In this paper, we address the problem of signal detection in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system by using auto-encoder (AE) network and extreme learning machine (ELM). The existing signal detection algorithms, such as zero-forcing successive-interference-cancellation (ZF-SIC), minimum-mean-square-error successive-interference-cancellation (MMSE-SIC), maximum likelihood detection (MLD) and quantum-genetic radial-basis-function (QGA-RBF) etc., haven't considered the characteristics invariance of signals in the process of transmission. Combined AE network with ELM, a novel signal detection scheme for MIMO-OFDM system is proposed. The proposed algorithm can obtain the features of received signals effectively through AE and recognize the corresponding original signals quickly via ELM. Moreover, the channel matrix is not required in the process of signal detection. We have derived a theoretically model and analyze the feasibility of feature extraction in received signals, and simulations are also carried out to evaluate the performance and compare that with some traditional and state-of-the-art algorithms. The simulation results confirm that the performance of the proposed scheme outperforms that of many detection schemes such as zero-forcing (ZF), ZF-SIC, minimum-mean-square-error (MMSE), MMSE-SIC, and reaches the similar bit-error-rate (BER) performance of MLD and QGA-RBF with much lower complexity.

Signal detection scheme is the key technology to the implementation of multiple-input multiple-output (MIMO) wireless communication system, while the spatial-multiplexing coded MIMO systems cause a severe design challenge for signal detection algorithms. Although many researches focus on searching the solution space for optimal solution based on more efficient searching algorithm, the signal detection of MIMO system does not regarded as a classification problem. In this paper, the detection problem is considered as a feature classification, and a novel signal detection scheme of MIMO system based on extreme learning machine auto encoder (ELM-AE) is proposed. The proposed algorithm can efficiently extract the features of input data by ELM-AE and classify these representations to corresponding groups rapidly by using extreme learning machine (ELM). This paper has constructed a theoretical model of the proposed signal detector for MIMO system and carried out simulations to evaluating its performance. Simulation results indicate that the proposed detector outperforms many traditional schemes and state-of-the-art algorithms.

The rapidly growing demands for bandwidth-intensive mobile broadband services have triggered tremendous efforts to develop the long-term-evolution (LTE)-Advanced and beyond cellular networks, which are widely deemed as a major advancement of the existing LTE networks. Constrained by limited spectral resources, wireless communication researchers and engineers have proposed a number of technologies to efficiently utilize spectral resources in multiple dimensions. Specifically, with a large number of transmit antennas deployed at a base station, massive multiple-input multiple- output (MIMO) (also called full-dimension MIMO [FD-MIMO] in the Third Generation Partnership Project [3GPP] community) is expected to achieve enormous spectral efficiency by exploiting degrees of freedom in the spatial domain. Recent theoretical and experimental findings highlight that massive MIMO is a promising solution to improve data rates , link reliability, and power savings for cellular networks. Another attractive technology, small cells, has also shown great potential for enhancing system throughputs of LTE-Advanced and beyond cellular networks. In the small cell solution, lower-cost base stations are densely deployed, and the size of a cell is substantially reduced, thus enabling great improvement of frequency reuse ratios in the geographical domain and significant enhancement of the spectral efficiency of cellular networks. Also, other technologies such as deviceto-device (D2D) and cognitive radio have also emerged as promising solutions to boost the spectral efficiency of cellular networks in various scenarios.

Full-dimension multiple-input multiple-output (FD-MIMO) systems, in which base stations are equipped with a large number of antennas in a two-dimensional panel, has received considerable attention from academia researchers and industry practitioners. Compared with legacy cellular communication systems, FD-MIMO systems can achieve significantly higher spectral efficiency with high order multi-user MIMO (MU-MIMO) transmissions. However, as high-order MU-MIMO also incurs high precoding and scheduling complexity, it is critical to reduce complexity of these operations in order to realize throughput potential of FD-MIMO systems in practice. In this paper, we propose a reduced complexity algorithm to realize the high performance precoding technique, signal-to-leakage plus noise ratio (SLNR) precoding, and propose an efficient scheduling algorithm to enable high-order MU-MIMO transmissions in FD-MIMO systems. We further demonstrate the effectiveness of the proposed algorithms by using system level simulations.

In this paper, we propose a sequential spectrum sensing algorithm for cognitive radio systems, which we term the sequential shifted chi-square test (SSCT). SSCT has the following attractive features for practical implementations. First, SSCT employs a simple test statistic and thus has a low implementation complexity. Secondly, SSCT is a sequential detection algorithm and is capable of achieving p...

While conventional cognitive radio (CR) system is striving at providing best possible protections for the usage of primary users (PU), little attention has been given to ensure the quality of service (QoS) of applications of secondary users (SU). When loading real-time applications over such a CR system, we have found that existing spectrum sensing schemes create a major hurdle for real-time traffic delivery of SU. For example, energy detection based sensing, a widely used technique, requires possibly more than 100 ms to detect a PU with weak signals. The delay is intolerable for real-time applications with stringent QoS requirements, such as voice over internet protocol (VoIP) or live video chat. This delay, along with other delays caused by backup channel searching, channel switching, and possible buffer overflow due to the insertion of sensing periods, makes supporting real-time applications over CR system very difficult if not impossible. In this paper, we present the design and implementation of a sensing-based CR system - RECOG, which is able to support realtime communications among SUs. We first redesign the conventional sensing scheme. Without increasing the complexity or trading off the detection performance, we break down a long sensing period into a series of shorter blocks, turning a disruptive long delay into negligible short delays. To enhance the sensing capability as well as better protect the QoS of SU traffic, we also incorporate an on-demand sensing scheme based on MAC layer information. In addition, to ensure a fast and reliable switching when PU returns, we integrate an efficient backup channel scanning and searching component in our system. Finally, to overcome a potential buffer overflow, we propose a CR-aware QoS manager. Our extensive experimental evaluations validate that RECOG can not only support realtime traffic among SUs with high quality, but also improve protections for PUs.

We propose a robust close-to-capacity dirty-paper coding (DPC) design framework in which multi-level low density parity check (LDPC) codes and trellis coded quantization (TCQ) are employed as the channel and source coding components, respectively. The proposed design framework is robust in the sense that it yields close to capacity solutions in the high-, medium-, and low-rate regimes. This is in contrast to existing practical DPC schemes that perform well only in one or two of these regimes, but not all three. We design codes for transmission rates of 0.5, 1.0, 1.5, and 2.0 bits/sample (b/s) using one, two, three, and four LDPC levels; at a block length of 2×10 5 , the codes perform 0.95, 0.58, 0.55, and 0.54 dB from the corresponding information theoretic limits, respectively. We also propose a low-complexity decoding scheme that does not involve iterative message passing between the source and channel decoders; the low-complexity scheme performs only 1.08, 0.85, and 0.79 dB away from the theoretical limits at transmission rates of 1.0, 1.5, and 2.0 b/s, respectively.

This paper considers the problem of how to quickly and accurately determine the availability of each spectrum band for a multi-band primary system using one or few sensors. Such problem is referred to as spectrum scanning. Two cases of practical interest are studied: 1) a single sensor case in which only one spectrum band is observed at one time; and 2) a multiple sensor case in which multiple spectrum bands are observed simultaneously. For each case, scenarios with and without a scanning delay constraint are investigated. Using mathematical tools from optimal stopping theory, optimal spectrum scanning algorithms are developed to minimize a cost function that strikes a desirable trade-off between detection performance and sensing delay. In the non delay-constrained case, it is shown that the optimal scanning algorithm is a concatenated sequential probability ratio test (C-SPRT). In the delay-constrained case, the optimal scanning algorithm has a high implementation complexity and truncation algorithms are developed as alternative low complexity options. Numerical examples are provided to illustrate the effectiveness of the proposed algorithms.

ABSTRACTThis paper considers three‐node discrete memoryless relay channels with generalised feedback. In particular, two generalised feedback configurations are investigated. In the first configuration, the source is assumed to be able to actively collect feedback signals from the channel, whereas in the second one, the destination is assumed to be able to actively transmit feedback signals to the relay. For both configurations, new coding schemes that are based on the notions of decode and forward and compress and forward are developed to exploit the feedback, and corresponding achievable rates are derived. For the first configuration, the proposed coding schemes exploit the feedback by allowing the source to cooperate with the relay in the process of forwarding the compressed version of the channel output sequence at the relay to the destination. For the second configuration, the proposed coding schemes allow the destination to perform compress and forward to enhance the decoding power at the relay which applies partial decode and forward, thereby improving the overall transmission rate. The derived achievable rates are also shown to generalise several existing results for corresponding settings with perfect feedback as special cases. Copyright © 2012 John Wiley & Sons, Ltd.

Owing to the special structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), the associated capacity region computation and beamforming optimization problems are typically non-convex, and thus cannot be solved directly. One feasible approach is to consider the respective dual multiple-access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality has been established via BC-MAC signal transformation, and is applicable only for the case in which the MIMO BC is subject to a single transmit sum-power constraint. An alternative approach is based on minimax duality, which can be applied to the case of the sum-power constraint or per-antenna power constraint. In this paper, the conventional BC-MAC duality is extended to the general linear transmit covariance constraint (LTCC) case, which includes sum-power and per-antenna power constraints as special cases. The obtained general BC-MAC duality is applied to solve the capacity region computation for the MIMO BC and beamforming optimization for the multiple-input single-output (MISO) BC, respectively, with multiple LTCCs. The relationship between this new general BC-MAC duality and the minimax duality is also discussed, and it is shown that the general BC-MAC duality leads to simpler problem formulations. Moreover, the general BC-MAC duality is extended to deal with the case of nonlinear transmit covariance constraints in the MIMO BC.

This paper investigates the issue of how to balance the tradeoff between sensing performance and sensing costs/rewards for cooperative sensing in a multichannel cognitive radio system. Two cases of practical interest are studied. In the first case, the number of available sensors is assumed to be sufficient for detecting available frequency channels. For this case, we study the problem of selecting appropriate sensors to minimize the cost of detecting all the available channels subject to sensing performance constraints. The problem can be solved by using a branch-and-bound algorithm. In the second case, the number of available sensors is assumed to be insufficient for detecting all the available channels. For this case, we study the problem of selecting appropriate channels to maximize the sensing rewards subject to sensing performance constraints. Since the computational complexity of solving this problem optimally is fairly high, we propose a greedy algorithm as a low complexity solution to the problem. We further validate the effectiveness of the proposed algorithm via Monte-Carlo simulations.

Recently, it has been shown that in comparison to the well-known energy detection scheme, the sequential shifted chi-square test (SSCT) is capable of delivering considerable reduction on the average sample number (ASN) while maintaining a comparable detection error performance for spectrum sensing.Nonetheless, SSCT needs to perform threshold comparisons on every received sample, which may be difficult or even infeasible in practice particularly when the sampling rate is high and/or the signal-to-noise ratio is low.This paper proposes an extension of SSCT, called block-wise SSCT (B-SSCT), to overcome this shortcoming.Numerical algorithms are applied to evaluate the false-alarm and miss-detection probabilities and the ASN of B-SSCT, in a recursive fashion.Simulation and numerical results show that compared to the original SSCT, B-SSCT is capable of achieving almost the same detection error performance with a significantly reduced number of threshold comparisons and a slightly increased ASN.An implementation example demonstrates potential practical feasibility of B-SSCT in a real environment.

We propose a close-to-capacity dirty-paper coding framework which employs multi-level low density parity-check (LDPC) and trellis coded quantization. The proposed coding framework is robust in the sense that it performs close to capacity in the high as well as the low rate regimes. This is in contrast to existing practical DPC schemes which perform well at one of these regimes, but never both. In order to evaluate the performance of our scheme, we consider its application to a cognitive radio channel. At a block length of 2 × 105, the designed dirty-paper coding scheme operates within 0.95, 0.58 and 0.6 dB of the theoretical limit at transmission rates of 0.5, 1.0 and 1.5 bits/sample, respectively. As far as the authors are aware, this is the best performance reported in the literature so far.