Design guidelines for spatial modulation

A new class of low-complexity, yet energyefficient Multiple-Input Multiple-Output (MIMO) transmission techniques, namely the family of Spatial Modulation (SM) aided MIMOs (SM-MIMO) has emerged. These systems are capable of exploiting the spatial dimensions (i.e. the antenna indices) as an additional dimension invoked for transmitting information, apart from the traditional Amplitude and Phase Modulation (APM). SM is capable of efficiently operating in diverse MIMO configurations in the context of future communication systems. It constitutes a promising transmission candidate for large-scale MIMO design and for the indoor optical wireless communication whilst relying on a single-Radio Frequency (RF) chain. Moreover, SM may also be viewed as an entirely new hybrid modulation scheme, which is still in its infancy. This paper aims for providing a general survey of the SM design framework as well as of its intrinsic limits. In particular, we focus our attention on the associated transceiver design, on spatial constellation optimization, on link adaptation techniques, on distributed/ cooperative protocol design issues, and on their meritorious variants

Near-Instantaneously Adaptive HSDPA-Style OFDM Versus MC-CDMA Transceivers for WIFI, WIMAX, and Next-Generation Cellular Systems

Burts-by-burst (BbB) adaptive high-speed downlink packet access (HSDPA) style multicarrier systems are reviewed, identifying their most critical design aspects. These systems exhibit numerous attractive features, rendering them eminently eligible for employment in next-generation wireless systems. It is argued that BbB-adaptive or symbol-by-symbol adaptive orthogonal frequency division multiplex (OFDM) modems counteract the near instantaneous channel quality variations and hence attain an increased throughput or robustness in comparison to their fixed-mode counterparts. Although they act quite differently, various diversity techniques, such as Rake receivers and space-time block coding (STBC) are also capable of mitigating the channel quality variations in their effort to reduce the bit error ratio (BER), provided that the individual antenna elements experience independent fading. By contrast, in the presence of correlated fading imposed by shadowing or time-variant multiuser interference, the benefits of space-time coding erode and it is unrealistic to expect that a fixed-mode space-time coded system remains capable of maintaining a near-constant BER

Hybrid Iterative Multiuser Detection for Channel Coded Space Division Multiple Access OFDM Systems

Space division multiple access (SDMA) aided orthogonal frequency division multiplexing (OFDM) systems assisted by efficient multiuser detection (MUD) techniques have recently attracted intensive research interests. The maximum likelihood detection (MLD) arrangement was found to attain the best performance, although this was achieved at the cost of a computational complexity, which increases exponentially both with the number of users and with the number of bits per symbol transmitted by higher order modulation schemes. By contrast, the minimum mean-square error (MMSE) SDMA-MUD exhibits a lower complexity at the cost of a performance loss. Forward error correction (FEC) schemes such as, for example, turbo trellis coded modulation (TTCM), may be efficiently combined with SDMA-OFDM systems for the sake of improving the achievable performance. Genetic algorithm (GA) based multiuser detection techniques have been shown to provide a good performance in MUD-aided code division multiple access (CDMA) systems. In this contribution, a GA-aided MMSE MUD is proposed for employment in a TTCM assisted SDMA-OFDM system, which is capable of achieving a similar performance to that attained by its optimum MLD-aided counterpart at a significantly lower complexity, especially at high user loads. Moreover, when the proposed biased Q-function based mutation (BQM) assisted iterative GA (IGA) MUD is employed, the GA-aided system’s performance can be further improved, for example, by reducing the bit error ratio (BER) measured at 3 dB by about five orders of magnitude in comparison to the TTCM assisted MMSE-SDMA-OFDM benchmarker system, while still maintaining modest complexity

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

Slow subcarrier-hopped Space Division Multiple Access OFDM systems

Recently Space Division Multiple Access (SDMA) assisted Multi-Input-Multi-Output (MIMO) OFDM systems invoking Multi-User Detection (MUD) techniques have attracted substantial research interests, which are capable of exploiting both transmitter multiplexing gain and receiver diversity gain. Furthermore, the classic Frequency-Hopping (FH) technique can be effectively amalgamated with SDMA-OFDM systems, resulting in Frequency-Hopped (FH) SDMA-OFDM. In this paper we devise a Turbo Trellis Coded Modulation (TTCM) assisted subcarrier-based FH/SDMA-OFDM scheme, which may be able to fully exploit the attainable frequency diversity, while exhibiting a high Multi-User-Interference (MUI) resistance. In the high-throughput scenario investigated, the proposed Uniform Slow-SubCarrier-Hopped (USSCH) SDMA-OFDM system was capable of achieving 6dB Eb=N0 gain at the BER of 10¡4 over the conventional SDMA-OFDM system, while maintaining a similar complexity

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Deep Learning Based Channel Estimation in Data Driven MIMO Receiver

OFDM (orthogonal frequency division multiplexing) is a wireless network methodology that sends multiple data streams across a particular channel while effectiently handling inter-symbol interference and enhancing frequency band available. And since the antenna is sending signals, evaluating the noise in a noisy channel is essential. This research aims into compressed sensing (CS) as a way to improve throughput and BER performance by transmitting additional data bits within every subcarrier frame whilst still limiting detector unpredictability. The Neuro-LS methodology is used in this study to generate a soft trellis decoding algorithm through channel estimation. Trellis decoding performs better BER, and DNN relying channel estimation outperforms BER, according to the findings

Decision Directed Channel Estimation Aided OFDM Employing Sample-Spaced and Fractionally-Spaced CIR Estimators

Abstract—In this letter we characterize the substantial difference between two channel estimation approaches, namely the sample-spaced (SS) and the fractionally-spaced (FS) channel impulse response (CIR) estimators. The achievable performance of decision-directed channel estimation (DDCE) methods employing both the SS- and the FS-CIR estimators is analyzed in the context of an OFDM system. The performance of the two estimation methods is compared and it is shown that the DDCE scheme employing the Projection Approximation Subspace Tracking (PAST)-aided FS-CIR estimator outperforms its SS-CIR estimator-based counterpart. Index Terms—Multiuser OFDM, decision directed channel estimation, impulse response estimation SDMA

Polar-Coded OFDM with Index Modulation

Polar codes, as the first error-correcting codes with an explicit construction to provably achieve thesymmetric capacity of memoryless channels, which are constructed based on channel polarization, have recently become a primary contender in communication networks for achieving tighter requirements with relatively low complexity. As one of the contributions in this thesis, three modified polar decoding schemes are proposed. These schemes include enhanced versions of successive cancellation-flip (SC-F), belief propagation (BP), and sphere decoding (SD). The proposed SC-F utilizes novel potential incorrect bits selection criteria and stack to improve its error correction performance. Next, to make the decoding performance of BP better, permutation and feedback structure are utilized. Then, in order to reduce the complexity without compromising performance, a SD by using novel decoding strategies according to modified path metric (PM) and radius extension is proposed. Additionally, to solve the problem that BP has redundant iterations, a new stopping criterion based on bit different ratio (BDR) is proposed. According to the simulation results and mathematical proof, all proposed schemes can achieve corresponding performance improvement or complexity reduction compared with existing works. Beside applying polar coding, to achieve a reliable and flexible transmission in a wireless communication system, a modified version of orthogonal frequency division multiplexing (OFDM) modulation based on index modulation, called OFDM-in-phase/quadrature-IM (OFDM-I/Q-IM), is applied. This modulation scheme can simultaneously improve spectral efficiency and bit-error rate (BER) performance with great flexibility in design and implementation. Hence, OFDM-I/Q-IM is considered as a potential candidate in the new generation of cellular networks. As the main contribution in this work, a polar-coded OFDM-I/Q-IM system is proposed. The general design guidelines for overcoming the difficulties associated with the application of polar codes in OFDM-I/Q-IM are presented. In the proposed system, at the transmitter, we employ a random frozen bits appending scheme which not only makes the polar code compatible with OFDM-I/Q-IM but also improves the BER performance of the system. Furthermore, at the receiver, it is shown that the \textit{a posteriori} information for each index provided by the index detector is essential for the iterative decoding of polar codes by the BP algorithm. Simulation results show that the proposed polar-coded OFDM-I/Q-IM system outperforms its OFDM counterpart in terms of BER performance