Intelligent reflecting surfaces (IRSs) have become a popular topic in recent years for their great potential for controlling the radio link environment of wireless networks. With this controlled environment, the coverage can be increased. This paper examines the coverage analysis of IRS-aided networks, considering both two-dimensional buildings and the product-distance path loss model for the first time. Leveraging the tools from stochastic geometry, the locations of base stations (BSs), buildings, and IRSs are modeled with a homogeneous Poisson point process (PPP). A Gamma approximation for the distribution of the nearest line-of-sight (LoS)-neighbor distance is proposed, leading to a closed-form expression for the distribution of the product-distance. Feasible BSs are defined as BSs which are reachable via an IRS deployed on a specific facade of a building, and the ratio of feasible BSs is derived. Simulations are performed, which confirm the proposed analytical methods. In the numerical results, it is observed that the IRSs can introduce up to a 45% coverage boost, and the effect of the IRS length on the coverage probability is limited beyond 1.2 meters at 60 GHz.

A novel cooperative automatic repeat request (ARQ) method with network coding is proposed for broadcast networks where single source node broadcasts multiple packets to multiple destinations. Upon a failed transmission, multiple relays cooperate with the source node at the retransmission phase, where network coding is used to combine multiple lost packets. A random access mechanism is employed where retransmission is done according to back-off counters. The proposed method employs the back-off mechanism to achieve space diversity by punishing the nodes which fail at previous retransmissions, and to reduce the number of retransmissions by giving priority to nodes having opportunity to employ network coding. The proposed method is compared with network coded ARQ without cooperation and traditional ARQ using Monte Carlo simulations for different channel settings. The effect of channel correlation is investigated where correlation is modeled as a finite-state Markov model. Simulation results show that the proposed use of cooperation and network coding for ARQ improves throughput performance when relay channels' conditions are better than the channels between source and destinations. It is also observed that, the proposed strategy exploits cooperation and space diversity to reduce the performance degradation due to correlation.

This paper extends some approximation methods that are used to identify closed form Bit Error Rate (BER) expressions which are frequently utilized in investigation and comparison of performance for wireless communication systems in the literature. By using this group of approximation methods, some expectation integrals, whose exact analyses are intractable and whose Monte Carlo simulation computations have high complexity, can be computed. For these integrals, by using the sampling property of the integrand functions of one or more arguments, reliable BER expressions revealing the diversity and coding gains are derived. Although the methods we present are valid for a larger class of integration problems, in this work we illustrate the method by showing the step by step derivation of the BER expressions for a canonical cooperative communication scenario as well as a network coded system scenario. The derived expressions agree with the simulation results for a very wide range of signal-to-noise ratio (SNR) values.

We propose a simple yet effective wireless network coding and decoding technique for a multiple unicast network. It utilizes spatial diversity through cooperation between nodes which carry out distributed encoding operations dictated by generator matrices of linear block codes. In order to exemplify the technique, we make use of greedy codes over the binary field and show that the arbitrary diversity orders can be flexibly assigned to nodes. Furthermore, we present the optimal detection rule for the given model that accounts for intermediate node errors and suggest a low-complexity network decoder using the sum-product (SP) algorithm. The proposed SP detector exhibits near optimal performance. We also show asymptotic superiority of network coding over a method that utilizes the wireless channel in a repetitive manner without network coding (NC) and give related rate-diversity trade-off curves. Finally, we extend the given encoding method through selective encoding in order to obtain extra coding gains.

This paper investigates simple means of analyzing the error rate performance of a general q-ary Galois Field network coded detect-and-forward cooperative relay network with known relay error statistics at the destination. Equivalent relay channels are used in obtaining an approximate error rate of the relay network, from which the diversity order is found. Error rate analyses using equivalent relay channel models are shown to be closely matched with simulation results. Using the equivalent relay channels, low complexity receivers are developed whose performances are close to that of the optimal maximum likelihood receiver.

We propose a simple yet effective wireless network coding (NC) and decoding technique. It utilizes spatial diversity through cooperation between nodes which carry out distributed encoding operations dictated by generator matrices of linear block codes. For this purpose, we make use of greedy codes over the binary field and show that desired diversity orders can be flexibly assigned to nodes in a multiple unicast network, contrary to the previous findings in the literature. Furthermore, we present the optimal detection rule for the given model that accounts for intermediate node errors and suggest a network decoder using the sum-product algorithm. The proposed sum-product detector exhibits near optimal performance.

Bi-directional relaying (BDR) in which two users wish to exchange independent information in presence of a relay is one of the fundamental wireless network coding applications that attracted intense interests recently. In this paper, maximum likelihood (ML), cooperative maximal ratio combining (CMRC) and soft information relaying (SIR) schemes proposed to combat error propagation in wireless relay systems are applied to the BDR scenario with demodulate-and-forward protocol, which decreases system complexity and cost by removing the decoding process at the relay terminal. Simulation results for Rayleigh fading channels show that detection schemes using ML and CMRC techniques outperform the noncooperative system that involves direct transmissions between user terminals and overcome the diversity gain reduction due to error propagation for different levels of channel state information (CSI). In case of SIR based communication scenario, the diversity gain is contingent on the availability of a coefficient depending on instantaneous CSI.

Application of the belief propagation (BP) algorithm is proposed for pilot-assisted reception of multi-carrier code division multiple access systems where the input symbols are binary phase shift keying symbols. The proposed method utilizes a Bayesian network graph that includes channel parameters as explicit nodes representing random variables. Channel parameters in both frequency and time domains are included in the graph. The messages on the graph are obtained by making large system assumptions for the user symbols and Gaussian assumptions for the messages related to the channel parameters. Density evolution analysis is performed whose results match well with the simulation results. For the simulated scenario it is found that BP with the proposed graph outperforms channel estimation followed by graph based detection, and non-graph based iterative channel estimation.

Application of the belief propagation (BP) algorithm is proposed for pilot-assisted reception of multi-carrier code division multiple access systems where the input symbols are binary phase shift keying symbols. The proposed method utilizes a Bayesian network graph that includes channel parameters as explicit nodes representing random variables. Channel parameters in both frequency and time domains are included in the graph. The messages on the graph are obtained by making large system assumptions for the transmitted symbol messages and Gaussian assumptions for the messages related to the channel parameters. It is observed via simulations that the proposed approach achieves performance very close to that of the previously proposed BP method with known channel parameters.

Application of the belief propagation (BP) algorithm is proposed for pilot-assisted reception of Gaussian distributed symbols over unknown inter-symbol interference channels whose parameters are also Gaussian distributed. The proposed Bayesian network graph for the demodulator includes the channel parameters as explicit nodes, allowing the combined estimation of the channel and information bearing symbols with the aid of pilot symbols. Since direct application of BP is not tractable for this model, the algorithm is obtained by introducing approximations of the message distributions appropriately. The proposed algorithm is parallel in nature, is suitable for sparse channels, and can be easily extended to coded systems for joint demodulation and decoding. The numerical results show near-optimal performance in the minimum mean squared error sense for a small example, and a performance better than that of iterative semi-blind equalization with batch matrix-based estimators for the general case.

This paper considers the problem of joint detection in the uplink of cellular multiaccess networks with base-station cooperation. Distributed multiuser detection algorithms with local message passing among neighbor base stations are proposed and compared in terms of computational complexity required in the base stations, the amount of serial communications among them, error rate performance, and convergence speed. The algorithms based on the belief propagation algorithm result in complexity and delay per base station which do not grow as the network size increases. In addition, it is observed that these algorithms have near single-user error rate performance for the fading channels considered. Thus it is illustrated that using the belief propagation algorithm, it is possible to have full frequency re-use and achieve near-optimal performance with moderate computational complexity and a limited amount of message passing between base stations of adjacent cells.

Application of the belief propagation (BP) algorithm is proposed for pilot-assisted reception of Gaussian symbols over unknown inter-symbol interference channels whose parameters are also Gaussian. The proposed Bayesian network graph for the demodulator includes the channel parameters explicitly, allowing the combined estimation of the channel and information bearing symbols. The proposed algorithm is parallel in nature, is suitable for sparse channels, and is easily expandable to coded systems for joint channel pilot-assisted demodulation and decoding. The numerical results show performance better than the performance of iterative semi-blind equalization with batch minimum mean-squared error estimation iterations.

This paper considers the problem of joint detection in the uplink of cellular multiaccess networks with base-station cooperation. Distributed multiuser detection algorithms with local passing among neighbor base stations are proposed and compared in terms of computational complexity required in the base stations, the amount of serial communications among them, error rate performance, and convergence speed. The algorithms based on the belief propagation algorithm result in complexity and delay per base station which do not grow as the network size increases. In addition, it is observed that these algorithms have near single user error rate performance for the fading channels considered. Thus it is illustrated that using the belief propagation algorithm, it is possible to use non-orthogonal signaling and still achieve near single user performance with moderate computational complexity and a limited amount of message passing between base stations of adjacent cells.

Space-frequency (SF) group codes are designed for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. A rather general channel model is assumed, where the channel is frequency-selective Rayleigh fading with arbitrary power-delay profile. It is shown that the SF group code has a symmetric distance structure like the space-time group code, if the group consists of diagonal matrices. A scenario where the multiple codewords are loaded onto the subcarriers of the OFDM system in parallel is considered. The optimality condition on the choice of subcarrier allocation is found, and an optimal subcarrier-allocation scheme is proposed. A transmit scheme where rotated versions of the same signal are transmitted from different transmit antennas is proposed, and it is shown that it satisfies the optimality condition. Then matrix groups are designed which guarantee that the resulting SF codes are full rank. Numerical comparisons with recently published techniques in the literature verify our improved performance.

A delayed space-frequency cyclic group code is proposed in this paper, which applies a new bandwidth and power-efficient space-frequency processing on the orthogonal-frequency- division-multiplexing (OFDM) system. The delayed orthogonality with respect to frequency tones among different transmitter antennas guarantees to exploit the full spatial and frequency diversity of the frequency-selective multi-input multi-output (MIMO) channel. In contrast with the schemes which combine the single-antenna error correcting code with delayed transmitter diversity in multi-antenna OFDM systems, this space-frequency code has a robust performance in flat-fading channel. Without a tradeoff of the diversity gain, the proposed code can be easily expanded to achieve high data rate and generalized to more than two transmit antennas.

Distributed decoding in the uplink of a rectangular planar cellular array with local message passing is considered. Two algorithms are proposed and compared: a BCJR-type algorithm applied to linear subgraphs, and belief propagation applied to the 2D graph of the cellular array.

A high rate delayed space-frequency cyclic group code is proposed in this paper, which applies a new bandwidth and power-efficient space-frequency processing on the orthogonal-frequency-division-multiplexing (OFDM) system. The delayed orthogonality with respect to frequency tones among different transmitter antennas guarantees to exploit the full spatial and frequency diversity of the frequency-selective multi-input multi-output (MlMO) channel. In addition, after partitioning the subcarriers into groups and coding each group with the proposed block code, a maximized multiplexing gain can be obtained without any diversity loss. The proposed code can also be easily generalized to multiple transmitter antenna systems.

Semiblind channel estimation combines the methods of channel estimation based on a pilot signal and blind channel estimation based on a data-only conveying signal. Maximum-likelihood (ML)-based semiblind estimators with Gaussian assumptions can provide improvement in performance, compared with channel-estimation schemes using the pilot signal only. This improvement can be even larger when the pilot and the data signals are sent simultaneously, as is the case in the third-generation wideband code-division multiple-access standards. However, the Gaussian ML approach results in very large complexity. Previously proposed semiblind methods with low complexity have been derived for serial pilot and data transmission, and are not suitable for the parallel transmission case. In this paper, algorithms for semiblind channel estimation for the parallel data and training signal case are developed. Approximations which reduce the computational complexity of the Gaussian ML method significantly are proposed. Solutions with iterations with very low attendant complexity are provided. The mean squared error analysis of the proposed method is obtained and compared with that of a method with no approximations. The approximations are justified through simulations, and the performance improvement over estimation schemes using the pilot signal solely is verified.

Blind equalization and decoding of unitary space-time codes is considered for inter-symbol interference (ISI) channels. Previously, an adaptive equalization method that can equalize channel blindly was proposed, by exploiting the structure of the codes proposed by Hochwald et al. In this paper, the performance of the decoder followed by this equalizer is investigated. After giving a modified version of the algorithm in order to avoid undesired convergence, a generalized likelihood ratio test based non-coherent decoder is presented. Semi-analytic methods are developed to investigate the performance of non-coherent decoder followed by zero-forcing and minimum-mean squared-error equalizers. Simulation results indicate that the performance of the algorithm is in between ZF and MMSE equalizers.

Single-user channel estimation in multiuser DS-CDMA systems for the case of sparse channels with large delay spreads is addressed. In addition, practical pulse shapes are considered. In sparse channels, the efficient way to estimate the parameters is to estimate the continuous delays of each path, instead of using the typical discrete tapped delay-line model. Due to the facts that the desired delays are not drawn from a simple finite set and that band-limited pulse shapes are employed, the resulting methods require numerical optimization techniques. To facilitate estimation, it is proposed to optimize the spreading code employed during the training, or estimation, phase. The optimal single-path spreading code is derived and extended for estimation in the multipath scenario. Both single-path and multipath channel estimation are considered. The proposed algorithms are evaluated through simulation and via the determination of the Cramer-Rao lower bound on the estimation variance. Analytical approximations of key performance measures are also derived and are seen to be tight for a variety of scenarios.

Maximum-likelihood (ML) based semi-blind estimators with Gaussian assumptions can provide improvement in performance compared to channel estimation schemes using the pilot signal only. In the third generation UTRA-FDD standard context this improvement can be even larger due to fact that the pilot and the data signals are sent simultaneously. However, the Gaussian ML approach results in very large complexity. In this paper, assumptions which reduce the computational complexity of the method significantly are proposed, without causing performance degradation. The assumptions are justified through simulations and the performance improvement over estimation schemes using pilot signal solely is verified.

Decentralized single user multipath channel estimation for the downlink direct-sequence code-division multiple-access (DS-CDMA) is addressed. Sparse channels and bandwidth efficient signaling are considered. Under the assumption of sparse channels, it is more efficient to estimate the continuous path delays, and associated complex coefficients, rather than adopting the typical equally spaced tapped delay line model, for which the number of parameters to be estimated can be much larger. On the other hand, estimation of the continuous delays is rather challenging for bandlimited pulse shapes. A pilot aided scheme for the estimation of the continuous delays is proposed, which reduced the interval over which the delay is searched. Performance improvement can be achieved by exploiting the fact that all users share the same channel: however no knowledge of the interfering users is assumed. Finally, for the single path channel, a closed form solution is obtained. The estimation algorithms for evaluated through simulations.

Direct-sequence code-division multiple access is emerging as a potential multiple-access communication scheme for future digital wireless communications systems. Such wide-band systems usually operate in a frequency-selective fading channel that introduces intersymbol interference and thus potential performance degradation. Previously proposed subspace-based blind channel identification algorithms, which provide estimates of channel parameters for effective equalization, suffer from high numerical complexity for systems with large spreading gains. In this paper, it is shown that, through the use of matched filter outputs, reduction in numerical complexity can be obtained. The complexity reduction is considerable when the channel length is small and the system is moderately loaded. The results show that the new algorithm suffers a slight performance loss. Although the employed matched filter outputs do not form a set of sufficient statistics for the unknown channels, the difference between the matched filter outputs and the sufficient statistics becomes negligible for large observation lengths and the asymptotic normalized Fisher information does not change. Performance is evaluated through simulations, the derivation of a tight approximation of the mean-squared channel estimation error, and through comparisons to the Cramer-Rao bound for the estimation error variance. It is shown that the approximation of the mean-squared error can be obtained in terms of the correlation of the spreading codes and the channels. This representation of the error supplies a tool for investigating the relationship between performance and spreading sequence correlations.

Blind identification of digital communication channels has attracted a significant amount of attention. In this work, subspace-based methods are considered specifically for direct-sequence/code-division multiple-access (DS/CDMA) signals in an asynchronous, multipath, multi-user environment. The objective is to reduce the complexity of previously proposed methods by exploiting the properties of DS/CDMA waveforms. In particular, knowledge of the DS/CDMA spreading codes is employed, via matched filtering, to reduce the dimensionality. The resulting algorithm shows negligible difference in performance with respect to a previously proposed algorithm by Torlak and Xu (see IEEE Trans. on Signal Processing, vol.45, no.1, p.137-47, 1997). However, the reduction in complexity can be significant. The Cramer-Rao lower bounds on the estimation variance are determined and provide further justification for the matched filtering scheme.