ADAPTIVE MODULATION & ADAPTIVE BEAMFORMING METHODS IN MOBILE COMMUNICATIONS: A REVIEW

ABSTRACT

This paper provides a review of various adaptive modulation and adaptive beamforming methods used in mobile communication systems. A comprehensive review includes some performance comparisons, advantages and drawbacks of each method.

1. Introduction

Current and future demands in mobile communication for various high speed multimedia data services entail a robust, high data rate transmission system. Increasing numbers of users amid limited spectrum motivate research on technology to expand the capacity and increase spectral efficiency. At the same time, some detrimental effects in randomly varying mobile communication environment like multipath fading, co-channel interference and Doppler effects need to be addressed. Adaptive modulation and adaptive beamforming are part of recent methods that known to offer the solution for the abovementioned problems.

Adaptive modulation is a technique that varies some transmission parameters to take advantage of favorable channel conditions. Under bad channel conditions, a robust signal transmission mode will be applied to ensure reliable data exchange. While, in good channel, spectrally efficient mode that offer higher throughput is applied. This mechanism ensures the most efficient mode is always used based on certain criteria and constraints. The varying parameters can be the symbol transmission rate, transmitted power level, constellation size, BER, code rate or scheme, any combination of these parameters [1]. Compared to the fixed system, which was designed specifically for the worst case channel conditions, this adaptive modulation offers higher spectral efficiency, higher throughput and remarkable capacity enhancement without sacrificing BER or wasting power [2].

Research on applications of adaptive antenna arrays have been an interesting subject over past 40 years [3] contributing to the invention of adaptive beamforming method. By taking advantage of the fact that users collocated in frequency domain are typically separated in spatial domain, the beamformer is used to direct the antenna beams toward the desired user while canceling signal from other users [4]. The beamformer electronically steer a phased array by weighting the amplitude and phase of signal at each array element in response to changes in the propagation environment. Capacity improvement is achieved by effective co-channel interference cancellation and multipath fading mitigation.

Theoretically proven impressive performance, coupled with enabling signal processing technologies has attracted researchers to focus on better utilization of the methods discussed. This paper will outline a few approaches of adaptive modulation and adaptive beamforming techniques and highlight some of the recent works that employ these techniques.

2. Adaptive Modulation

With the main objective to maximize the spectral efficiency, many approaches of adaptive modulation have been proposed in literature. An early work includes in Chapter 13 of [5], where W.T. Webb and L. Hanzo introduce variable rate QAM. The transmitter varies the signal constellation size from 1bit/symbol corresponding to BPSK to 6bits/symbol star 64-QAM. In a good quality channel, the constellation size is increased, and as the channel quality become worst, i.e. as the receiver enters a deep fade, the constellation size is decreased to a value, which provides an acceptable BER. Two choices of implementation can be applied where to keep constant of one parameter and varying the other parameter. Specifying a required BER value leads to varying data throughput and vise versa. The chapter also highlights two different types of switching criteria to control the modulation modes, Received Signal Strength Indicator (RSSI) system and error detector switching system. RSSI system use SNR values corresponding to the BER of interest as the switching thresholds while the later system use the channel coder to monitor the channel quality. Simulation results showed the performance improvement over fixed modulation and comparisons of the two switching systems. It is observed that RSSI typically offering a slightly higher number of bits/sym at low SNRs for some BERs. This is due to the RSSI switching system’s ability to select a lower number of levels before any errors occurred. RSSI is also more attractive in term of implementation complexity because no additional BCH codec is needed.

Another literature [2] indicated the above switching system as the channel state information (CSI) which specified the channel quality. SNR based CSI corresponding to RSSI system was compared with error-based CSI corresponding to error detector switching system. SNR based CSI adapts with a faster rate, but relies on the computation of adaptation or switching thresholds that may be inaccurate. Accuracy of the threshold mechanism increases by taking into account higher order statistics of SNR than just the mean.

Studies on varying combination of parameters also attract a great interest. In [6], A. J. Goldsmith and S.G. Chua proposed a variable-rate and variable-power MQAM modulation scheme for fading channels. The transmission rate and power is both optimized to maximize spectral efficiency, while satisfying average power and BER constraints. Spectral efficiency of the proposed technique was derived and compared with Shannon’s capacity limit. A comparison in terms of spectral efficiency between the proposed method and two fixed-rate variable-power schemes also performed. One of the compared scheme, using channel inversion method adapts the transmit power to maintain a constant received SNR. The main drawback of this technique is it suffers a large power penalty since it use most of it power to compensate for deep fades. The other method, called truncated channel inversion maintains a constant received SNR unless the channel fading falls below a given threshold point. It is acknowledged this technique can achieve almost the same spectral efficiency as the proposed method. However, the outage probability can be quite high.

A question raised on power adaptation of whether or not power adaptation actually provides substantial performance gains over constant power system. Goldsmith showed theoretically in [7] that Shannon capacity can be achieved by varying both rate and power. However, as stressed in [1], Shannon capacity assumes that BER is arbitrarily small, coding schemes are random and of unbounded length and complexity, and there is no delay constraint. Therefore, the capacity results do not necessarily shown insight of practical system. Moreover, [7] also highlights that varying both power and rate achieve negligibly increase in channel capacity compared to varying rate only. The results shown in [1] prove that constraining power or rate to be constant causes only little lost in spectral efficiency. They also concluded that spectral efficiency of adaptive modulation is relatively insensitive to which degrees of freedom are adapted.

Realizing that previous work only deals with uncoded modulation, Goldsmith and S.G. Chua [8] propose adaptive coded modulation for fading channels. They applied coset codes since the code design are separable from modulation design, which is well suited to be combined with adaptive modulation system. Special cases of coset codes, trellis and lattice codes were first applied to general class adaptive modulation. Combination of trellis code with spectrally efficient adaptive M-ary quadrature amplitude modulation (M-QAM) introduced in [6] produce trellis-coded adaptive MQAM. Analytical and simulation results shown that the new simple 4-state trellis-coded adaptive MQAM achieves 3-dB effective coding gain relative to uncoded adaptive M-QAM and 3.6 dB for 8-state trellis. Compared with traditional trellis codes and fixed-rate modulation, the new scheme shown more than 20 dB power savings.

K. J. Hole, Henrik Holm and Geir E. Æien [9] introduced a general variable-rate constant-power Trellis Coded Modulation (TCM) scheme for frequency-flat, slowly varying Nakagami Fading (NMF) channel. Their main contribution is the development of a general technique to determine the average spectral efficiency of the coding scheme for any set of 2L-dimensional (2L-D) trellis codes. The paper concentrates on code sets that can be generated by the same encoder and decoded by the same decoder to avoid hardware complexity. It is assumed that the perfect channel state estimation (CSI) is available at the decoder and reliable feedback channel available between the encoder and decoder.

Considering channel estimation accuracy and feedback delay problem, D. L. Goeckel [10] propose an adaptive trellis-coded modulation schemes, which is proved to gain higher bandwidth efficiency over their non-adaptive counterparts on time-varying channels. The scheme was designed using only a single outdated fading estimate when neither the Doppler frequency nor exact shape of autocorrelation function of the channel fading process is known. This issue concerning channel estimation errors and outdated feedback have become one of critical issue in ensure the effectiveness of adaptive modulation. Some works on this includes [11] [12]

Another way of categorizing adaptive modulation is based on the adaptation algorithms used, including the constraints and the objectives. Some typical constraints are upper bound BER, fixed throughput and average transmitted power [13]. For some application that required low delay such as speech and real-time video communication, fixed throughput adaptive transmission is favored. However, for data transmission systems, which can tolerate for higher delay the variable throughput, maximum BER is usually utilized [14]. Aiming to maximize the throughput, some works done on deriving the optimum switching mode thresholds subject to the average BER constraints [12] [15].

2. Adaptive Beamforming

Popularly referred as smart antenna, adaptive beamforming is one of antenna arrays application in mobile communication. With the ability to adaptively directing the beam in specific directions it is known to be an effective technique in canceling co-channel interference. Some of the invaluable references that thoroughly outlined all the methods and algorithms include [4] [16] [17] [18]

J. Litva and K. Y. Lo in Chapter 3 of [4] explained in detail the basic concept of adaptive beamforming starting from the used of two elements array to suppress interference. The fundamental method in adaptive beamforming is to choose the weights of array elements in order to optimize the beamformer response to fulfill certain criterion. The criterion includes Minimum Mean-Square Error, Maximum Signal-to-Interference Ratio and Minimum Variance was discussed in the book. The choice of criteria is not critically important since they are closely related to each other. The more important part is the adaptive algorithms, which will determine the speed of convergence and hardware complexity required. The algorithms include Least Mean Squares algorithm (LMS), Direct Sample Covarince Matrix Inversion (SMI), Recursive Least Squares Algorithms (RLS) and Neural Networks. The notion of partially adaptivity then explained which is the alternative technique when the number of array elements becomes very large until it is difficult to implement full adaptivity. Another important component in adaptive beamforming is the reference signals, also known as the prior knowledge of the signal of interest, which is needed to decrease the complexity, improve accuracy and achieve faster convergence. Two known types are temporal reference and spatial reference.

Chapter 8 of the book then focuses on application of adaptive beamforming in Mobile Communication. Some benefits of using adaptive antennas in mobile communication were listed. The performance improvement in terms of BER and co-channel interference reduction was shown using a few simulation results from some established literatures. Since spatial channel information available on the uplink and most of the research done on it, the discussion was focused on this type of application. An optimum criterion, which was explained in chapter 3 is directly applicable here. Some adaptive algorithms that suitable in mobile communication with their implementation issues then were briefly discussed. This includes LMS algorithm, SMI technique, RLS algorithm touching on the pro and cons of each of them. Other algorithms that proposed to overcome shortcomings or improve the performance of the three basic algorithms such as conjugate gradient method, eigenanlysis algorithm, rotational invariance method, linear least square error (LSSE) algorithm, and Hopfield neural network with respective references are listed.

Then, the discussion continued with some comparison on reference signal types. The estimation technique of spatial reference signals referred as Angle of Arrival (AOA) of the desired signal was categorized into two groups. The first group named as wavenumber estimation is based on decomposition of a covariance matrix whose terms consist of estimates of the correlation between the signals at the elements of an array antenna. The techniques include Multiple signal classification (MUSIC), modified forward-backward linear prediction (FBLP), Principal Eigenvector Gram-Schmidt (PEGS), Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT). The second group is parametric estimation cover a variety of maximum likelihood estimation (MLE) techniques, which require a high computational complexity. It is noted that the main drawback of AOA approaches is requirement for array calibration and extra processing load. The temporal reference may be a pilot signal that is correlated with the wanted signal, or known PN code in CDMA. The alternative techniques in case of unavailability of explicit reference signal called blind adaptive beamforming were briefly described. They are Constant Modulus Algorithm, Decision Directed Algorithm and Cyclostationary Algorithms. Finally, some implementation issues for downlink application were discussed.

An earlier literature by B. D. V. Veen and K. M. Buckley [16] introduced beamforming as a versatile form of spatial filtering. Started with the basic concept, associated the explanation with FIR filtering. Beamformer was classified into data independent and statistically optimum beamformer. Independent of the received data, the first class of beamformer chose a fixed antenna arrays weights. The later class use statistical information of received data to select the weights. Adaptive beamforming comes into picture for the fact that the data statistics are often unknown and varying over time. Two basic adaptive approaches, block adaptation and continuous adaptation were discussed. In block adaptation, the statistics are estimated from temporal block of array data while continuous adaptation the weights are adjusted as the data is sampled. Two basics adaptive algorithms, LMS and RLS also introduced. Partial adaptivity was highlighted.

Lal. C. Godara [17][18] contributed a thorough study on antenna arrays application in mobile communication. Part I gave a brief overview of mobile communications, antenna array terminology, the usage of antenna arrays in mobile communication systems, the advantages and improvements that it brings, design issues, and the feasibility in implementation. Part II presented a detail depiction of various beam-forming schemes, adaptive algorithms, DOA estimation methods, and some issues on error sensitivities. Relevant details and references were provided for further research on each topic.