This overview portrays the 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 in multiuser 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.

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Multiuser MIMO-OFDM for Next-Generation Wireless Systems

We consider the uplink of a multiuser system where the transmitters as well as the receiver are equipped with multiple antennas. Each user multiplexes its symbols by a linear precoder through its transmit antennas. We work with the system-wide mean squared error as the performance measure and propose algorithms to find the jointly optimum linear precoders at each transmitter and linear decoders at the receiver. We first work with the case where the number of symbols to be transmitted by each user is given. We then investigate how the symbol rate should be chosen for each user with optimum transmitters and receivers. The convergence analysis of the algorithms is given, and numerical evidence that supports the analysis is presented.

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Transceiver optimization for multiuser MIMO systems

As the growing demand for mobile communications is constantly increasing, the need for better coverage, improved capacity, and higher transmission quality rises. Thus, a more efficient use of the radio spectrum is required. Smart antenna systems are capable of efficiently utilizing the radio spectrum and is a promise for an effective solution to the present wireless systems problems while achieving reliable and robust high-speed high-data-rate transmission. The purpose of this book is to provide the reader a broad view of the system aspects of smart antennas. In fact, smart antenna systems comprise several critical areas such as individual antenna array design, signal processing algorithms, space-time processing, wireless channel modeling and coding, and network performance. In this book we include an overview of smart antenna concepts, introduce some of the areas that impact smart antennas, and examine the influence of interaction and integration of these areas to Mobile Ad-Hoc Networks. In addition, the general principles and major benefits of using space-time processing are introduced, especially employing multiple-input multiple-output (MIMO) techniques.

Introduction to Smart Antennas

An adaptive medium-access control (MAC) protocol for heterogeneous networks with finite population is proposed. Referred to as the multiqueue service room (MQSR) protocol, this scheme is capable of handling users with different quality-of-service (QoS) constraints. By exploiting the multipacket reception (MPR) capability, the MQSR protocol adaptively grants access to the MPR channel to a number of users such that the expected number of successfully received packets is maximized in each slot. The optimal access protocol avoids unnecessary empty slots for light traffic and excessive collisions for heavy traffic. It has superior throughput and delay performance as compared to, for example, the slotted ALOHA with the optimal retransmission probability. This protocol can be applied to random-access networks with multimedia traffic.

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A multiqueue service room MAC protocol for wireless networks with multipacket reception

A dynamic medium access control (MAC) protocol is proposed for a finite-user slotted channel with multipacket reception (MPR). This protocol divides the time axis into transmission periods (TPs) where the ith TP is dedicated to the transmission of the packets generated in the (i-1)th TP. At the beginning of each TP, the state (active or idle) of each user is estimated based on the length of the previous TP and the incoming traffic load. By exploiting the information on the state of users and the channel MPR capability, the number of users who can simultaneously access the channel in the current TP is chosen so that the expected length of this TP is minimized. As a result, the MPR capability is more efficiently utilized by the proposed protocol as compared to, for example, the slotted ALOHA with optimal retransmission probability. Furthermore, the proposed protocol requires little online computation. Its simplicity is comparable to that of slotted ALOHA. It can be applied to random access networks with spread spectrum and/or antenna array.

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A dynamic queue protocol for multiaccess wireless networks with multipacket reception

We introduce and study the use of dynamic slot allocation (DSA) in packet-switched space-division-multiple-access (SDMA) systems. In conventional SDMA, a smart antenna is used at the basestation to simultaneously communicate with multiple stations on the same frequency channel. When dynamic slot allocation is added, the basestation uses uplink channel measurements to intelligently construct future SDMA/TDMA frames. It is shown that under a simple minimum signal-to-interference-plus-noise ratio (SINR) constraint, the problem of performing optimal dynamic slot allocation is NP-complete. Heuristic slot allocation algorithms are introduced which are capable of greatly increasing SDMA/TDMA frame capacity compared with a random allocation of stations. The paper uses both theoretical results and measured data from an experimental testbed to characterize the performance of dynamic slot allocation. The experimental system operates at a carrier frequency of 1.86 GHz and uses an eight-element circular antenna array. It is demonstrated that significant increases in system capacity are possible using DSA in the indoor situations that were tested. Dynamic slot allocation requires the channel to be essentially constant from the time that channel measurements are made until the SDMA/TDMA frame is transmitted. We also present channel measurements which show the effects of channel time coherence in the presence of indoor pedestrian movement. This and other results we have taken suggest that dynamic slot allocation is possible at the frequency considered, provided turnaround times are in the low-to-mid tens of milliseconds.

John Litva

Papers

Dynamic slot allocation (DSA) in indoor SDMA/TDMA using a smart antenna basestation