The deployment of millimeter-wave (mmWave) wireless communication systems in urban macrocell scenarios with large coverage and cost-efficient transmission schemes is investigated with a focus on space-time propagation characteristics. First, channel measurement campaigns are conducted at two 5G main candidate frequency bands of 28 GHz and 39 GHz in a central business district and a residential area, based on our new-designed time domain channel sounder, which can support directional scanning sounding with less time consumption. Next, using a reasonable data preprocessing method, small-scale channel characteristics across line-of-sight (LoS) and non-LoS (NLoS) links are analyzed via power delay angular profiles, root mean square delay spread, and azimuth and elevation angular spread. Measurement and analysis results show that space-time propagation parameters are layout-related and have further implications on the mmWave system design.

A typical wireless sensor network (WSN) has multiple sensors, each of which obtains noisy observation of one common physical event and sends the observation to a fusion center (FC) for post-processing. This paper provides a comprehensive research on robust linear transceiver design in the presence of imperfect channel-state information (CSI) in multi-input multi-output (MIMO) WSNs. For the CSI uncertainty, the classical ellipsoid model is assumed, where channel estimation errors live in an ellipsoid. Two robust beamforming problems are considered: 1) the worst-case mean-square-error (MSE) minimization with limited power and 2) sum-power minimization with guaranteed worst-case MSE. For both problems, centralized and decentralized solutions are developed. Moreover, an interesting fundamental relation between the metrics of signal-to-noise ratio (SNR) and MSE has been presented, which extends our proposed solutions to handle the transceiver design when SNR is considered. Extensive numerical results are presented to confirm our findings.

This paper considers joint precoder design to optimize throughput, power consumption and energy efficiency (EE) in the context of multi-antenna wireless sensor networks with coherent multiple access channels. To maximize throughput, both centralized and decentralized algorithms are developed. Our centralized algorithm obtains a new second order cone programming formulation of the problem, which is different from related works and can apply to more generic system setup compared to existing literature. In addition, noting the fact that all existing solutions in literature are centralized based, we propose a novel decentralized solution and analyses its convergence. Besides the throughput maximization, the power consumption and EE problems are also attacked. To optimize these two metrics, a decentralized algorithm based on dual-decomposition and block successive upper-bound method has been developed, which runs in parallel with semi-analytical solutions and has provable strong convergence. A sufficient condition for the validity of the decentralized method is obtained. Extensive numerical results are presented to consolidate our findings.

Designing run-length limited codes for visible light communication systems must account for multiple performance factors, including spectral efficiency, power efficiency, dc balance, and flicker avoidance. This paper reports a new class of enhanced Miller codes, termed eMiller codes, which are capable of achieving highly desirable performances in all of these accounts. An improved Viterbi algorithm (VA), termed $mn$ VA, is developed to help further enhance the performance of eMiller codes by preserving multiple candidate sequences at each decoding stage. This performance-enhancing algorithm introduces little complexity increase compared with the original VA. Analysis on flicker control, power spectral density, and minimum Hamming distance demonstrates the all-around wellness of these new codes. Extensive simulations are carried out to evaluate eMiller codes by themselves and in practical visible light communication (VLC) systems. It is shown that the original VA already allows eMiller codes to deliver a performance noticeably better than conventional Miller and FM0/FM1 codes (and on par with Manchester codes). This result is particularly exciting, as eMiller codes are also more spectrally efficient than Manchester codes. The $mn$ VA further allows eMiller codes to surpass Manchester codes and 4B6B codes in practical RS-coded VLC systems. Simulation results confirm the superb performance of the RS-eMiller schemes.

A two-hop parallel-relay network is considered in this paper. Conventional schemes investigating the coding strategies at the relay(s) have largely focused on hard encoding, with the exception of one pioneering strategy that proposed a soft distributed encoding using the soft estimate (tanh-based) information. To fully harness the gain promised by soft encoding, this paper focuses on distributed soft encoding strategies at the relays. Unlike the previous work that favors the tanh form for soft encoding, we advocate the range-limited log-likelihood ratios (rLLR) as a better way for the relays to capture the reliability of the messages sent by the sender, and especially to further soft-encode these messages. Based on this, we develop a simple but effective soft-encoding relay (SoER) strategy that exploits the useful features of rLLR. Specifically, the close resemblance of rLLR to the tanh form allows us to derive a very simple convolutional encoding, and the piece-wise linearity of rLLR allows us to evaluate the codeword probability density function (PDF) analytically, which further allows us to derive a Viterbi decoding algorithm using a more precise PDF (in addition to the Gaussian-approximated Viterbi algorithm). We finally extend the nonrecursive convolutional SoER strategy to the turbo SoER strategy. Simulation results confirm the efficiency of the new proposed SoER schemes.

This paper investigates a cooperative transmission scheme for a multi-source single-destination system through signal-superposition-based braid coding. The source nodes take turns to transmit, and each time, a source “overlays” its new data together with (some or all of) what it overhears from its partner(s) using signal superposition, in a way similar to French-braiding the hair. We demonstrate how the resultant braid coding can be effectively employed in M-to-1 data collection networks to achieve progressive cooperation. We analyze two subclasses of braid coding, the nonregenerative and the regenerative cases, and, using the pairwise error probability (PEP) as a figure of merit, derive the optimal weight parameters theoretically for each class. For the regenerative case, a modified Viterbi maximum-likelihood (ML) estimator is proposed, whose complexity is linear to the message length. We compute the (Euclidean) free distance, and identify the memory size that strikes the best balance between performance and complexity. The proposed cooperative framework based on braid coding is general and subsumes several previous superposition modulation-based cooperative schemes as its special case. Simulations confirm the efficiency of the proposed schemes.

In wireless networks, getting the global knowledge of channel state information (CSI, e.g., channel gain or link loss probability) is always beneficial for the nodes to optimize the network design. However, the node usually only has the local CSI between itself and other nodes, and lacks the CSI between any pair of other nodes. To enable all the nodes to get the global CSI, in this paper, we propose a network-coded third-party information exchange scheme, with an emphasis on minimizing the total transmission cost for ( ) exchanging the CSI among the nodes. We show that for a network of N nodes, if and only if any k nodes (1 ≤ k <; N) send at least (2 : k) packets, a feasible solution exists for third-party information exchange. Formulating the problem of feasible and optimal solutions as an integer linear programming (ILP) problem, we compute the optimal number of packets that must be transmitted by every node. Guided by the necessary and sufficient condition, we construct two practical transmission schemes: fair load (FL) scheme and proportional load (PL) scheme. A deterministic encoding strategy based on XORs coding over GF(2) is further designed to guarantee that with FL or PL scheme, each node finally can decode the complete packets. It is shown that in two specific networks, these two schemes are optimal, achieving the minimum transmission cost. In more general networks, simulation results show that PL is still close to optimal with a high probability. Finally, a distributed transmission protocol is developed, which allows FL and PL schemes to be operated in a distributed and hence scalable manner.

This paper considers soft-message forwarding in a 2-hop wireless network. Previous methods have only considered the source-relay channel quality but ignored the relay-destination channel quality, causing potential sub-optimality especially in a parallel-relay setting. This paper takes a centralized approach by accounting for all the individual channel-segments, and proposes a “Z-forward” strategy, in which the i-th relay represents the forward messages in a parameterized piece-wise linear form: θi-truncated log-likelihood ratio (LLR) of its reception. This message representation not only is numerically stable, and soft-information-preserving, but also allows us to analytically derive the end-to-end bit error rate (with maximal ratio combining (MRC)), and to compute the optimal values of θinumerically. The results confirm that previous message-forward proposals, however a good performance in a single-relay setting, will considerably degrade as the the number of relays increases. Next, to further simplify the design, we propose a single threshold θ for all the relays, in lieu of one for each, and show that it strikes a balance between performance and computation. Additionally, with Z-forward, we are able to derive the exact probability density function (pdf) of the final reception at the destination, and subsequently to develop the maximum likelihood (ML) estimator. Extensive simulations are presented to verify the efficiency of the new schemes.

This paper proposes an optimal estimate-forward strategy, termed Z-forwarding, for 2-hop parallel-relay systems. The previous tanh-forwarding strategy, which is optimized for the single-relay system is shown to be no longer optimal for a parallel-relay system. Instead, a new, parametrically-optimized Z-forwarding strategy is proposed, where the relay re-transmits a nonlinear but piece-wise linear function of the log-likelihood ratio (LLR) of the source signal. By analytically formulating the end-to-end bit error rate (BER), optimal thresholds that minimize the BER are computed as a function of all the source-relay and relay-destination channels. Maximum likelihood (ML) detector is also developed for the destination to recoup all the diversity gains from the multiple relays. It is shown that Z-forwarding strategy delivers a performance comparable to tanh-forwarding in a single relay system, but considerably better than tanh-forwarding (as well as amplify-forward and decode-forward) in a parallel-relay system.

This paper proposes an efficient method for optimal joint precoding-postcoding design in a multi-input multi-output (MIMO) multi-sensor noisy observation context - a problem that is of great interest to the multi-relay MIMO transmission system. A set of wireless sensors, each provisioned with a different number of antennas and a different power constraint, precode and send their noisy observations of the same data to a common fusion center, which postcodes the data to make a best estimate of the original data. Taking the mean square error as a performance metric, we show that the optimal joint precoding-postcoding design problem is non-convex. Leveraging the alternative minimization framework, we are able to decompose it to two convex subproblems, one of which promises closed-form solutions. However, unlike previous studies that assume a total power constraint, the condition of individual power constraint and individual noise uncertainty at each sensor has tremendously complicated the second convex subproblem. Rather than numerically solve it via conventional convex optimization tools, we attack it analytically by transforming, approximating, and decomposing it to a set of new problems. We show that the new problems can be efficiently tackled via the Karush-Kuhn-Tucker conditions in an iterative manner. Simultions show that it leads to a convergence much faster and more robust than the conventional convex optimization tools.

This paper considers a multi-input multi-output (MIMO) wireless relay network, where the source S communicates wirelessly to the destination D via the help of a nonregenerative relay R. Previous studies are almost exclusively based on the assumptions of equal time-duration phases for S-R and R-D transmission and equal number of multiple antennas for every node, and the results concerning arbitrary time scheduling and arbitrary antenna arrays are not yet known. To achieve the best end-to-end data rate, this paper considers joint source and relay beamforming design combined with time scheduling and subchannel pairing, in accordance to respective link conditions. We first propose a practical beamforming framework that is inspired by the optimal MIMO relay beamforming structure. It is shown that in this framework, after subchannel pairing, the joint beamforming design problem can be transformed to a joint optimal power allocation problem. Since the overall optimization problem is nevertheless very difficult, involving both continuous and integer variables as well as a non-convex objective function make, we solve it by enumerating integer variables, and with each given integer values, performing alternative optimization and decomposing the problem into convex subproblems. Numerical results confirm the effectiveness of our approach, and it is shown that relaxing the equal time-duration constraint and pairing subchannels optimally can significantly improve the end-to-end capacity.

This paper considers optimal transceiver design for a multi-terminal multi-inputmulti-output (MIMO) system, where L sensors wirelessly communicate individually-contaminated observations of the same source to the fusion center. The constraint that each sensor has individual power cap significantly complicates the non-convex optimization problem, and the optimal (linear) precoding and postcoding are not previously known. Using the signal-to-noise-ratio (SNR) as the performance metric, and employing the alternative minimization approach, we decompose the original problem into multiple subproblems that will run iteratively. The key results include the development of a closed-form solution to the optimal postcoder given the precoders, and the development of a closed-form solution for the ε-optimal precoders given the postcoder. The former is achieved via eigenvalue decomposition, and the latter is achieved by bounding the optimal solutions from above and from below, designing a series of fast-converging bisection search, and developing the closed-form analytical solution for each search. The convergence and the complexity of the proposed algorithm is analyzed and simulations are provided to confirm the efficiency of our proposal.

Linear erasure codes with local repairability are desirable for distributed data storage systems. An [n, k, d] linear code having all-symbol (r, δ)-locality, denoted as (r, δ)a, is considered optimal if it has the actual highest minimum distance of any code of the given parameters n, k, r and δ. A minimum distance bound is given in [10]. The existing results on the existence and the construction of optimal (r, δ)alinear codes are limited to only two small regions within this special case, namely, i) m = 0 and ii) m ≥ (v+δ-1) > (δ-1) and δ = 2, where m = n mod (r+δ-1) and v = k mod r. This paper investigates the properties and existence conditions for optimal (r, δ)alinear codes with general r and δ. First, a structure theorem is derived for general optimal (r, δ)acodes which helps illuminate some of their structure properties. Next, the entire problem space with arbitrary n, k, r and δ is divided into eight different cases (regions) with regard to the specific relations of these parameters. For two cases, it is rigorously proved that no (r, δ)alinear code can achieve the minimum distance bound in [10]. For four other cases the optimal (r, δ)acodes are shown to exist over a field of size q ≥ (k-1n), deterministic constructions are proposed. Our new constructive algorithms not only cover more cases, but for the same cases where previous algorithms exist, the new constructions require a smaller field, which translates to potentially lower computational complexity. Our findings substantially enriches the knowledge on optimal (r, δ)alinear codes, leaving only two cases in which the construction of optimal codes are not yet known.

In many practical applications such as direction-of- arrival (DOA) estimation and line spectral estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a continuous domain. To apply the conventional compressed sensing to such applications, the continuous parameter space has to be discretized to a finite set of grid points. Discretization, however, incurs errors and leads to deteriorated recovery performance. To address this issue, we propose an iterative reweighted method which jointly estimates the unknown parameters and the sparse signals. Specifically, the proposed algorithm is developed by iteratively decreasing a surrogate function majorizing a given objective function, which results in a gradual and interweaved iterative process to refine the unknown parameters and the sparse signal. Numerical results show that the algorithm provides superior performance in resolving closely-spaced frequency components.

This paper considers wireless sensor networks with limited bandwidth, where sensors must transmit sensing results that are distorted with noise uncertainty, to the fusion center reliably and efficiently. A class of rate-1 linear complex-field coding (CFC) transmission strategy is developed, to effectively protect the noisy observation of the signals without any bandwidth expansion. Pairwise error probability (PEP) is analyzed, design criterion for optimal complex-field coding strategies is derived, and optimal codes are examined. In addition to maximum likelihood (ML) decoding, a low-complexity decoding scheme termed partially nulling and canceling (PNC) is also proposed. The PNC decoder, representing a midway tradeoff between sphere decoding and (linear) minimum mean square error (MMSE) detection, strikes a good balance between complexity and performance. Extensive simulations show that the proposed CFC is able to reap considerable gains without any bandwith epansion!

We consider the relaying of binary antipodal signals across two hops via soft regeneration and soft error protection. The signals at the input of the regenerator are degraded by additive white Gaussian noise (AWGN). Traditional approaches either directly relay the noisy analog waveform to the destination (thus missing the opportunity for coding gain), or make hard detection and then re-encode the bits using a digital error correction code (which may risk error propagation). This paper proposes a new class of soft-input soft-output (SISO) encoding strategies that are shown to outperform the conventional hard and soft forwarding schemes. Making essential use of the trellis structure, the soft encoder and the maximum likelihood decoder both operate efficiently in linear time, and support variable block sizes and code rates. Practical applications in relay communication are discussed. Simulations confirm the outstanding performance of the proposed scheme.

In the paper, a new distributed coding scheme for parallel relay systems is proposed, in which a sender communicates to a destination that is two hops away via two (or more) parallel relays. The key idea is the exploitation of a (rate-1) soft convolutional encoder at each of the parallel relays, to collaboratively form a simple but powerful distributed analog coding scheme to achieve efficient forwarding of soft reliability messages. We detail the encoding and decoding process of the proposed soft-encoding distributed coding. As the input of the encoder would affect the overall performance, we analyze what form of messages at the relay is most appropriate to be forwarded to the destination. The range-limited log likelihood ratio (range-limited LLR) is chosen as the input. The optimality of the range-limited LLRs as the best form of relaying messages is verified by the simulation results. Our new distributed coding scheme can obviously outperform the existing ones.

This paper investigates the general achievable rate region (ARR) for a wireless multi-way relay (MWR) network with full message exchange and decode-and-forward (DF) cooperative strategy. The MWR network consists of K users, each equipped with M antennas, and a single relay equipped with N antennas; and there is no user-user direct connectivity. Since different schedules would result in different ARRs, the general ARR must subsume all possible schedules; and this result is not previously known. We first present a cut set outer bound, and then derive and analyze the general ARR for DF by considering all meaningful schedules. We show that the general ARR is a convex hull expanded from a set of ARRs corresponding to special schedules. We further show that the ARR of a specific schedule can be expanded by its extreme points via the Krein-Milman theorem. In this, we have shown that the general ARR (of a MWR network using DF) is obtained by identifying and expanding these extreme points. We demonstrate the numerical results via exemplary cases.

In this paper, we consider the problem of minimizing the total transmission cost for exchanging channel state information. We proposed a network coded cooperative data exchange scheme, such that the total transmission cost is minimized while each client can decode all the channel information held by all other clients. In this paper, we first derive a necessary and sufficient condition for a feasible transmission. Based on the derived condition, there exists a feasible code design to guarantee that each client can decode the complete information. We further formulate the problem of minimizing the total transmission cost as an integer linear programming. Finally, we discuss the probability that each client can decode the complete information with distributed random linear network coding.

Analog error correction codes, by relaxing the source space and the codeword space from discrete fields to continuous fields, present a generalization of digital codes. While linear codes are sufficient for digital codes, they are not for analog codes, and hence nonlinear mappings must be employed to fully harness the power of analog codes. This paper demonstrates new ways of building effective (nonlinear) analog codes from a special class of nonlinear, fast-diverging functions known as the chaotic functions. It is shown that the “butterfly effect” of the chaotic functions matches elegantly with the distance expansion condition required for error correction, and that the useful idea in digital turbo codes can be exploited to construct efficient turbolike chaotic analog codes. Simulations show that the new analog codes can perform on par with, or better than, their digital counter-parts when transmitting analog sources.

This paper presents a new paradigm for image transmission through analog error correction codes. Conventional schemes rely on digitizing images through quantization (which inevitably causes significant bandwidth expansion) and transmitting binary bit-streams through digital error correction codes (which do not automatically differentiate the different levels of significance among the bits). To strike a better overall performance in terms of transmission efficiency and quality, we propose to use a single analog error correction code in lieu of digital quantization, digital code and digital modulation. The key is to get analog coding right. We show that this can be achieved by cleverly exploiting an elegant “butterfly” property of chaotic systems. Specifically, we demonstrate a tail-biting triple-branch baker's map code and its maximum-likelihood decoding algorithm. Simulations show that the proposed analog code can actually outperform digital turbo code, one of the best codes known to date! The results and findings discussed in this paper speak volume for the promising potential of analog codes, in spite of their rather short history.

Previous work introduced the idea of matching network codes with network graphs to handle the network dynamics. This paper further integrates channel coding in the adaptive network coding framework through an elegant treatment of circulant shifting. Several code constructions are developed. Theoretical analysis and simulations show that the resultant generalized adaptive network coded cooperation (GANCC) is simple, adaptive, distributive, and capable of remarkable coding gains even with a very small number of cooperating users.

A new class of analog codes based on 2-dimensional discrete-time chaotic systems, the baker's map, are proposed. The fundamental idea is to effectively transform the "sensitivity-to-initial-condition" property of a chaotic system to serve the "distance expansion" condition required by a good error correction code. By cleverly applying the baker's map on the tent map to achieve a higher dimensional nonlinear mapping, and by engineering a simple mirrored replication structure to protect against the weaker dimension, the proposed "mirrored baker's codes" promise considerably better performance than the existing tent map codes. A maximum likelihood detector is derived, simplified and evaluated. Comparison with the present-day digital coding systems, including convolutional codes and turbo codes, reveals a remarkably on-par performance achieved by the proposed new codes.

A new clustering approach, termed Distributed Energy Efficient clustering Protocol (DEEP), is proposed for wireless sensor networks. Using a non-iterative cluster formation operation, the protocol spends an extremely low overhead energy compared to the existing protocols and terminates faster than the energy-expensive iterative processes. The distributed head election algorithm guarantees that the periodically-elected leaders have the highest residual energy among their members in each data reporting cycle, effectively balancing the energy consumption among sensors. The DEEP also accounts for sensor limitations as well as practical concerns such as wireless collision that has not been considered by the existing clustering protocols. It intelligently exploits the overhearing ability of sensors to alleviate the loss of clustering control packets due to collisions. The proposed protocol is very effective in forming well-distributed clusters that ensure the required load balancing, the connectivity of clusters, and the minimum data communication energy during the data collection stage. In addition, the DEEP does not make any advanced assumptions about the required number of clusters, the network density, the energy consumption pattern of sensors or their clock synchronization and capabilities. For a thorough evaluation, we have compared the performance of DEEP with an existing clustering protocol. The simulation results show the effectiveness of DEEP in reducing the energy expenditure besides assuring other desirable features. We have also examined its performance in prolonging the network lifetime in the context of a practical routing protocol.

This paper considers wireless spectrum sensing in harsh environments dominated by shadowing and fading. By modeling the network of the secondary users as Markov random fields and pulling a group of secondary users to cooperate through distributed probabilistic inference, effective sensing and fusion can be achieved. The proposed framework subsumes belief propagation, as well as conventional weighted hard/soft combining (such as maximal ratio combining and equal gain combing). It can also account for the distance-dependent correlation among individual sensing results by setting appropriate compatibility function. Theoretic upper and lower bounds are derived, demonstrating the significant gains made possible by effective cooperation. Extensive simulations confirm the analytical results.

Maximum distance separable (MDS) array codes are XOR-based optimal erasure codes that are particularly suitable for use in disk arrays. This paper develops an innovative method to build MDS array codes from an elegant class of nested graphs, termed complete-graph-of-rings (CGR). We discuss a systematic and concrete way to transfer these graphs to array codes, unveil an interesting relation between the proposed map and the renowned perfect 1-factorization, and show that the proposed CGR codes subsume B-codes as their "contracted" codes. These new codes, termed CGR codes, and their dual codes are simple to describe, and require minimal encoding and decoding complexity.

With the introduction of a dither sequence and scaling factors, dithered Tomlinson-Harashima precoding (THP) has shown to outperform conventional spatial THP in multi-user MIMO systems. Through a careful analysis of dithered THP, this paper exhibits that it is not only possible, but also beneficial, to eliminate the dither sequence and develop efficient scale-only THP, and the key is to resolve the scaling factors. By capturing the dependency of the interference level on the scaling factors through a closed-form piece-wise function, we formulated a new method to optimize the scaling factors. We present that the new scale-only THP is not only simpler, but also performs better. As much as 1 dB of performance gain is observed at the low signal-to-noise region.

This paper proposes a novel construction of analog codes based on nonlinear chaotic dynamical systems in general and the tent map in particular. In light of the importance of the symbolic coding in estimating tent chaos, the proposed construction exploit useful ideas of interleaving and parallel concatenation from classical coding theory to protect symbolic coding and subsequently chaotic states. The resultant new codes, dubbed chaotic analog turbo (CAT) codes, extend Chen-Wornell codes, in a spirit close to that of digital turbo codes extending digital convolutional codes. A new sequence detection method that improves the existing backward detection method is also developed. Through analysis and simulations, it is shown that CAT codes can noticeably outperform the existing analog ones.

A new class of rateless low-density parity-check (LDPC) codes, called the dynamic degree distribution (DDD) codes, are proposed for erasure channels. The parity symbols are generated from a set of degree distributions, one embedded in one another and each optimized for different channel erasure rates. Thus, instead of sticking to a fixed degree distribution for all the channel conditions, the encoder progressively adapts different degree distributions for different channel conditions, such that each distribution is operated in its most-desirable region. Theoretic analysis shows that the new codes perform close to the capacity in a large range of code rates with fairly low complexity. Simulations confirm that these codes can actually outperform the existing raptor rateless codes.

In wireless sensor networks, localization in indoor environment suffers from non-LOS (line-of-sight) and multi-path fading. In this paper, a new localization algorithm is proposed, which makes essential use of distributed space-time-codes (DSTC) to combat fading. By cleverly devising DSTC and employing a novel ML synchronization technique, the proposed algorithm exploits the full benefits of TDOA (time-difference-of-arrival) techniques and provides a greater accuracy than the localization methods reported previously. Simulation results show that the new algorithm performs robustly in a variety of conditions.

We consider practical network coding, a useful generalization of routing, in a dynamic wireless network comprising a set of nodes transmitting data wirelessly to a set of common destinations. It is shown that an arbitrary network can be transformed to a "trellis network", such that routing becomes equivalent to path discovery in the trellis. Exploiting a distributed, real-time matching-code-with-graph technique in each stage of the possibly time-varying trellis, a general network coding framework is developed. Depending on whether or not decoding is performed at the intermediate relays, three practical network coding categories, progress network coding, concatenated network coding and hybrid network coding, are investigated. Analysis shows that the proposed framework can be as dissemination- efficient as those with random codes, but only more practical.

We consider user cooperation in a relay network that comprises a large collection of transmitters sending wireless data to a common receiver. A new framework exploiting the network coding technology, referred to as adaptive network coded cooperation (ANCC), is proposed to combat the lossy nature of wireless links and to adapt to the changing network topology. The central idea is to match network-on-graph, i.e. instantaneous network topologies described in graphs, with the well-known class of code-on-graph, i.e. low-density parity-check (LDPC) codes and LDPC-like codes. Both achievable rates and outage probabilities are evaluated for ANCC, and closed-form expressions are derived for the asymptotic case where the network size increases without bound. Compared with the existing schemes, ANCC significantly outperforms repetition-based schemes and performs on par with space-time coded cooperation (STCC), but obviates the need for stringent inter-user synchronization at the bit/baud level.

Researchers have developed handy tools to evaluate the ensemble-average of distance spectra for turbo codes, but evaluating distance spectra for an individual turbo code with a specific random interleaver remains computationally prohibitive. This paper develops a new way to efficiently tackle this problem. The new method enjoys low complexity, and generates more comprehensive spectra results than existing methods possibly.

Most user cooperation protocols work in a timesharing manner, where each user transmits its own message and relays for the other at different segments of a time slot. We develop a new scheme to send these messages simultaneously using network coding. We show that network coding is more tolerant to poor inter-user channels than time-sharing, and achieves a better overall performance. We generalize the scheme to a multi-user, multi-slot cooperation framework. Under this framework, we show that the network coding scheme reaps a better diversity order and provides a better effective inter-user channel than time-sharing schemes.

Amplify-forward (AF) can be modeled as a way of forwarding soft reliability information extracted directly from the transmit channel, without utilizing the channel code that may well exist in the source-packet.Decode-forwardexploits the channel code but forwards the hard-decisions only. These observations motivate the proposition of an efficient new relaying strategy,decode-amplify-forward(DAF). By having the relay perform soft decoding and forward the reliability information at the output of its decoder instead of that from the raw channel, DAF cleverly combines the merits of both AF and DF: soft information representation in AF and coding gain in DF. A hybrid scheme of DAF andcoded cooperation(DAF-CC), implemented through simple time-sharing, is further discussed. The information-theoretic achievable rates of these schemes are formulated for channels with binary inputs and Gaussian outputs. Both analysis and simulations confirm that the proposed schemes are capable of considerably better performances than the existing schemes.

We investigate good coprime interleavers that perform well in turbo codes and allow for memory-efficient implementation. We first evaluate the spread and the randomness properties of these interleavers using the cycle correlation sum (CCS) and the variance of the second order spread spectrum (VSSS) criteria, respectively. A critical parameter cpoweris introduced for interleavers whose lengths are power of 2. It is found that a large cpowertypically associates with a small CCS value, indicating a good minimum spread of the coprime interleaver, but the degree of randomness decreases with growing cpower- We next develop rules on choosing good parameters that strike a good balance between minimum spread and degree of randomness. Simulation results confirm that the coprime interleavers designed from our rules perform as well as or better than S-random interleavers.

Adaptive-network-coded-cooperation (ANCC) is an efficient user cooperation scheme proposed for large wireless networks comprising a host of terminals communicating with a common destination. By matching code graphs with instantaneous network graphs in a distributed and adaptive manner, the protocol enables network coding to be exploited in networks with unreliable channels and changing topologies. This paper analyzes the outage behavior of ANCC when the number of terminals trends to infinity. A threshold phenomenon is revealed which demonstrates that an arbitrarily small outage can be achieved with a sufficiently large network as long as the channel conditions are above a certain threshold. Comparison with the existing cooperation schemes shows that ANCC achieves a substantial gain of 30 dB over repetition, and falls only 1 dB short of space-time-coded-cooperation (but obviating the need for stringent inter-user synchronization).

We propose a combined channel-network coding solution for efficient user cooperation in wireless ad-hoc networks that comprise a host of terminals communicating to a common destination. The proposed framework, termed generalized adaptive network coded cooperation or GANCC, addresses the challenge of inter-user outage, which widely persists in practical cooperation scenarios, by adaptively matching code graphs to instantaneous network graphs (topologies). Additionally, GANCC treats channel codes as an integral part of the network code, and in doing so not only extracts the most benefit from these codes but also provides a live example supporting the notion that network codes are generalization of channel codes (as well as source codes)

We conduct an information theoretic analysis for the adaptive-network-coded-cooperation (ANCC) protocol over large wireless relay networks. The ANCC protocol adaptively encodes the data from different terminals using a single network code by matching the instantaneous network topology with the code graph of a low-density-parity-check (LDPC) code. The ergodic capacity and the outage probability of this protocol are analyzed for both finite and infinite network sizes, and closed-form expressions are derived for the infinite case. Comparison with the existing protocols including repetition and space-time-coded-cooperation confirm that ANCC is both low-complexity and high-performance

Iterative analysis for low-density parity-check (LDPC) codes uses the prevailing assumption that messages exchanged between the variable nodes and the check nodes follow a Gaussian distribution. However, the justification is largely pragmatic rather than being based on any rigorous theory. This paper provides a theoretic support by investigating when and how well the Gaussian distribution approximates the real message density and the far subtler why. The analytical results are verified by extensive simulations.

We consider multi-hop transmission from the source to the destination in ad-hoc wireless networks. Cooperative forwarding approaches in the framework of progressive network coding are proposed which generalize coded cooperation in a multi-hop context. In this framework, the source node and each succeeding relay node progressively decode what they receive from the previous stages and re-encode the messages to different parts of the parity bits from a single (network) codeword hop by hop. The maximal achievable rates for the multi-hop wireless networks using traditional repetition-forward and progressive network coding are analyzed with respect to different transmit power constraint and packet length allocation. The optimal number of relays are derived in each scheme. It is found that progressive network coding with adaptive packet length significantly increases the system throughput and improves the energy efficiency

Error control coding can be used over free-space optical (FSO) links to mitigate turbulence-induced fading. In this paper, we derive error performance bounds for coded FSO communication systems operating over atmospheric turbulence channels, considering the recently introduced gamma-gamma turbulence model. We derive a pairwise error probability (PEP) expression and then apply the transfer function technique in conjunction with the derived PEP to obtain upper bounds on the bit error rate. Simulation results are further demonstrated to confirm the analytical results.

Provides a listing of current committee members and society officers.

A novel scheme exploiting rate-compatible punctured convolutional (RCPC) codes is proposed for distributed and adaptive source coding. The proposed scheme is based on the SF-ISF approach in (Z. Tu et al., Eurasip J. Appl. SigPro. - Special Iss. on Turbo Proc.). It is simple, general, flexible, and provenly optimal. For the class of RCPC codes that are obtained from puncturing the parity bits of a recursive systematic convolutional (RSC) mother code, it is shown that an "optimal" codec exists where a single source encoder and a single source decoder can accomodate a set of different compression rates efficiently.

The paper evaluates the relative merits of amplify-and-forward (AF) and decode-and-forward (DF), the two basic modes of cooperative communications, in practical systems. Specifically, the paper considers the case where the two user channels are slow fading with similar channel qualities and the inter-user channel is some 10 dB better. Through evaluation of the excess information rate, analysis of the worst-case error rate, and simulations using practical turbo codes, it is consistently shown that the two modes are practically on a par with each other. Furthermore, the study points to inter-user outage as the detrimental factor, and location, rather than the specific cooperative strategy, as the key element in cooperative communications.

Message ferrying (MF) (Wenrui Zhao and Ammar, M.H., Proc. IEEE Workshop on Future Trends in Distrib. Computing Syst., 2003), a viable solution for routing in highly partitioned ad-hoc networks, exploits message ferries to transfer packets between disconnected nodes. The paper studies the delivery quality of service (QoS) for certain urgent messages in the constrained and the relaxed constrained MF systems. Efficient algorithms to compute near-optimal ferry routes are proposed, delay analysis is conducted and the results are compared to the non-constrained scenario.

The recently proposed turbo-binning scheme is shown to be both efficient and optimal for uniform source Slepian-Wolf coding problem (Z. Tu et al., 2003). This paper studies the case when sources are i.i.d. but nonuniformly distributed. It is firstly shown that any algebraic binning scheme based on linear codes is optimal for nonuniform sources only asymptotically. Next two modifications are proposed to improve the performance of the turbo-binning scheme for nonuniform sources. The first is to carefully design the constituent encoder structures to maximally match the turbo code to the nonuniform source distribution, and the second is to use variable-length syndrome sequences to index the bins. Simulations show that the combination of both strategies can lead to an improvement of as much as 0.22 bit/symbol in overall compression rate for highly nonuniform sources.

This work presents an efficient structured binning scheme for solving the noiseless distributed source coding problem with parallel concatenated convolutional codes, or turbo codes. The novelty in the proposed scheme is the introduction of a syndrome former and an inverse syndrome former to efficiently and optimally exploit an existing turbo code without the need to redesign or modify the code structure and/or decoding algorithms. Extension of the proposed approach to serially concatenated codes is also briefed and examples including conventional turbo codes and asymmetric turbo codes are given to show the efficiency and the general applicability of the approach. Simulation results reveal good performance which is close to the theoretic limit.

We present an efficient algorithm to compute the distance spectrum of a general finite intersymbol interference (ISI) channel, whose complexity is lower than those of existing methods. Closed-form expressions are derived for both input-output Euclidean distance enumerators and asymptotic distance spectrum shapes for 2-tap and 3-tap ISI channels. Coded and/or precoded ISI channels are also discussed.

We propose a novel class of provably good codes which are a serial concatenation of a single-parity-check (SPC)-based product code, an interleaver, and a rate-1 recursive convolutional code. The proposed codes, termed product accumulate (PA) codes, are linear time encodable and linear time decodable. We show that the product code by itself does not have a positive threshold, but a PA code can provide arbitrarily low bit-error rate (BER) under both maximum-likelihood (ML) decoding and iterative decoding. Two message-passing decoding algorithms are proposed and it is shown that a particular update schedule for these message-passing algorithms is equivalent to conventional turbo decoding of the serial concatenated code, but with significantly lower complexity. Tight upper bounds on the ML performance using Divsalar's (1999) simple bound and thresholds under density evolution (DE) show that these codes are capable of performance within a few tenths of a decibel away from the Shannon limit. Simulation results confirm these claims and show that these codes provide performance similar to turbo codes but with significantly less decoding complexity and with a lower error floor. Hence, we propose PA codes as a class of prospective codes with good performance, low decoding complexity, regular structure, and flexible rate adaptivity for all rates above 1/2.

This work investigates the achievable information rate with the state-of-the-art turbo coding and intensity modulation/direct detection for outdoor long-distance free-space optic (FSO) communications. The channel under weak atmospheric turbulence is modeled as a log-normal intensity fading channel where on-off keying makes it look asymmetric. While no effort is made to spectrally match the code to the asymmetry of the channel, the decoding strategy is optimally adjusted to match to the channel response. In addition to fixed rate turbo coding, a family of variable rate turbo codes are constructed and discussed. Shannon capacity is also briefly visited to denote the theoretic limit. It is shown that under low turbulence a single long turbo code is sufficient to get within 1 dB from the capacity, but when the turbulence gets strong, adaptive coding is necessary to close the gap. We expect these results to be useful for current and immediate future systems.

This paper evaluates two-dimensional turbo product codes based on single-parity check codes (TPC/SPC) and low-density parity check (LDPC) codes for use in digital magnetic recording systems. It is first shown that the combination of a TPC/SPC code and a precoded partial response (PR) channel results in a good distance spectrum due to the interleaving gain. Then, density evolution is used to compute the thresholds for TPC/SPC codes and LDPC codes over PR channels. Analysis shows that TPC/SPC codes have a performance close to that of LDPC codes for large codeword lengths. Simulation results for practical block lengths show that TPC/SPC codes perform as well as LDPC codes in terms of bit error rate, but possess better burst error statistics which is important in the presence of an outer Reed-Solomon code. Further, the encoding complexity of TPC/SPC codes is only linear in the codeword length and the generator matrix does not have to be stored explicitly. Based on. the results in the paper and these advantages, TPC/SPC codes seem like a viable alternative to LDPC codes.

Following the trend of turbo codes and low density parity check (LDPC) codes, single-parity turbo product codes (TPC/SPC) are being seriously considered for application in future high-density recording systems. Recent work on TPC/SPC codes has focused on ideal partial response channels with additive white Gaussian noise. This work extends the investigation to a more realistic equalized Lorentzian channel model where imperfect channel shaping, colored noise and recording density effect are taken into consideration. The effect of precoding is discussed and the interleaving gain is quantified. Simulation results of the turbo decoding system with both channel models are presented. A comprehensive evaluation is conducted, including BER performance, code rate selection, equalization targets and error statistics, which demonstrate TPC/SPC codes to be a promising candidate for future high-density recording systems.

Product accumulate (PA) codes were proposed and shown by Li, Narayanan and Georghiades (see Proc. Intl.. Symp. Inform. Theory, Washington DC, p.122-22, June 2001, and IEEE Tran. Info. Theory) to be a class of simple and provably good codes for rate R/spl ges/1/2. This work investigates the generalized product accumulate (GPA) codes which have rates over the entire range and which are also "good" both in the maximum likelihood (ML) sense and under the iterative approach. Analysis concentrates on the weight distribution over the code ensemble, the ML bounds, and the existence and computation of threshold phenomenon in the iterative decoding. A tight upper bound due to Divsalar (see Proc. 1998 Allerton Conf. Commun. and Control, Sept. 1998, p.201-10) and the thresholds computed using density evolution are examined. Simulations are presented and evaluated, especially for rate R/spl les/1/2.

This paper proposes an efficient graph-based sum-product algorithm for decoding 1/(1+D/sup n/) code, whose Tanner (1981) graph is cycle-free. A rigorous proof is given which shows the proposed algorithm is equivalent to the MAP decoding implementing the BCJR algorithm, but with a lower complexity magnitude. The paper presents an explicit example which confirms the claim that the sum-product algorithm is optimal on cycle-free graphs. A parallel realization is then discussed and shown to resemble low density parity check (LDPC) decoding. The paper further proposes a min-sum algorithm which is equivalent to the max-log-MAP algorithm. Prospective applications which can take advantage of the proposed decoding algorithms are discussed and simulations are provided.

We investigate the performance of low density parity check (LDPC) codes, single-parity turbo product codes (TPC/SPC) and multi-parity turbo product codes (TPC/MPC) over various partial response channels (PR) encountered in magnetic and magneto-optical (MO) recording systems, like PR4/EPR4 and PR1/PR2 channels. The codes have similarity in structures and can be decoded using simple message-passing algorithms. We show that the combination of a TPC/SPC code and a precoded PR channel results in good distance spectrum due to interleaving gain. Density evolution is then used to compute the thresholds for TPC/SPC and LDPC codes over PR channels. Through analysis and through simulations, we show the three types of codes yield comparable bit error rate performance with similar complexity, but they exhibit quite different error statistics, which in turn may result in sharp differences in block failure rate after the Reed-Solomon error correction code (RS-ECC).

A new class of codes, named product accumulate codes, which are the concatenation of an outer product code and an inner rate-1 differential encoder (or accumulator) is proposed. We show that these codes can perform within a few tenths of a dB from the Shannon limit for rates/spl ges/1/2. For practical block lengths, these codes provide similar performance to turbo codes but with significantly lower decoding complexity.