This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We formulate online pseudo-labeling as an optimization problem to find pseudo-labels that maximize the mutual information between the label and data while being close to a given model probability. We derive a fixed-point iteration method and prove its convergence to the optimal solution. In contrast to baselines, MIRA combined with pseudo-label prediction enables a simple yet effective clustering-based representation learning without incorporating extra training techniques or artificial constraints such as sampling strategy, equipartition constraints, etc. With relatively small training epochs, representation learned by MIRA achieves state-of-the-art performance on various downstream tasks, including the linear/${\it k}$-NN evaluation and transfer learning. Especially, with only 400 epochs, our method applied to ImageNet dataset with ResNet-50 architecture achieves 75.6% linear evaluation accuracy.

We propose unsupervised embedding adaptation for the downstream few-shot classification task. Based on findings that deep neural networks learn to generalize before memorizing, we develop Early-Stage Feature Reconstruction (ESFR) — a novel adaptation scheme with feature reconstruction and dimensionality-driven early stopping that finds generalizable features. Incorporating ESFR consistently improves the performance of baseline methods on all standard settings, including the recently proposed transductive method. ESFR used in conjunction with the transductive method further achieves state-of-the-art performance on mini-ImageNet, tiered-ImageNet, and CUB; especially with 1.2% 2.0% improvements in accuracy over the previous best performing method on 1-shot setting.

The generalization ability of most meta-reinforcement learning (meta-RL) methods is largely limited to test tasks that are sampled from the same distribution used to sample training tasks. To overcome the limitation, we propose Latent Dynamics Mixture (LDM) that trains a reinforcement learning agent with imaginary tasks generated from mixtures of learned latent dynamics. By training a policy on mixture tasks along with original training tasks, LDM allows the agent to prepare for unseen test tasks during training and prevents the agent from overfitting the training tasks. LDM significantly outperforms standard meta-RL methods in test returns on the gridworld navigation and MuJoCo tasks where we strictly separate the training task distribution and the test task distribution.

We propose Episodic Backward Update (EBU) – a novel deep reinforcement learning algorithm with a direct value propagation. In contrast to the conventional use of the experience replay with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state to its previous states. Our computationally efficient recursive algorithm allows sparse and delayed rewards to propagate directly through all transitions of the sampled episode. We theoretically prove the convergence of the EBU method and experimentally demonstrate its performance in both deterministic and stochastic environments. Especially in 49 games of Atari 2600 domain, EBU achieves the same mean and median human normalized performance of DQN by using only 5% and 10% of samples, respectively.

We consider the problem of sparse phase retrieval from Fourier transform magnitudes to recover the k-sparse signal vector and its support T. We exploit extended support estimate 8 with size larger than k satisfying 8 ) T and obtained by a trained deep neural network (DNN). To make the DNN learnable, it provides 8 as the union of equivalent solutions of T by utilizing modulo Fourier invariances. Set 8 can be estimated with short running time via the DNN, and support T can be determined from the DNN output rather than from the full index set by applying hard thresholding to 8. Thus, the DNN-based extended support estimation improves the reconstruction performance of the signal with a low complexity burden dependent on k. Numerical results verify that the proposed scheme has a superior performance with lower complexity compared to local search-based greedy sparse phase retrieval and a state-of-the-art variant of the Fienup method.

In this paper, the capacity of continuous-space electromagnetic channels is analyzed. We assume the regions confining the transceivers are filled with dielectric that is either lossy or lossless and show how the capacity is affected by the physical loss of the source region. Our analysis is based on calculation of exact power consumption in the presence of mutual coupling and accurate modeling of noise based on fluctuation-dissipation theorem. We also analytically derive the Q factor and show how the capacity is affected by the bandwidth constraint. Finally, we compare our work with the existing literature and analyze the maximum gain of spherical dielectric antennas in practical applications.

In this paper, we prove a unified achievability bound that generalizes and improves random coding bounds for any combination of source coding, channel coding, joint source-channel coding, and coding for computing problems assuming blockwise node operation. As a general network setup, we consider an acyclic discrete memoryless network, where the network demands and constraints are specified by a joint-typicality constraint on the whole channel input and output sequences. For achievability, a basic building block for node operation consists of simultaneous nonunique decoding, simultaneous compression, and symbol-by-symbol mapping. Our bound can be useful for deriving random coding bounds without error analysis, especially for large and complex networks. In particular, our bound can be used for unifying and generalizing many known relaying strategies. For example, a generalized decode-compress-amplify-and-forward bound is obtained as a simple corollary of our main theorem, and it is shown to strictly outperform the previously known relaying schemes. Furthermore, by exploiting the symmetry in our bound, we formally define and characterize three types of network duality based on channel input-output reversal and network flow reversal combined with packing-covering duality.

This paper investigates spectral shaping of multi-carrier-modulation waveforms based on combination of Nyquist windowing and subband filtering. The combined windowing/filtering allows simultaneous control on both subcarrier and subband spectra. When compared with the existing Nyquist windowing or subband filtering techniques under a fixed excess frame length constraint, the proposed scheme offers reduced sensitivity to carrier frequency and symbol timing offsets. Establishing an analytical tool based on the error spectrum stack consisting of the error signals evaluated at different signal delay positions, we explore the window-filter trade-off and provide the minimum interference power solution for given ranges of carrier frequency and symbol timing offsets. Our design targets low-latency applications having no provisions for high-precision synchronization and having potential need for spectrum aggregation.

This paper studies inner and outer bounds on the degrees of freedom (DoF) region of the multi-antenna two-user Gaussian interference channel with an instantaneous relay (IR) or relay without delay. It is assumed that the two transmitters and the two receivers have M antennas, while the IR receives through Nr antennas and transmits through Nt antennas. In the proposed achievable scheme, which generalizes a known one for the case M = Nr= Ntto any (M, Nr, Nt), the IR performs memoryless linear operations on its received signal so as to neutralize interference at the receivers, and the beamforming matrices used by the IR and the transmitters are jointly designed. This joint design strictly outperforms known achievable schemes. Two outer bounds are derived. An information theoretic outer bound is obtained by giving the receivers or the IR genie side information, so that the DoF region of the resulting enhanced channel is known; this converse is valid for any type of processing at the IR and shows the optimality of the proposed achievable scheme for some (M, Nr, Nt). A linear processing outer bound is obtained when the IR is restricted to performs linear operations, without any memoryless restriction, on its received signal and shows the optimality of the proposed achievable scheme among all linear processing schemes at the IR. As a result of independent interest, the DoF region of the classical multi-antenna two-user Gaussian interference channel without relay when the channel matrices can have any structure is also derived, which generalized available DoF region results that were derived under certain assumptions on the structure of the channel matrices.

The joint sparse recovery problem addresses simultaneous recovery of jointly sparse signals (signal matrix) and their union support whose cardinality is k from their multiple measurement vectors (MMV) obtained through a common sensing matrix. k + 1 is the ideal lower bound on the minimum required number of measurements for perfect recovery for almost all signals, i.e., excluding a set of Lebesgue measure zero. To get close to the lower bound by taking advantage of the signal structure, Lee, et al. proposed the Subspace-Augmented MUltiple SIgnal Classification (SA-MUSIC) method which is guaranteed to achieve the lower bound when the rank of signal matrix is k and provided less restrictive conditions than existing methods in approaching k +1 in the practically important case when the rank of the signal matrix is smaller than k. The conditions, however, are still restrictive despite its empirically superior performance. We propose an efficient algorithm called the Two-stage orthogonal Subspace Matching Pursuit (TSMP) which has less theoretical restriction in approaching the lower bound than existing algorithms. Empirical results show that the TSMP method with low complexity outperforms most existing methods. The proposed scheme has better empirical performance than most existing methods even in the single measurement vectors (SMV) problem case. Variants of restricted isometry property or mutual coherence are used to improve the theoretical guarantees of TSMP and to cover the noisy case as well.

In this paper, we prove a sharp limit on the community detection problem with colored edges. We assume two equal-sized communities and there are m different types of edges. If two vertices are in the same community, the distribution of edges follows pi= αilog n/n for 1 ≤ i ≤ m, otherwise the distribution of edges is qi= βilog n/n for 1 ≤ i ≤ m, where αiand αiare positive constants and n is the total number of vertices. Under these assumptions, a fundamental limit on community detection is characterized using the Hellinger distance between the two distributions. If Σi=1m(√αi- √βi)2> 2, then the community detection via maximum likelihood (ML) estimator is possible with high probability. If Σi=1m(√αi- √βi)2<; 2, the probability that the ML estimator fails to detect the communities does not go to zero.

In this paper, we show the spatial degrees of freedom (DoF) in free space can be improved by using superdirectivity and such a gain is theoretically unlimited regardless of the physical size of the transmit and receive antenna arrays. Our conclusion is based on calculating the exact power consumption in the presence of mutual coupling and on calculating the exact spatial correlation of the noise field using the fluctuation dissipation theorem. As a result, high current excitation and high signal-to-noise ratio are available at the transmitter and the receiver, respectively. They were not believed to be possible in many previous works that did not consider such exact behaviors. We also analyze the effect of scattering in the near field region and discuss some solutions for many practical problems arising in implementing superdirectivity.

In this paper, we take a unified approach for network information theory and prove a coding theorem, which can recover most of the achievability results in network information theory that are based on random coding. The final single-letter expression has a very simple form, which was made possible by treating sources, channels, states and side information in a unified way and by combining various constraints such as cost and distortion constraints as a single joint-typicality constraint. To demonstrate usefulness of our unified coding theorem, we show that a generalized decode-compress-amplify-and-forward bound can be obtained as a simple corollary of our theorem and show it strictly outperforms previously known coding schemes. Using our unified framework, we formally define and characterize three types of network duality based on channel input-output reversal and network flow reversal combined with packing-covering duality.

In this paper, we propose a noisy network coding integrated with partial decode-and-forward relaying for single-source multicast discrete memoryless networks (DMN's). Our coding scheme generalizes the partial-decode-compress-and-forward scheme (Theorem 7) by Cover and El Gamal. This is the first time the theorem is generalized for DMN's such that each relay performs both partial decode-and-forward and compress-and-forward simultaneously. Our coding scheme simultaneously generalizes both noisy network coding by Lim, Kim, El Gamal, and Chung and distributed decode-and-forward by Lim, Kim, and Kim. It is not trivial to combine the two schemes because of inherent incompatibility in their encoding and decoding strategies. We solve this problem by sending the same long message over multiple blocks at the source and at the same time by letting the source find the auxiliary covering indices that carry information about the message simultaneously over all blocks.

This paper studies the degrees of freedom (DoF) of the two-user multi-antenna Gaussian interference channel with an instantaneous relay, or relay without delay, where the relay transmitted signal in channel use t can depend on all received signals up to and including that at channel use t. It is assumed that the two transmitters and the two receivers have M antennas, while the relay receives through N antennas and transmits through L antennas. An achievable DoF is derived for all possible values of (M;N;L), based on a linear transmission strategy at the relay that aims to neutralize as much interference as possible at each destination. The proposed scheme is shown to attain the largest DoF among all linear transmission strategies at the relay and to actually be the optimal DoF for certain values of (M;N;L).

In this paper, we study causal discrete memoryless relay networks. The network consists of multiple nodes, each of which can be a source, a relay, and/or a destination. In the network, there are two types of relays: 1) relays with one sample delay (strictly causal) and 2) relays without delay (causal) whose transmit signals depend not only on the past received symbols but also on the current received symbols. For this network, we derive two new cut-set bounds, one when every node has a message and the other when only the strictly causal relays have messages. Using the examples of a causal vector Gaussian two-way relay channel and a causal vector Gaussian relay channel, we show that the new cut-set bounds can be achieved by a simple amplify-and-forward type relaying. Our result for the causal relay channel strengthens the previously known capacity result for the same channel by El Gamal, Hassanpour, and Mammen.

We study the sum degrees of freedom (DoFs) of the K-user rank-deficient interference channel with feedback. For the two-user case, we characterize the sum DoF by developing an achievable scheme and deriving a matching upper bound. For the three-user case, we develop a new achievable scheme which employs interference alignment to efficiently utilize the dimension of the received signal space. In addition, we derive an upper bound for the general K-user case and show the tightness of the bound when the number of antennas at each node is sufficiently large. As a consequence of these results, we show that feedback can increase the DoF when the number of antennas at each node is large enough as compared with the ranks of channel matrices. This finding is in contrast to the full-rank interference channel where feedback provides no DoF gain. The gain comes from using feedback to provide alternative signal paths, thereby effectively increasing the ranks of desired channel matrices.

In this paper, we study a two-user interference channel with a causal relay, where the relay's transmit symbol depends not only on its past received symbols, but also on its present received symbol. This is an appropriate model for studying amplify-and-forward type relaying when the bandwidth delay-spread product is much smaller than one. For the discrete memoryless interference channel with a causal relay, we derive a genie-aided outer bound. For the Gaussian interference channel with a causal relay, we define strong and very strong interference conditions and propose an outer bound for each case. We also propose an achievable scheme based on instantaneous amplify-and-forward (AF) relaying for the Gaussian interference channel with a causal relay and so it achieves capacity under some conditions. Our result extends the previous result by El Gamal, Hassanpour, and Mammen on the optimality of instantaneous AF relaying for the Gaussian relay channel with a causal relay to that of the Gaussian interference channel with a causal relay under strong and very strong interference.

We characterize the total degrees of freedom (DoF) of the two-user rank-deficient interference channel with feedback, in which transmitter i and receiver j use Miand Njantennas, respectively, and the rank of the channel matrix between transmitter i and receiver j is given by Dji≤ min(Mi, Nj) ∀i, j = 1,2. One consequence of this result is that feedback can increase the DoF when the number of antennas at each node is large enough as compared to the ranks of channel matrices. This finding is in contrast to the full-rank interference channel where feedback provides no DoF gain. The gain comes from using feedback to provide alternative signal paths, thereby effectively increasing the ranks of desired channel matrices.

In this paper, the capacity of wireless channels is characterized based on electromagnetic and antenna theories with only minimal assumptions. We assume the transmitter can generate an arbitrary current distribution inside a spherical region and the receive antennas are uniformly distributed on a bigger sphere surrounding the transmitter. The capacity is shown to be (αP/N0) log e [bits/sec] in the limit of large number of receive antennas, where P is the transmit power constraint, a is the normalized density of the receive antennas and N0is the noise power spectral density. Although this result may look trivial, it is surprising in two ways. First, this result holds regardless of the bandwidth (bandwith can even be negligibly small). Second, this result shows that the capacity is irrespective of the size of the region containing the transmitter. This is against some previous results that claimed the maximum degrees of freedom is proportional to the surface area containing the transmitter normalized by the square of the wavelength. Our result has important practical implications since it shows that even a compact antenna array with negligible bandwidth and antenna spacing well below the wavelength can provide a huge throughput as if the array was big enough so that the antenna spacing is on the order of the wavelength.

We establish an achievable rate region for discrete memoryless interference relay channels that consist of two source-destination pairs and one or more relays. We develop an achievable scheme combining Han-Kobayashi and noisy network coding. We apply our achievability to two cases. First, we characterize the capacity region of some classes of discrete memoryless interference relay channels. These classes naturally generalize the injective deterministic discrete memoryless interference channel by El Gamal and Costa and the discrete memoryless relay channel. Moreover, for the Gaussian interference relay channel with orthogonal receiver components, we show that our scheme achieves a better sum rate than that of noisy network coding.

We consider a discrete memoryless broadcast relay network that consists of one transmitter, two receivers and a relay. The transmitter sends independent messages to each receiver using Marton coding. The relay performs decode-and-forward using another Marton coding. We show the achievable rate region of the proposed scheme and also provide an outer bound for the channel.

In this paper, we study a layered linear binary field network with time-varying channels, which is a simplified model reflecting broadcast, interference, and fading natures of wireless communications. We observe that fading can play an important role in mitigating interuser interference effectively for both single-hop and multihop networks. We propose new coding schemes with randomized ergodic channel pairing, which exploit such channel variations, and derive their achievable ergodic rates. By comparing them with the cut-set upper bound, the capacity region of single-hop networks and the sum capacity of multihop networks are characterized for some classes of channel distributions and network topologies.

In this paper, we characterize the capacity of a new class of discrete memoryless multicast networks having a tree topology. For achievability, a novel coding scheme is constructed where some relays employ a combination of decode-and-forward and compress-and-forward and the other relays perform a random binning such that codebook constructions and relay operations are independent for each node and do not depend on the network topology. For converse, a new technique of iteratively manipulating inequalities exploiting the tree topology is used. This class of multicast tree networks includes the class of diamond networks studied by Kang and Ulukus as a special case.

We study benefits of opportunistic routing in a large wireless ad hoc network by examining how the power, delay, and total throughput scale as the number of source-destination pairs increases up to the operating maximum. Our opportunistic routing is novel in a sense that it is massively parallel, i.e., it is performed by many nodes simultaneously to maximize the opportunistic gain while controlling the interuser interference. The scaling behavior of conventional multihop transmission that does not employ opportunistic routing is also examined for comparison. Our main results indicate that our opportunistic routing can exhibit a net improvement in overall power-delay tradeoff over the conventional routing by providing up to a logarithmic boost in the scaling law. Such a gain is possible since the receivers can tolerate more interference due to the increased received signal power provided by the multi user diversity gain, which means that having more simultaneous transmissions is possible.

In this paper, message dissemination with node mobility is studied where each node in the network having mobility wants to send its message to all the other nodes. The channel capacity between two nodes is assumed to be large enough for exchanging all the messages they have when they get close enough. We show that what type of network graph enables each node to accumulate all messages in the network, and investigate the dissemination time T for all nodes to get all messages. For a general directed graph model, the upper and lower bounds on T are given as Θ(n2) and Θ(1), respectively. For some special cases, we present tighter bounds. For general undirected graph, T is upper bounded by Θ(n). For grid graph, T is an order of Θ(√n).

In this paper, we consider causal discrete-memoryless relay networks (DMRNs) with causal side information. The networks consist of multiple nodes, each of which can be a source, relay, and/or destination. There are two types of relays in the network, i.e., relays with one sample delay (strictly causal relays) and relays without delay (causal relays) whose transmit signal depends not only on the past received symbols but also on the current received symbol. Also, we assume that some nodes can causally use channel state information (CSI) as side information. For the network, we derive a new cut-set bound, which recovers the classical cut-set bound and our previous cut-set bound for causal DMRNs without side information. Using an example of a fading relay channel, we show that the new cut-set bound can be achieved by a simple amplify-and-forward type relaying.

Single-message unequal error protection (UEP) is a channel coding scheme that protects one special message differently from other (regular) messages. This induces three different types of errors in the system: 1) miss (where we decode the special codeword as a regular codeword), 2) false alarm (where we decode a regular codeword as the special codeword), and 3) decoding error (where we decode a regular codeword to another regular codeword). In this paper, we investigate the fundamental limits of single-message UEP, in the context of discrete memoryless channels (DMCs) without feedback. Similar to Borade et al., we use error exponents as the performance metric, and discuss maximizing the miss error exponent and the false alarm error exponent, respectively. We provide a new converse proof for the miss reliability function, i.e., the optimal miss error exponent as a function of communication rate, and extend the inner and outer bound results for the false alarm reliability function in Borade et al. from rates close to capacity to all rates up to capacity.

This paper considers a Gaussian multiple-input single-output (MISO) broadcast channel with a per-antenna peak power constraint (or simply peak power constraint). It is more realistic to consider the peak power constraint on each transmit antenna because in many practical implementations each antenna is equipped with its own power amplifier. Assuming the perfect knowledge of the channel state information (CSI) at the transmitter, we propose an achievable scheme using dirty-tape coding (DTC). The ideal dirty-paper coding (DPC), which is a capacity-achieving scheme for the Gaussian multiple-input multiple-output (MIMO) broadcast channel, cannot be used for our model because its optimal input distribution is Gaussian and thus the peak power constraint is violated. On the other hand, the channel input of DTC is uniformly distributed in a fixed range, which helps to control the peak power of the transmit signal easily. We also present an algorithm that finds capacity-achieving beamforming vectors and power allocation factors under a per-antenna average power constraint and use the optimized parameters in the proposed scheme. Simulation results show that the proposed scheme provides gains of 2.7dB over a non-DTC scheme based on minimum mean square error (MMSE) beamforming at high signal-to-noise ratio (SNR) when there are three receivers and the transmitter has three antennas.

We consider a class of K-user line-of-sight interference channels without channel state information at transmitters (CSIT). All transmitters are fixed while receiver i has high mobility if i∈{1,2,⋯,K1} and has low mobility otherwise, where K-1≤ K1≤ K, and the channel coherence times for high- and low-mobility receivers are given by one and T, respectively. In addition, we assume that both transmitter i and receiver i use M≥ T antennas if i∈{1,2,⋯K1} and use only one antenna otherwise. In this paper, we propose an interference alignment (IA) scheme for these channels. The key idea is to group receivers with different mobility to have different coherence time. The results show that IA can improve the degrees of freedom even in the absence of CSIT under certain conditions.

We show that the 2 × 2 × 2 interference network, i.e., the multihop interference network formed by concatenation of two two-user interference channels achieves the min-cut outer bound value of 2 DoF, for almost all values of channel coefficients, for both time-varying or fixed-channel coefficients. The key to this result is a new idea, called aligned interference neutralization, that provides a way to align interference terms over each hop in a manner that allows them to be canceled over the air at the last hop.

We consider a wireless network in which a single-source node havingmantennas transmits independent messages tondestination nodes, where each destination node has a single antenna. We assume that the source is located at the center of a unit area and the destinations are located uniformly at random in the same area. By applying transmit beamforming at the source, an achievable sum rate can scale as min{m,n}, which is defined by the aggregate rate of all messages. For transmit beamforming, however, channel state information (CSI) is essentially required at the transmitter (CSIT), which is hard to acquire in practice because of the time-varying nature of wireless channels and feedback overhead. We show that, even without CSIT, almost the same sum rate scaling law assuming CSIT is achievable by inducing cooperation between destinations. Specifically, we study the sum rate scaling law when both the numbermof the source antennas and the numbernof destinations increase such thatn=mβfor β >; 0. If β >; 1 the optimal sum rate scales asmlogmand if 0mβ(1-ε)andmβlogm, where ε >; 0 is an arbitrarily small constant, which shows significant improvement compared to the case of no cooperation between destinations. Our result is of particular interest because we did not assume any additional bandwidth for cooperation.

In this paper, we characterize the information-theoretic capacity scaling of wireless ad hoc networks with randomly distributed nodes. By using an exact channel model from Maxwell's equations, we successfully resolve the conflict in the literature between the linear capacity scaling by Özgür and the degrees of freedom limit given as the ratio of the network diameter and the wavelength by Franceschetti In dense networks where the network area is fixed, the capacity scaling is given as the minimum of and the degrees of freedom limit to within an arbitrarily small exponent. In extended networks where the network area is linear in , the capacity scaling is given as the minimum of and the degrees of freedom limit to within an arbitrarily small exponent. Hence, we recover the linear capacity scaling by Özgür if in dense networks and if in extended networks. Otherwise, the capacity scaling is given as the degrees of freedom limit characterized by Franceschetti For achievability, a modified hierarchical cooperation is proposed based on a lower bound on the capacity of multiple-input multiple-output channel between two node clusters using our channel model.

We analyze the achievable degrees of freedom (DoF) of the time-varying K-user rank deficient (M, N, D) interference channel (IC) in which each transmitter and receiver uses M and N antennas, respectively, and the rank of each channel matrix is equal to D ≤ min (M, N). The proposed achievable schemes are based on zero forcing and interference alignment. We see that the total DoF of min (DK, max(M+N-D,(DL/L+1)K)) is achievable, where L=(max(M,N/D)) and (x) denotes the integer part of x. The result shows that the achievable total DoF of the K-user (M, N, D) IC has similar tendency to that of the full rank IC.

We show that the 2 × 2 × 2 interference network, i.e., the multihop interference network formed by concatenation of two 2-user interference channels achieves the min-cut outer bound value of 2 DoF, for almost all values of channel coefficients, for both time-varying or fixed channel coefficients. The key to this result is a new idea, called aligned interference neutralization, that provides a way to align interference terms over each hop in a manner that allows them to be cancelled over the air at the last hop.

In this paper, we characterize the capacity of two new classes of relay channels, which we call more capable and less noisy relay channels. In these relay channels, the channel seen by the relay is stronger than that seen by the destination in a certain sense, which resembles the conditions for the more capable and less noisy broadcast channels. In more capable relay channels, decode-and-forward (DF) is shown to be optimal. The more capable relay channel includes as special cases several examples in the literature where DF is shown to be optimal. In less noisy relay channels, partial decode-and-forward (PDF) is shown to achieve the capacity. This is the third class of relay channels where PDF is shown to be optimal, where two previously known classes are the semideterministic relay channel and the relay channel with orthogonal components at the source. By using the definition of less noisy relay channel, we can easily construct many examples where DF is strictly suboptimal but PDF is capacity achieving.

Based on recent work on asynchronous communication, this paper proposes a slotted asynchronous channel model and investigates the fundamental limits of asynchronous communication, in terms of miss and false alarm error exponents. We propose coding schemes that are suitable for various asynchronous communication scenarios, and quantify more precisely the suboptimality of training-based schemes, i.e., communication strategies that separate synchronization from information transmission. In particular, we show that under a broad set of conditions, training-based schemes are suboptimal at all positive rates. Finally, we demonstrate these performance differences by specializing our results to BSCs and AWGN channels.

We analyze achievable multiplexing gains (MUXGs) of fully connected K-user line-of-sight (LOS) interference channels (ICs). An array of polarimetric antennas, each composed of three orthogonal electric dipoles and three orthogonal magnetic dipoles in which all six elements are co-located, is used throughout this paper. For a K-user LOS IC with single polarization, the maximum achievable MUXG is only K, regardless of the number of transmit and receive antennas at each node due to the key-hole effect. If polarimetric antennas are used at each node, a trivial upper bound on the MUXG is now 2K. In this paper, we consider zero-forcing (ZF) and interference alignment (IA) schemes to achieve this upper bound. We show the optimal MUXG of 2K is achievable if M ≥ ((K+1)/6) polarimetric antennas are used at each node for any K. In addition, using the proposed ZF scheme, we obtain minimal dipole configurations at each node that achieve this upper bound for K <; 5. Furthermore, we analyze achievable MUXGs using the distributed interference alignment (DIA) algorithm. Although IA can generally provide a higher MUXG than ZF for LOS ICs, we observe that the number of required dipole elements to achieve the optimal MUXG of 2K under a ZF scheme is the same as that required under IA for all cases we consider.

In this paper, we study the diversity-multiplexing tradeoff assuming groupwise successive interference cancelation (GSIC). GSIC with layered codes in multiple-input multiple-output (MIMO) can vary from the plain SIC to the maximum likelihood (ML) decoding according to the group sizes, thus is flexible in both complexity and performance. The maximum group size is a dominating factor for the computation complexity of GSIC when the processing complexity is polynomially dependent on the group size with a high order. In this paper, we show the tradeoff among the maximum group size, diversity gain, and multiplexing gain (GDMT) under the GSIC and under the GSIC combined with antenna selection (GSICAS). For a given group-size bound c, we show that the number of groups achieving the optimal DMT under the GSIC with α selected transmit antennas is equal to ⌈α/c⌉ and a larger group should have a higher priority in decoding order. The optimal grouping is obtained in a systematic way. Based on these results, we find a limited set of groupings that contains the grouping achieving the optimal GDMT under GSIC and under GSICAS. The optimal multiplexing gain allocation for the optimal grouping is then found systematically and efficiently within the limited set.

We study a layeredK-userM-hop Gaussian relay network consisting ofKmnodes in themthlayer, whereM≥ 2 andK=K1=KM+1. We observe that the time-varying nature of wireless channels or fading can be exploited to mitigate the interuser interference. The proposed amplify-and-forward relaying scheme exploits such channel variations and works for a wide class of channel distributions including Rayleigh fading. We show a general achievable degrees of freedom (DoF) region for this class of Gaussian relay networks. Specifically, the set of all (d1,...,dK) such thatdi≤ 1 for alliand Σi=1K di≤KΣis achievable, wherediis the DoF of theithsource-destination pair andKΣis the maximum integer such thatKΣ≤ minm{Km} andM/KΣis an integer. We show that surprisingly the achievable DoF region coincides with the cut-set outer bound ifM/ minm{Km} is an integer; thus, interference-free communication is possible in terms of DoF. We further characterize an achievable DoF region assuming multi-antenna nodes and general message set, which again coincides with the cut-set outer bound for a certain class of networks.

Two distinct, but overlapping, networks that operate at the same time, space, and frequency is considered. The first network consists ofnrandomly distributed primary users, which form an ad hoc network. The second network again consists ofmrandomly distributed ad hoc secondary users or cognitive users. The primary users have priority access to the spectrum and do not need to change their communication protocol in the presence of the secondary users. The secondary users, however, need to adjust their protocol based on knowledge about the locations of the primary users to bring little loss to the primary network's throughput. By introducing preservation regions around primary receivers, a modified multihop routing protocol is proposed for the cognitive users. Assumingm=nβwith β >; 1, it is shown that the secondary network achieves almost the same throughput scaling law as a stand-alone network while the primary network throughput is subject to only a vanishingly small fractional loss. Specifically, the primary network achieves the sum throughput of ordern1/2and, for any δ >; 0, the secondary network achieves the sum throughput of orderm1/2-δwith an arbitrarily small fraction of outage. Thus, almost all secondary source-destination pairs can communicate at a rate of orderm-1/2-δ.

In this paper, we consider a parallel relay network where two relays cooperatively help a source transmit its message to a destination. We assume the source and the destination nodes are equipped with multiple antennas. Three basic schemes and their achievable rates are studied: Decode-and-Forward (DF), Amplify-and-Forward (AF), and Compress-and-Forward (CF). For the DF scheme, the source transmits two private signals, one for each relay, where dirty paper coding (DPC) is used between the two private streams, and a common signal for both relays. The relays make efficient use of the common information to introduce a proper amount of correlation in the transmission to the destination. We show that the DF scheme achieves the capacity under certain conditions. We also show that the AF and CF schemes are asymptotically optimal in the high relay power limit if the relays-to-destination channel is full rank. The relative advantages of the three schemes are discussed with numerical results.

A noisy network coding scheme for communicating messages between multiple sources and destinations over a general noisy network is presented. For multi-message multicast networks, the scheme naturally generalizes network coding over noiseless networks by Ahlswede, Cai, Li, and Yeung, and compress-forward coding for the relay channel by Cover and El Gamal to discrete memoryless and Gaussian networks. The scheme also extends the results on coding for wireless relay networks and deterministic networks by Avestimehr, Diggavi, and Tse, and coding for wireless erasure networks by Dana, Gowaikar, Palanki, Hassibi, and Effros. The scheme involves lossy compression by the relay as in the compress-forward coding scheme for the relay channel. However, unlike previous compress-forward schemes in which independent messages are sent over multiple blocks, the same message is sent multiple times using independent codebooks as in the network coding scheme for cyclic networks. Furthermore, the relays do not use Wyner-Ziv binning as in previous compress-forward schemes, and each decoder performs simultaneous decoding of the received signals from all the blocks without uniquely decoding the compression indices. A consequence of this new scheme is that achievability is proved simply and more generally without resorting to time expansion to extend results for acyclic networks to networks with cycles. The noisy network coding scheme is then extended to general multi-message networks by combining it with decoding techniques for the interference channel. For the Gaussian multicast network, noisy network coding improves the previously established gap to the cutset bound. We also demonstrate through two popular Gaussian network examples that noisy network coding can outperform conventional compress-forward, amplify-forward, and hash-forward coding schemes.

In this paper, we consider a class of single-source multicast relay networks. We assume that all outgoing channels of a node in the network to its neighbors are orthogonal while the incoming signals from its neighbors can interfere with each other. We first focus on Gaussian relay networks with interference and find an achievable rate using a lattice coding scheme. We show that the achievable rate of our scheme is within a constant bit gap from the information theoretic cut-set bound, where the constant depends only on the network topology, but not on the transmit power, noise variance, and channel gains. This is similar to a recent result by Avestimehr, Diggavi, and Tse, who showed an approximate capacity characterization for general Gaussian relay networks. However, our achievability uses a structured code instead of a random one. Using the idea used in the Gaussian case, we also consider a linear finite-field symmetric network with interference and characterize its capacity using a linear coding scheme.

This paper analyzes the impact and benefits of infrastructure support in improving the throughput scaling in networks ofnrandomly located wireless nodes. The infrastructure uses multiantenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbitrary rates relative ton. Under the model, capacity scaling laws are analyzed for both dense and extended networks. Two BS-based routing schemes are first introduced in this study: an infrastructure-supported single-hop (ISH) routing protocol with multiple-access uplink and broadcast downlink and an infrastructure-supported multihop (IMH) routing protocol. Then, their achievable throughput scalings are analyzed. These schemes are compared against two conventional schemes without BSs: the multihop (MH) transmission and hierarchical cooperation (HC) schemes. It is shown that a linear throughput scaling is achieved in dense networks, as in the case without help of BSs. In contrast, the proposed BS-based routing schemes can, under realistic network conditions, improve the throughput scaling significantly in extended networks. The gain comes from the following advantages of these BS-based protocols. First, more nodes can transmit simultaneously in the proposed scheme than in the MH scheme if the number of BSs and the number of antennas are large enough. Second, by improving the long-distance signal-to-noise ratio (SNR), the received signal power can be larger than that of the HC, enabling a better throughput scaling under extended networks. Furthermore, by deriving the corresponding information-theoretic cut-set upper bounds, it is shown under extended networks that a combination of four schemes IMH, ISH, MH, and HC is order-optimal in all operating regimes.

This paper considers a two user Gaussian multiple-input single-output (MISO) broadcast channel with a per-antenna peak power constraint (or simply peak power constraint). It is more realistic to consider the peak power constraint on each transmit antenna because each antenna is equipped with its own power amplifier in many practical implementations. Assuming the perfect channel state information (CSI) at the transmitter, we propose an achievable scheme using a dirty-tape coding (DTC). The uniform input in a fixed range of the DTC scheme helps to control the peak power of the transmit signal easily. We also present an optimization algorithm that finds the capacity achieving beamforming vectors and power allocations under a per-antenna average power constraint used in our achievable scheme. Simulation results show that as the transmit power increases, the achievable rate region under the peak power constraint is getting close to the capacity region under the reduced per-antenna average power constraint by 1/3. Compared to a non-DTC scheme based on minimum mean square error (MMSE) beamforming, the proposed scheme performs better.

In this paper, we characterize the capacity of a class of single-source single-destination discrete memoryless relay networks with an arbitrary number of nodes. In this class, the network is assumed to have a tree topology where the root node is the source, each parent node in the graph has at most one noisy child node and any number of noiseless child nodes, and the set of leaf nodes is the destination. A combination of decode-and-forward (DF) and compress-and-forward (CF) at noisy relay nodes is shown to be optimal. Our result is the first to show that the combination of DF and CF is capacity achieving for a non-trivial class of noisy networks with an arbitrary number of nodes.

In this paper, we study the capacity scaling of wireless ad hoc networks considering the effect of a finite wavelength, which gives a unified view on two seemingly contradictory results on the capacity scaling. Recently, it was shown that the order-optimal linear throughput scaling is achievable for some networks using hierarchical cooperation (HC) by Özgür et al., but later it was proved to violate the physical limit by Franceschetti et al. The cause of such a contradiction is the idealized channel model in the former that does not capture the channel correlation due to the finite wavelength. Taking into account such an effect of the finite wavelength, we construct a modified HC scheme and analyze its throughput scaling in terms of both the number of nodes and wavelength. Our result is consistent with the physical limit while recovering the linear throughput scaling asymptotically as the wavelength tends to zero.

Noisy network coding unifies network coding by Ahlswede, Cai, Li, and Yeung for noiseless networks and compress-forward by Cover and El Gamal for noisy relay channels. In particular, it achieves the best known capacity inner bounds for multi-source multicast networks including deterministic networks by Avestimehr, Diggavi, and Tse and erasure networks by Dana, Gowaikar, Palanki, Hassibi, and Effros. This paper extends noisy network coding for multicast networks to networks with general message demand by combining the underlying noisy network coding scheme with decoding techniques for interference channels. At one extreme, noisy network coding is combined with simultaneous decoding, while at the other extreme interference is treated as noise. The potential of noisy network coding as a canonical building block for wireless networks is demonstrated via three examples of Gaussian networks that have drawn recent attentions.

We study K-user M-hop Gaussian relay networks with Kmnodes in the m-th layer, where M is even and K = K1= KM+1. We observe that the time-varying nature of wireless channels (fading) can be exploited to mitigate the inter-user interference. The proposed block Markov encoding and relaying scheme exploits such channel variations and works for any isotropically distributed channels including Rayleigh fading. We show a general achievable degrees of freedom (DoF) region of this class of Gaussian relay networks, which coincides with the cut-set outer bound if M/Kminis an integer, where Kmin= minm{Km}. Therefore, we completely characterize the DoF region for the case where M/Kminis an integer.

In this letter, we implement a simplified version of the Cover van der Meulen Hajek Pursley (CMHP) coding originally characterized by Wajcer, Wiesel, and Shamai. The vector Gaussian broadcast channel with imperfect channel state information at the transmitter (CSIT) is considered where the transmitter only knows the channel mean and variance. Our focus is on the implementation and performance analysis of CMHP under the imperfect CSIT model using practical codes. Turbo codes described in IEEE 802.20 draft specification and quadrature amplitude modulation are used to implement CMHP. In order to find the optimal power allocation and beamforming vectors which maximize the sum rate with practical codes, we introduce the SINR penalty factor. The SNRs that achieve various target spectral efficiency are presented and analyzed.

In this paper, a Gaussian two-way relay channel, where two source nodes exchange messages with each other through a relay, is considered. We assume that all nodes operate in full-duplex mode and there is no direct channel between the source nodes. We propose an achievable scheme composed of nested lattice codes for the uplink and structured binning for the downlink. Unlike conventional nested lattice codes, our codes utilize two different shaping lattices for source nodes based on a three-stage lattice partition chain, which is a key ingredient for producing the best gap-to-capacity results to date. Specifically, for all channel parameters, the achievable rate region of our scheme is within1/2bit from the capacity region for each user and its sum rate is within log3/2bit from the sum capacity.

We propose a cognitive radio system in fading environments where the secondary sender opportunistically violates the secondary-to-primary (S-P) interference power limit. Assuming a slowly-varying fading interference channel with two-senders and two-receivers, the primary sender adjusts its rate and power depending on its own channel, while ignoring the secondary user. Through an optimization process, the secondary sender's rate and power are determined and the decision is made on whether the secondary sender violates the S-P interference power limit. The primary receiver removes the effect of excessive S-P interference by multiuser decoding (MUD) which jointly decodes the primary and secondary senders' data. Achievable rates of the proposed system are examined by computer simulation. It was observed that the proposed system tends to violate the SP interference power limit when the S-P channel gain is larger than the other channel gains. Remarkably, the proposed system can provide a considerably high data rate for the secondary user even without sacrificing the data rate for the primary user over a wide range of signal-to-noise ratios.

In cellular networks, relays and/or mobiles can cooperate to improve the overall system performance. For example, once the transmission from a base station with multiple antennas is received by multiple nodes (relays and/or mobiles), they can exchange information to enhance the quality of intended signals while suppressing interference. Such virtual multiple- input multiple-output (MIMO) transmission is a key ingredient in hierarchical cooperation by Ozgiir et al., which was shown to improve the throughput scaling of ad hoc networks greatly. In this paper, we analyze the achievable rate of such a cooperative MIMO between a base station with multiple antennas and a node group. An antenna array with a fixed area has a limited number of degrees of freedom. We use a realistic channel model that can capture the effect of geometry of antenna arrays on the number of degrees of freedom. Our achievable rate is limited by the product of the aperture of the antenna array in the base station and the angular spread, which is consistent with some known results on the limit of the degrees of antenna arrays.

The separability in parallel Gaussian interference channels (PGICs) is studied in this paper. We generalize the separability results in one-sided PGICs (OPGICs) by Sung et al. to two-sided PGICs (TPGICs). Specifically, for strong and mixed TPGICs, we show necessary and sufficient conditions for the separability. For this, we show diagonal covariance matrices are sum-rate optimal for strong and mixed TPGICs.

We study a fading linear finite-field relay network having multiple source-destination pairs. Because of the interference created by different unicast sessions, the problem of finding its capacity region is in general difficult. We observe that, since channels are time-varying, relays can deliver their received signals by waiting for appropriate channel realizations such that the destinations can decode their messages without interference. We propose a block Markov encoding and relaying scheme that exploits such channel variations. By deriving a general cut-set upper bound and an achievable rate region, we characterize the sum capacity for some classes of channel distributions and network topologies. For example, when the channels are uniformly distributed, the sum capacity is given by the minimum average rank of the channel matrices constructed by all cuts that separate the entire sources and destinations. We also describe other cases where the capacity is characterized.

Motivated by fading channels and erasure channels, the problem of reliable communication over deterministic relay networks is studied, in which relay nodes receive a function of the incoming signals and a random network state. An achievable rate is characterized for the case in which destination nodes have full knowledge of the state information. If the relay nodes receive a linear function of the incoming signals and the state in a finite field, then the achievable rate is shown to be optimal, meeting the cut-set upper bound on the capacity. This result generalizes on a unified framework the work of Avestimehr, Diggavi, and Tse on the deterministic networks with state dependency, the work of Dana, Gowaikar, Palanki, Hassibi, and Effros on linear erasure networks with interference, and the work of Smith and Vishwanath on linear erasure networks with broadcast.

On behalf of the Technical Programme Committee, we are glad to welcome you to the 7th edition Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

We study two distinct, but overlapping, networks which operate at the same time, space and frequency. The first network consists of n randomly distributed primary users, which form either an ad hoc network, or an infrastructure supported ad hoc network in which l additional base stations support the primary users. The second network consists of m randomly distributed secondary or cognitive users. The primary users have priority access to the spectrum and do not change their communication protocol in the presence of secondary users. The secondary users, however, need to adjust their protocol based on knowledge about the locations of the primary users so as not to harm the primary network's scaling law. Base on percolation theory, we show that surprisingly, when the secondary network is denser than the primary network, both networks can simultaneously achieve the same throughput scaling as a standalone ad hoc network.

A Gaussian multiple-input multiple-output broadcast channel (MIMO GBC) is considered. Throughout the paper it is assumed that 1) input signals are Gaussian and 2) perfect channel state information is available at the transmitter and at the receivers. By considering each data stream as a single user, the uplink-downlink signal-to-interference-plus-noise (SINR) duality is generalized to the MIMO case with general cross-talk matrix. The duality is subsequently applied to finding the solution for the SINR-balancing problem. The result serves as a tool for characterizing achievable rate regions of different coding strategies. Next, we investigate a superposition coding scheme proposed by Cover-van der Meulen-Hajek and Pursley (nicknamed CMHP), where there is a common message to both users. We consider a MIMO broadcast channel with two users, each user has two antennas and the transmitter has four antennas. Assuming one common stream is sent by CMHP coding and successive decoding, a lower bound to the CMHP rate region is found. Behaviors of the CMHP rate region and sumrate are analyzed. We find the sumrate gaps between DPC, CMHP, and MMSE at high SNR for general 2-user multiple-input singleoutput (MISO) Gaussian broadcast channel. The result suggests when CMHP is beneficial for sumrate.

In this paper, we propose a network coding using a lattice for the two-way relay channel with two nodes communicating bidirectionally through a relay, which we call modulo-and- forward (MF). Our scheme extends the network coding in the binary channel to the Gaussian channel case, where XOR in the binary case is replaced by mod Lambda for the Gaussian case, where Lambda is a high-dimensional lattice whose shaping gain is close to optimal. If the relay node re-transmits the received signal after the mod Lambda operation, we can reduce the complexity compared to decode-and-forward (DF) and can get a better power efficiency compared to amplify-and-forward (AF). When the transmission powers of two nodes are different, we use superposition coding and partial decoding at the relay node. Finally, we plot and compare the sum rates of three different schemes, i.e., AF, DF, and MF. We show that by applying the proposed scheme, we can get better performance than AF and DF schemes under some conditions.

We construct lattices with high coding gains based on nested low-density parity-check (LDPC) codes by Construction D′.We generalize the LDPC lattices [1] to have irregular degrees and call them irregular LDPC lattices. To construct good irregular LDPC lattices, we optimize the degree distributions by using density evolution and a modified sequential quadratic programming (SQP) and show our optimized irregular LDPC lattice has a threshold 0.48dB from the capacity, which is about 0.6dB better than the regular one in [1]. To construct a Tanner graph corresponding to LDPC lattices, we generalize the progressive-edge growth (PEG) algorithm and show a lower bound on the girth of the graph. Simulation is performed using the sum-product algorithm. Also, we compare the performance of joint decoding and multi-stage decoding [2] and show the advantage of joint decoding.

A hierarchical cooperative multiple-input multiple-output (MIMO) transmission can greatly improve the throughput scaling in wireless networks as shown in [1]. In this paper, we analyze the effect of channel correlation on the throughput scaling in wireless networks by focusing on an achievable rate of the cooperative MIMO transmission between two clusters of nodes as a function of the number of nodes in each cluster, the physical size of each cluster, and the distance between the two clusters. Channel correlation occurs naturally in a regime when the effect of the wavelength cannot be ignored. We use a realistic channel model that can model this channel correlation accurately. Although it is not critical, we assume more generally multiple antennas are allowed per node since this assumption becomes natural for our channel model. Our main results are in a sense consistent with the upper bounds in [2], [3] on the degrees of freedom in MIMO and in wireless networks.

We analyze performance-complexity tradeoffs of rateless codes over noisy symmetric channels. Unlike in erasure channels, the decoder for such codes needs to use all received symbols in noisy channels because each of them have some information about the transmitted message. To reduce the complexity, the receiver can discard some unreliable symbols, but it must receive more symbols due to the lost information. This results in a performance-complexity tradeoff. We also consider another scenario where a rateless code is concatenated with a fixed-rate code, which is typically used in practice, e.g., in [1]. The fixed-rate code provides a soft-decision decoding and only correctly decoded blocks are used in the rateless code decoder. If some soft information of error-detected blocks is also used, the receiver can decode the message with less received symbols at the expense of increased processing. This results in another type of performance-complexity tradeoff. For these scenarios, we find the optimal tradeoffs and show sub-optimal tradeoffs achievable by practical rateless codes.

We analyze the benefits of infrastructure support in improving the throughput scaling in networks of n randomly located wireless nodes. The infrastructure uses multi-antenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbtrary rates relative to n. We introduce two multi-antenna BS-based routing protocols and analyze their throughput scaling laws. Two conventional schemes not using BSs are also shown for comparison. In dense networks, we show that the BS-based routing schemes do not improve the throughput scaling. In contrast, in extended networks, we show what our BS-based routing schemes can, under certain network conditions, improve the throughput scaling significantly.

In this paper, we analyze the diversity-multiplexing tradeoff (DMT), originally introduced by Zheng and Tse, and outage performance for Rician multiple-input-multiple-output (MIMO) channels. The DMT characteristics of Rayleigh and Rician channels are shown to be identical. In a high signal-to-noise ratio (SNR) regime, the log-log plot of outage probability versus SNR curve for a Rician channel is a shifted version of that for the corresponding Rayleigh channel. The SNR gap between the outage curves of the Rayleigh and Rician channels is derived. The DMT and outage performance are also analyzed for Rician multiple-input-single-output (MISO)/single-input-multiple-output (SIMO) channels over a finite SNR regime. A closed-form expression for the outage probability is derived and the finite SNR DMT characteristic is analyzed. It is observed that the maximum diversity gain can be achieved at some finite SNR-the maximum gain tends to increase linearly with the Rician factor. The finite SNR diversity gain is shown to be a linear function of the finite SNR multiplexing gain. The consistency between the DMTs for finite and infinite SNRs is also shown.

In this paper, we design and analyze a soft handoff algorithm for code-division multiple access (CDMA) systems in a Rayleigh fading environment. For each mobile, this algorithm selects a set of base stations to be in handoff with the mobile based on the average channel conditions between the mobile and the base stations. For a given target value for the average number of base stations in handoff, our handoff algorithm is optimal in that it maximizes the effective channel gain seen by mobiles. We consider both power control and rate control in the forward and reverse links of CDMA systems, where maximizing the effective channel gain minimizes the average transmit power for the power controlled case and maximizes the average received power for the rate controlled case, respectively.

An intercarrier interference (ICI) canceling technique for multiple-input single-output (MISO) orthogonal frequency division multiplexing (OFDM) systems in fast fading channels is proposed. The proposed scheme consists of a linear space-frequency block code (SFBC) at the transmitter and a receive frequency block code (RFBC) at the receiver. The code design is based on the upper bound of pairwise error probability (PEP) which is derived under assumptions and approximations stated. Specifically, the proposed code is designed to minimize the upper bound of the PEP. The simulation results demonstrate that the proposed technique outperforms the conventional methods in fast fading environments.

This paper considers a broadband cellular orthogonal frequency-division multiple-access (OFDMA) system with relay nodes operating in decode-and-forward and half-duplex mode. Two transmit resource allocation problems for sum-rate maximization are formulated for such a system. The first one is the optimization of subcarrier allocation with predetermined power assignment for each subcarrier, and the other is the joint optimization of power and subcarrier allocation. Since these problems can't be solved easily in direct forms, we make continuous relaxation and solve the dual problems using a subgradient method. Numerical results show that a remarkable increment in sum-rate is achieved, with the aid of relay nodes and sophisticated resource allocation, compared to a system without relay nodes.

We study how fast a broadcast message can be propagated through large wireless networks. A two-phase opportunistic broadcasting is proposed in this paper. At the first phase, all nodes having the message broadcast it simultaneously with random phases, which gives a chance for remote nodes to receive the message through opportunistic beamforming. At the second phase, each node having the message transmits it to its neighbor nodes. By performing this two phases repeatedly, the message propagates through the network. It is shown that the two- phase opportunistic broadcasting achieves a linear increase of the propagation distance. By comparing it with an upper-bound, we show it is asymptotically order optimal in the high attenuation regime. Furthermore, our scheme can have a potentially huge gain compared to naive multihop broadcasting.

We study the benefits of opportunistic routing in wireless networks by examining how the power and delay scale as the number of source-destination (S-D) pairs increases, where S-D pairs are randomly located over the network. The scaling behavior of conventional multi-hop transmission that does not employ opportunistic routing is also examined. The results indicate that the opportunistic routing can exhibit better power- delay trade-off than the conventional routing while providing up to a logarithmic boost in the scaling law. The gain comes from the fact that the system with opportunistic routing can tolerate more interference due to increased received signal power from utilizing the multi-user diversity gain. Furthermore, we derive an upper bound on the total throughput using the cut-set theorem. It is shown that the achievable rates of the conventional and opportunistic routing schemes become close to the upper bound when the number of S-D pairs is large enough.

A limited feedback-based transmit beamforming technique for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is investigated in time-varying channels. The performance of the system is significantly degraded by outdated feedback information even when the channel varies slowly. To compensate for the impairment in time-varying channels, the optimal transmit beamforming vector for a future channel, which maximizes the expected effective channel gain, is derived by applying the autoregressive (AR) model to the channels. These are obtained at the receiver. Following this, schemes for the selection of beamforming vectors are proposed to reduce the feedback amount. These can effectively reduce the amount of feedback information by utilizing both the frequency and time correlation of transmit beamforming vectors. Simulation results show that the proposed techniques outperform existing schemes in terms of the bit error rate (BER) performance with the same amount of feedback.

We design a coding scheme for Costa's dirty paper coding (DPC) (M.H.M. Costa, 1983) using a channel and a shaping code. We show that by transforming the channel noise distribution the DPC channel can be converted into the binary erasure channel (BEC) with binary interference with memory. Furthermore, the messages exchanged during the iterative decoding between the channel and shaping codes become one dimensional under the new model. We analyze the iterative decoding and find good shaping and channel code pairs using some closed-form extrinsic information transfer (EXIT) curves. We verify that our dirty paper codes designed using this method are also good for the original DPC channel with the additive white Gaussian noise (AWGN) and arbitrary interference. Our implementation of DPC uses short block codes such as repetition codes for shaping codes. Although the shaping gains of such codes are not very high, they may provide a better complexity-performance trade-off for simple practical implementations of DPC. Furthermore, we show accurate theoretical analysis is possible for such codes under our channel model.

In this paper, we propose an adaptive sub-band nulling technique in order to improve the performance of orthogonal frequency division multiplexing (OFDM)-based wireless communication systems. It excludes some sub-bands experiencing deep-fading and the transmission power for the excluded sub-bands is reallocated for the remaining sub-bands. We compute the optimal number of nulled sub-bands in order to maximize the capacity. Water-filling is one well-known optimal resource allocation scheme. However, it has tremendous complexity at the transmitter and requires full channel state information from the receiver. We compare the performance of the proposed scheme with that of the water-filling scheme and the result shows that the performance of the proposed scheme is similar to that of the water-filling in a wide range of signal-to-noise ratio (SNR) values. Furthermore, the proposed adaptive sub-band nulling can be used as an enhanced distributed transmission mode in future OFDM-based wireless communication systems after a small modification in the frame structure.

Rate-t/k (tlesk) intercarrier interference (ICI) canceling codes for orthogonal frequency division multiplexing (OFDM) systems are proposed. The capacity lower bounds of OFDM systems employing these codes are derived for time-varying frequency-selective channels. The optimal codes maximizing these bounds are designed numerically. The simulation results indicate that the optimal codes can provide both higher capacity lower bounds and lower bit error rates (BERs) than the existing ICI canceling codes

If the channel is static and is perfectly known to both the transmitter and the receiver, the water-filling technique with adaptive modulation is known to be optimal (Gallager, 1968). However, for orthogonal frequency-division multiplexing (OFDM) systems, this requires intensive traffic overheads for reporting channel state information on all subcarriers to the transmitter. In this paper, we consider an adaptive modulation and coding scheme for bit-interleaved coded OFDM with reduced feedback information satisfying a specified quality of service level. We propose a rate adaptation scheme, which utilizes the estimated bit error rate for supportable transmission rates. In this scheme, a user equipment chooses a modulation and coding scheme (MCS) level, which can provide the maximum spectral efficiency based on one OFDM symbol rather than on all subchannels. Then the user needs to send back only the selected MCS level index. The proposed scheme does not require the water-filling procedure, and the amount of the feedback information reduces to a single integer value irrespective of the number of subcarriers. Simulation results show that the proposed scheme can significantly reduce the system complexity while minimizing the performance loss compared to the optimum water-filling scheme.

In this paper, we study the robustness of scheduling against channel variations. Specifically, we consider a class of generalized proportional fairness (PF) schedulers that achieve optimal throughput-fairness trade-off and study their robustness in terms of maintaining a certain fairness criterion when the channel statistics change from the additive white Gaussian noise (AWGN) to Rayleigh fading for all users. We conclude that the scheduler is robust only when it is configured to achieve the proportional fairness. For the other scheduler configurations, which are not robust, we show how fairness deviates from the ideal one when the channel statistics change and show how scheduler parameters need to be adjusted to restore the desired fairness. We also show simulation results.

In orthogonal code hopping multiplexing (OCHM) systems, hopping pattern (HP) collisions may degrade the system performance. Previous studies on the effect of HP collisions in OCHM systems were mainly based on computer simulations and there was no rigorous mathematical analysis of bit error rate (BER) performance. The HP collisions in OCHM systems differ from the hits in frequency-hopping (FH) systems or intracell interference in DS-CDMA systems because it can be effectively controlled through synergy and perforation techniques. In this paper, we introduce a received signal model for OCHM systems and analyze the BER performance for OCHM systems. Through the analysis of the BER performance, OCHM systems can be characterized more clearly and the allocated power at base station can be estimated. Furthermore, the user capacity is analyzed for a given channel coding scheme.

The rate-t/k (t

In this paper, we first generalize a recent work of Zheng and Tse on diversity-multiplexing tradeoff (DMT) for some rank-deficient channels (poor scattering). We show that rank deficiency lowers DMT curves from that of i.i.d. Rayleigh fading channels. We show an interesting observation that suggests a fractional diversity gain may be possible at integer multiplexing gains. As the scattering becomes rich, the DMT approaches that of the i.i.d. Rayleigh fading channels. We next focus on spatially correlated multiple-input multiple-output (MIMO) channels. We show that spatial correlation does not change the DMT but still degrades the outage performance and analyze such a degradation at high SNR

We analyze the outage performance and diversity-multiplexing tradeoff (DMT), originally introduced by Zheng and Tse, for multiple antenna Rician channels or channels with partial channel state information at the transmitter (CSIT). The asymptotic behaviors of the outage are analyzed in the limit of high signal-to-noise ratio (SNR). We also analyze the SNR where the maximum diversity order (MDO) is obtained for Rician channels, which serves as a desired operation point. In addition, by applying the outage analysis for Rician channels we present the differential DMT (DDMT) to develop a better understanding of the asymptotic relationship between the outage probability, transmission rate, SNR, and Rician factor

Presented in this paper is a study of the capacity evaluation of various multiuser MIMO schemes in cellular environments. The throughputs per user of the generalized zero-forcing with rank adaptation and vector perturbation schemes are compared with the capacity bound of the Gaussian MIMO broadcast channel, obtained by dirty paper coding under proportional fairness scheduling. The average cell throughputs of these schemes are also compared. From these comparisons, this study provides vital information for applying multiuser MIMO schemes in multicell environments

Density evolution is an algorithm for computing the capacity of low-density parity-check (LDPC) codes under message-passing decoding. For memoryless binary-input continuous-output additive white Gaussian noise (AWGN) channels and sum-product decoders, we use a Gaussian approximation for message densities under density evolution to simplify the analysis of the decoding algorithm. We convert the infinite-dimensional problem of iteratively calculating message densities, which is needed to find the exact threshold, to a one-dimensional problem of updating the means of the Gaussian densities. This simplification not only allows us to calculate the threshold quickly and to understand the behavior of the decoder better, but also makes it easier to design good irregular LDPC codes for AWGN channels. For various regular LDPC codes we have examined, thresholds can be estimated within 0.1 dB of the exact value. For rates between 0.5 and 0.9, codes designed using the Gaussian approximation perform within 0.02 dB of the best performing codes found so far by using density evolution when the maximum variable degree is 10. We show that by using the Gaussian approximation, we can visualize the sum-product decoding algorithm. We also show that the optimization of degree distributions can be understood and done graphically using the visualization.

A simple sphere bound gives the best possible tradeoff between the volume per point of an infinite array L and its error probability on an additive white Gaussian noise (AWGN) channel. It is shown that the sphere bound can be approached by a large class of coset codes or multilevel coset codes with multistage decoding, including certain binary lattices. These codes have structure of the kind that has been found to be useful in practice. Capacity curves and design guidance for practical codes are given. Exponential error bounds for coset codes are developed, generalizing Poltyrev's (1994) bounds for lattices. These results are based on the channel coding theorems of information theory, rather than the Minkowski-Hlawka theorem of lattice theory.

Summary form only given. By jointly optimizing source and channel codes, we can generally get less overall average distortion and more robustness to channel impairments than with separately designed source and channel codes. In many cases, however, it is difficult or sometimes impossible for a single coding scheme to achieve the least possible distortion for a large range of channel variations except the trivial Gaussian case, where the rate distortion bound on the mean square error is achieved for all values of the channel signal to noise ratio (SNR) when the i.i.d. Gaussian source and the additive white Gaussian noise (AWGN) channel have the same bandwidth. In this paper, we show that a simple uncoded system that transmits an unmodified uniform i.i.d. source defined on the unit interval I=[0,1] through a modulo AWGN channel (an AWGN channel followed by a mod-1 mapping such that the channel output is the fractional part of the AWGN channel output) can achieve the rate distortion bound almost optimally for all values of the channel SNR.