decode-forward achieves the capacity asymptotically as SNR increases.

We investigate routing in cooperative multiple-terminal wireless networks in which the nodes can collaborate with each other in data transmission. First, we motivate cooperation by showing that decode-forward, an information-theoretic cooperative coding strategy, achieves rates significantly higher than those achievable by the conventional multi-hop routing, a point-to-point non-cooperative coding strategy. We then construct an algorithm to find optimal (rate-maximizing) routes for decode-forward. We show that the algorithm is able to find shortest optimal routes and is optimal in fading channels. However, the algorithm runs in factorial time in the worst case. So, we propose a near-optimal heuristic algorithm that runs in polynomial time. The heuristic algorithm always outputs optimal routes when the nodes transmit independent codewords, and outputs optimal routes with high probability when the nodes transmit arbitrarily correlated codewords. Lastly, we implement decode-forward using low-density parity-check codes to compare the bit error rate performance of different routes.

In this paper, we extend our previous work on the half duplex multiple-relay channel (MRC). A capacity upper bound based on the max-flow min-cut argument and achievable transmission rates based on the decode-forward coding strategy (DF) for the half duplex MRC have been shown to be functions of the schedule of the network, which is defined as the probability mass function of the transmit state vector (a description of which nodes transmit and which receive). Finding the optimal (rate-maximizing) schedule for DF can be formulated as a maximin optimization problem which is not easily solved in general. In our recent paper, we presented a technique to find optimal schedules for the 4-node MRC based on minimax hypothesis testing. Closed-form solutions were obtained for certain channel topologies. In this paper, we extend the technique to solve for optimal schedules for the general D-node half duplex MRC, where D ges 3.

Half duplex devices are widely used in today's wireless networks. These devices can only send or receive, but not do both at the same time. In this paper, we use cooperative decode-forward relay strategies to increase the throughput of half-duplex wireless networks. Due to the half duplex constraint, relays need to carefully choose their transmission states in order to maximize the throughput. We show that the transmission schedule optimization can be formulated as a linear programming problem. Although the number of possible states grows exponentially as the number of relays increases, only a small subset of these states needs to be used in the optimal transmission schedule. This observation allows us to use heuristic algorithms to solve for near-optimal schedule in large networks. Our numerical results show that the decode-forward strategy can provide nearly 3 times more throughput than the traditional multi-hop relaying strategy in half duplex wireless networks.

This correspondence investigates the interplay between cooperation and achievable rates in multiterminal networks. Cooperation refers to the process of nodes working together to relay data toward the destination. There is an inherent tradeoff between achievable information transmission rates and the level of cooperation, which is determined by how many nodes are involved and how the nodes encode/decode the data. We illustrate this tradeoff by studying information-theoretic decode–forward-based coding strategies for data transmission in multiterminal networks. Decode-forward strategies are usually discussed in the context ofomniscient coding, in which all nodes in the network fully cooperate with each other, both in encoding and decoding. In this correspondence, we investigatemyopic coding, in which each node cooperates with only a few neighboring nodes. We show that achievable rates of myopic decode–forward can be as large as that of omniscient decode–forward in the low signal-to-noise ratio (SNR) regime. We also show that when each node has only a few cooperating neighbors, adding one node into the cooperation increases the transmission rate significantly. Furthermore, we show that myopic decode–forward can achieve nonzero rates as the network size grows without bound.

The National Aeronautics And Space Administration (NASA), in coordination with the United States Geological Survey (USGS), has extended the highly successful Earth Observing 1 (EO-1) mission through fiscal year 2007 (September 30, 2007). This decision is based in part on lower cost, highly autonomous satellite operations developed by NASA, continued scientific interest in EO-1 image data, and the need for back-up imaging capability in the event Landsat 5 or Landsat 7 fails before the launch of the Landsat Data Continuity Mission (LDCM), currently anticipated for 2012. At present, there are no known obstacles to EO-1 supplying useful data through that time frame. Funding for an additional two years of operation of E-1 is being considered being under NASA's senior review process.

In this paper, we study a routing problem on the Gaussian multiple relay channel, in which nodes employ a decode-and-forward coding strategy. We are interested in routes for the information flow through the relays that achieve the highest DF rate. We first construct an algorithm that provably finds optimal DF routes. As the algorithm runs in factorial time in the worst case, we propose a polynomial time heuristic algorithm that finds an optimal route with high probability. We demonstrate that that the optimal (and near optimal) DF routes are good in practice by simulating a distributed DF coding scheme using low density parity check codes with puncturing and incremental redundancy.

The multiple-access channel with feedback and correlated sources (MACFCS) models a sensor network in which sensors collect and transmit correlated data to a common sink. We present four achievable rate regions and a capacity outer bound for the MACFCS. For the first achievable region, we construct a decode-forward based coding strategy. The sources first exchange their data, and then cooperate to send full information to the destination. We term this strategy full decoding at sources with decode-forward (FDS-DF). For two of the other achievable regions, we first perform Slepian–Wolf coding to remove the correlation among the source data. This is followed by either (i) a compress-forward based coding strategy for the multiple-access channel with feedback, or (ii) an existing coding strategy for the multiple-access channel. We also find another achievable region using a multihop coding strategy, which only uses point-to-point coding (no cooperation). From numerical computations, we see that different strategies perform better under certain source correlation structures and network topologies. More specifically, FDS-DF approaches the capacity when (i) the inter-source distance decreases, or (ii) the correlation among the sources gets higher. Furthermore, the cooperative coding strategies considered support larger achievable rate regions than the noncooperative multihop strategy.

In this paper, we derive the capacity of a special class of mesh networks. A mesh network is defined as a heterogeneous wireless network in which the transmission among power limited nodes is assisted by powerful relays, which use the same wireless medium. We find the capacity of the mesh network when there is one source, one destination, and multiple relays. We call this channel the single source multiple relay single destination (SSMRSD) mesh network. Our approach is as follows. We first look at an upper bound on the information theoretic capacity of these networks in the Gaussian setting. We then show that the bound is achievable asymptotically using the compress-forward strategy for the multiple relay channel. Theoretically, the results indicate the value of cooperation and the utility of carefully deployed relays in wireless ad-hoc and sensor networks. The capacity characterization quantifies how the relays can be used to either conserve node energy or to increase transmission rate

In this paper, we investigate communication strategies for the multiple access channel with feedback and correlated sources (MACFCS). The MACFCS models a wireless sensor network scenario in which sensors distributed throughout an arbitrary random field collect correlated measurements and transmit them to a common sink. We derive achievable rate regions for the three-node MACFCS. First, we study the strategy when source coding and channel coding are combined, which we term full decoding at sources. Second, we look at several strategies when source coding and channel coding are separated, which we term full decoding at destination. From numerical computations on Gaussian channels, we see that different strategies perform better under certain source correlations and channel setups

In this paper, we investigate achievable rates for data transmission from sources to sinks through multiple relay networks. We consider myopic coding, a constrained communication strategy in which each node has only a local view of the network, meaning that nodes can only transmit to and decode from neighboring nodes. We compare this with omniscient coding, in which every node has a global view of the network and all nodes can cooperate. Using Gaussian channels as examples, we find that when the nodes transmit at low power, the rates achievable with two-hop myopic coding are as large as that under omniscient coding in a five-node multiple relay channel and close to that under omniscient coding in a six-node multiple relay channel. These results suggest that we may do local coding and cooperation without compromising much on the transmission rate. Practically, myopic coding schemes are more robust to topology changes because encoding and decoding at a node are not affected when there are changes at remote nodes. Furthermore, myopic coding mitigates the high computational complexity and large buffer/memory requirements of omniscient coding