This paper proposes per-tone equalization methods for single carrier block transmission with cyclic prefix (SC-CP) systems. Minimum mean-square-error (MMSE) based optimum weights of the per-tone equalizers are derived for SISO (single-input single-output), SIMO (single-input multiple-output), and MIMO (multiple-input multiple-output) SC-CP systems. Unlike conventional frequency domain equalization methods, where discrete Fourier transform (DFT) is employed, the per-tone equalizers utilize sliding DFT, which makes it possible to achieve good performance even when the length of the guard interval is shorter than the channel order. Computer simulation results show that the proposed equalizers can significantly improve the bit error rate (BER) performance of the SISO, SIMO, and MIMO SC-CP systems with the insufficient guard interval.

Many research efforts are being focused upon the design of dynamic Inter-Cell Interference Coordination (ICIC) schemes for macrocell/picocell heterogeneous networks employing Cell Range Expansion (CRE). In order to protect the expanded Pico User Equipments (ePUEs) located in the CRE region from severe Macro Base Station (MBS) interference in downlink, the conventional methods reduce the transmit power of the MBS in the Almost Blank Subframes (ABSs), where ePUEs can be scheduled. However, this severely limits the amount of usable resources/power for the MBS as compared to Resource Block (RB)-based dynamic allocation. Instead, we propose a self-organized RB-based dynamic resource allocation method. Based on the proposed partial Channel State Information (CSI) sharing, the MBS obtains ePUEs' CSI and predicts their RB allocation. Then, the MBS reduces its transmit power in RBs where the ePUEs' allocation probability is estimated to be high. The simulation results show that the proposed scheme achieves excellent macrocell/picocell performance trade-offs, even when taking into account the overhead increase due to the partial CSI sharing.

In remote control, efficient compression or representation of control signals is essential to send them through rate-limited channels. For this purpose, we propose an approach of sparse control signal representation using the compressive sampling technique. The problem of obtaining sparse representation is formulated by cardinality-constrained 2 optimization of the control performance, which is reducible to 1-2 optimization. The low rate random sampling employed in the proposed method based on the compressive sampling, in addition to the fact that the 1-2 optimization can be effectively solved by a fast iteration method, enables us to generate the sparse control signal with reduced computational complexity, which is preferable in remote control systems where computation delays seriously degrade the performance. We give a theoretical result for control performance analysis based on the notion of restricted isometry property (RIP). An example is shown to illustrate the effectiveness of the proposed approach via numerical experiments.

Recently, the marginalized particle filter (MPF) has been applied to blind symbol detection problems over selective fading channels. The MPF can ease the computational burden of the standard particle filter (PF) while offering better estimates compared with the standard PF. In this paper, we investigate the application of the blind MPF detector to more realistic situations where the systems suffer from analog imperfections which are non-linear signal distortion due to the inaccurate analog circuits in wireless devices. By reformulating the system model using the widely linear representation and employing the auxiliary variable resampling (AVR) technique for estimation of the imperfections, the blind MPF detector is successfully modified to cope with the analog imperfections. The effectiveness of the proposed MPF detector is demonstrated via computer simulations.

In this paper, we propose to adapt both the modulation scheme and the transmit power in the frequency domain using a heuristic evaluation of the bit error rate (BER) for each subcarrier. The proposed method consists in ordering in terms of fading impact, grouping a certain number of subcarriers and performing local power adaptation in each subcarrier group. The subcarrier grouping is performed in order to equalize the average channel condition of each subcarrier group. Grouping and local power adaptation allow us to take advantage of the channel variations and to reduce the computational complexity of the proposed power distribution scheme, while avoiding the performance degradation due to the suboptimum power adaptation as much as possible. Compared to the conventional power distribution methods, the proposed scheme does not require any iterative process and the power adaptation is directly performed using an analytical formula. Simulations show a gain in terms of BER performance compared to equal power distribution and existing algorithms for power distribution. In addition, due to the subcarrier group specificity, the trade-off between the computational complexity and the performance can be controlled by adjusting the size of the subcarrier groups. Simulation results show significant improvement of BER performance compared to equal power allocation.

We consider Downlink (DL) scheduling for a multi-user cooperative cellular system with fixed relays. The conventional scheduling trend is to avoid interference by allocating orthogonal radio resources to each user, although simultaneous allocation of users on the same resource has been proven to be superior in, e.g., the broadcast channel. Therefore, we design a scheduler where in each frame, two selected relayed users are supported simultaneously through the Superposition Coding (SC) based scheme proposed in this paper. In this scheme, the messages destined to the two users are superposed in the modulation domain into three SC layers, allowing them to benefit from their high quality relayed links, thereby increasing the sum-rate. We derive the optimal power allocation over these three layers that maximizes the sum-rate under an equal rates' constraint. By integrating this scheme into the proposed scheduler, the simulation results show that our proposed SC scheduler provides high throughput and rate outage probability performance, indicating a significant fairness improvement. This validates the approach of simultaneous allocation versus orthogonal allocation in the cooperative cellular system.

Recent advances in cooperative communication and wireless Network Coding (NC) may lead to huge performance gains in relay systems. In this context, we focus on the two-way relay scenario, where two nodes exchange information via a common relay. We design a practical Superposition Coding (SC) based NC scheme for Decode-and-Forward (DF) half-duplex relaying, where the goal is to increase the achievable rate. By taking advantage of the direct link and by providing a suboptimal yet efficient power division among the superposed layers, our proposed SC two-way relaying scheme outperforms the reference schemes, including the well-known 3-step DF-NC scheme and the capacity of 2-step schemes for a large set of SNRs, while approaching closely the performance bound.

In this paper, we propose an overloaded multiple-input multiple-output (MIMO) signal detection scheme with slab decoding and lattice reduction (LR). The proposed scheme firstly splits the transmitted signal vector into two parts, the post-voting vector composed of the same number of signal elements as that of receive antennas, and the pre-voting vector composed of the remaining elements. Secondly, it reduces the candidates of the pre-voting vector using slab decoding and determines the post-voting vectors for each pre-voting vector candidate by LR-aided minimum mean square error (MMSE)-successive interference cancellation (SIC) detection. From the performance analysis of the proposed scheme, we derive an upper bound of the error probability and show that it can achieve the full diversity order. Simulation results show that the proposed scheme can achieve almost the same performance as the optimal ML detection while reducing the required computational complexity.

Cardinality estimation schemes of Radio Frequency IDentification (RFID) tags using Framed Slotted ALOHA (FSA) based protocol are studied in this paper. Not as same as previous estimation schemes, we consider tag cardinality estimation problem under not only detection errors but also capture effect, where a tag's IDentity (ID) might not be detected even in a singleton slot, while it might be identified even in a collision slot due to the fading of wireless channels. Maximum Likelihood (ML) approach is utilized for the estimation of the detection error probability, the capture effect probability, and the tag cardinality. The performance of the proposed method is evaluated under different system parameters via computer simulations to show the method's effectiveness comparing to other conventional approaches.

In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the discreteness of the error signal. We firstly formulate the error recovery problem as the maximum a posteriori (MAP) estimation and then relax the MAP estimation into a convex optimization problem, which reconstructs a discrete-valued sparse vector from its linear measurements. By using the restricted isometry property (RIP), we also provide a theoretical upper bound of the size of the reconstruction error with the optimization problem. Simulation results show that the proposed error recovery method has better bit error rate (BER) performance than that of the conventional error recovery method.

This paper proposes low-complexity pre- and post-frequency domain equalization and frequency diversity combining methods for block transmission schemes with cyclic prefix. In the proposed methods, the equalization and diversity combining are performed simultaneously in discrete frequency domain. The weights for the proposed equalizer and combiner are derived based on zero-forcing and minimum-mean-square error criteria. We demonstrate the performance of the proposed methods, including bit-error rate performance and peak-to-average power ratios of the transmitted signal, via computer simulations.

Diffusion least-mean-square (LMS) is a method to estimate and track an unknown parameter at multiple nodes in a network. When the unknown vector has sparsity, the sparse promoting version of diffusion LMS, which utilizes a sparse regularization term in the cost function, is known to show better convergence performance than that of the original diffusion LMS. This paper proposes a novel choice of the coefficients involved in the updates of sparse diffusion LMS using the idea of message propagation. Moreover, we optimize the proposed coefficients with respect to mean-square-deviation at the steady-state. Simulation results demonstrate that the proposed method outperforms conventional methods in terms of the convergence performance.

This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control.