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

Cognitive Radio for Emergency Networks

In the scope of the Adaptive Ad-hoc Freeband (AAF) project, an emergency network built on top of Cognitive Radio is proposed to alleviate the spectrum shortage problem which is the major limitation for emergency networks. Cognitive Radio has been proposed as a promising technology to solve todayâ?~B??~D?s spectrum scarcity problem by allowing a secondary user in the non-used parts of the spectrum that aactully are assigned to primary services. Cognitive Radio has to work in different frequency bands and various wireless channels and supports multimedia services. A heterogenous reconfigurable System-on-Chip (SoC) architecture is proposed to enable the evolution from the traditional software defined radio to Cognitive Radio

Cross-Layer Optimization and Dynamic Spectrum Access for Distributed Wireless Networks

We proposed a novel spectrum allocation approach for distributed cognitive radio networks. Cognitive radio systems are capable of sensing the prevailing environmental conditions and automatically adapting its operating parameters in order to enhance system and network performance. Using this technology, our proposed approach optimizes each individual wireless device and its single-hop communication links using the partial operating parameter and environmental information from adjacent devices within the wireless network. Assuming stationary wireless nodes, all wireless communication links employ non-contiguous orthogonal frequency division multiplexing (NC-OFDM) in order to enable dynamic spectrum access (DSA). The proposed approach will attempt to simultaneously minimize the bit error rate, minimize out-of-band (OOB) interference, and maximize overall throughput using a multi-objective fitness function. Without loss in generality, genetic algorithms are employed to perform the actual optimization. Two generic optimization approaches, subcarrier-wise approach and block-wise approach, were proposed to access spectrum. We also proposed and analyzed several approaches implemented via genetic algorithms (GA), such as quantizing variables, using adaptive variable ranges, and Multi-Objective Genetic Algorithms, for increasing the speed and improving the results of combined spectrum utilization/cross-layer optimization approaches proposed, together with several assisting processes and modifications devised to make the optimization to improve efficiency and execution time

Dynamic Resource Allocation Algorithms for Cognitive Radio Systems

Cognitive Radio (CR) is a novel concept for improving the utilization of the radio spectrum. This promises the efficient use of scarce radio resources. Orthogonal Frequency Division Multiplexing (OFDM) is a reliable transmission scheme for Cognitive Radio Systems which provides flexibility in allocating the radio resources in dynamic environment. It also assures no mutual interference among the CR radio channels which are just adjacent to each other. Allocation of radio resources dynamically is a major challenge in cognitive radio systems. In this project, various algorithms for resource allocation in OFDM based CR systems have been studied. The algorithms attempt to maximize the total throughput of the CR system (secondary users) subject to the total power constraint of the CR system and tolerable interference from and to the licensed band (primary users). We have implemented two algorithms Particle Swarm Algorithm(PSO) and Genetic Algorithm(GA) and compared their results

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

Multiuser MIMO-OFDM Systems using Subcarrier Hopping

Recently space division multiple access (SDMA) assisted multiple-input–multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems invoking multiuser detection (MUD) techniques have attracted substantial research interest, which is capable of exploiting both transmitter multiplexing gain and receiver diversity gain. A new scheme referred to here as slowsubcarrierhopping (SSCH) assisted multiuser SDMA-OFDM, is proposed. It is shown that, with the aid of the so-called uniform SSCH (USSCH) pattern, the multiuser interference (MUI) experienced by the high-throughput SDMA-OFDM system can be effectively suppressed, resulting in a significant performance improvement. In the investigations conducted, the proposed USSCH-aided SDMA-OFDM system was capable of outperforming a range of SDMA-OFDM systems considered, including the conventional SDMA-OFDM system dispensing with the employment of frequency-hopping techniques. For example, at an Eb/N0 value of 12 dB, the proposed USSCH/SDMA-OFDM system reduced the bit error ratio (BER) by about three orders of magnitude, in comparison to the conventional SDMA-OFDM system, while maintaining a similar computational complexity

Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose

Sidelobe Suppression and Agile Transmission Techniques for Multicarrier-based Cognitive Radio Systems

With the advent of new high data rate wireless applications, as well as growth of existing wireless services, demand for additional bandwidth is rapidly increasing. Existing spectrum allocation policies of the Federal Communications Commission (FCC) prohibits unlicensed access to licensed spectrum, constraining them instead to several heavily populated, interference-prone frequency bands, which causes spectrum scarcity. However, it has been shown by several spectrum measurement campaigns that the current licensed spectrum usage across time and frequency is inefficient. Therefore, a concept of unlicensed users temporarily ``borrowing spectrum from incumbent license holders to improve the spectrum utilization, called ``spectrum pooling , which is based on dynamic spectrum access (DSA), is proposed. Cognitive radio is a communication paradigm that employs software-defined radio technology in order to perform DSA and offers versatile, powerful and portable wireless transceivers. Orthogonal frequency division multiplexing (OFDM) is a promising candidate for cognitive radio transmission. OFDM supports high data rates that are robust to channel impairments. In addition, some subcarriers can be deactivated which constitutes a non-contiguous OFDM (NC-OFDM) transmission. However, one of the biggest problems for OFDM transmission is high out-of-band (OOB) radiation, which is caused by sinc-type function representing the symbols during one time constant. Thus, high sidelobe may occur that will interfere with neighboring transmissions. This thesis presents two novel techniques for NC-OFDM sidelobe suppression. Another concern about cognitive radio systems is that the influence of frequency-selective fading channel. Consequently, this thesis also presents a combined approach employing power loading, bit allocation and sidelobe suppression for OFDM-based cognitive radio systems optimization