We consider scheduling and resource allocation in long-term evolution (LTE) networks across voice over the LTE (VoLTE) and best-effort data users. The difference between these two is that VoLTE users get scheduling priority to receive their required quality of service. As we show, strict priority causes data services to suffer. We propose new scheduling and resource allocation algorithms to maximize the sum or proportional fair (PF) throughout amongst data users while meeting VoLTE demands. Essentially, we use VoLTE as an example application with both a guaranteed bit-rate and strict application-specific requirement. We first formulate and solve the frame-level optimization problem for throughput maximization; however, this leads to an integer problem coupled across the LTE transmission time intervals (TTIs). We then propose a TTI-level problem to decouple scheduling across TTIs. Finally, we propose a heuristic scheme, with extremely low complexity. The formulations illustrate the details required to realize resource allocation in an implemented standard. The numerical results show that the performance of the TTI-level scheme is very close to that of the frame-level upper bound. Similarly, the heuristic scheme works well compared to TTI-level optimization and a baseline scheduling algorithm. Finally, we show that our PF optimization retains the high fairness index characterizing PF-scheduling.

A multiuser multi-input multi-output (MU-MIMO) downlink system with block diagonalization-based precoding is presented with a study of the impact of limited bandwidth resource for the feedback-links on the sum capacity. The channel estimation is acquired at each user via downlink training, and the linear quantized form of estimated channels is fed back to the base station (BS) via noisy, finite-rate channel state information (CSI) feedback-links. Two architectures for CSI feedback are considered: parallel feedback where there is a feedback-link for each user; and serial feedback where a single feedback-link is used for all the users. The sum capacity is formulated as a function of the link parameters, enabling optimization of the number of training symbols and required feedback bits with the criterion of maximum sum capacity. The approach allows a performance comparison for different feedback architectures, the number of transmit antennas, and the number of users. The effect of time variations of the channel is analyzed via simulation, quantifying the loss of sum capacity relative to the case with perfect CSI for different Doppler frequencies.

We analyze the uplink of coordinated multi-point (CoMP) networks in which cooperation can be amongst N = 2 or N = 3 base stations (BSs). We consider a 2-D network of BSs on a regular hexagonal lattice wherein the cooperation tessellates the 2-D plane into cooperation regions (CRs); specifically, we analyze the impact of the interference between the CRs in the network. Our model accounts realistic propagation conditions, particularly including shadowing. We obtain accurate, closed-form, approximations for the user capacity coverage probability (CCP) and the ergodic capacity at each point within the CR. To provide a network-level analysis, we focus on the locations within each CR with the minimum CCP - “the worst-case point(s)”. The worst-case CCP and/or ergodic capacity can be used in parametric studies for network design. Here, the analysis is applied to obtain the relationship between cell size and CCP and, thereby, the required density of BSs to achieve a chosen target capacity coverage. The analysis also allows for a comparison between different orders of BS cooperation, quantifying the reduced required BS density from higher orders of cooperation. Comprehensive simulations are used to illustrate the accuracy of our analysis, including the approximations used for analytic tractability.

Assuming a network of infinite extent, several researchers have analyzed small-cell networks using a Poisson point process (PPP) location model, leading to simple analytic expressions. The general assumption has been that these results apply to finite-area networks as well. However, do the results of infinite-area networks apply to finite-area networks? In this paper, we answer this question by obtaining an accurate approximation for the achievable signal-to-interference-plus-noise ratio (SINR) and user capacity in the downlink of a finite-area network with a fixed number of access points (APs). The APs are uniformly distributed within the area of interest. Our analysis shows that, crucially, the results of infinite-area networks are very different from those for finite-area networks of low-to-medium AP density. Comprehensive simulations are used to illustrate the accuracy of our analysis. For practical values of signal transmit powers and AP densities, the analytic expressions capture the behavior of the system well. As an added benefit, the formulations developed here can be used in parametric studies for network design. Here, the analysis is used to obtain the required number of APs to guarantee a desired target capacity in a finite-area network.

This paper addresses adaptive modulation and power allocation for maximizing the practicable sum capacity (sum of the uncoded throughputs of the users) of a multiuser multiple-input single-output MU-MISO) system. Since the optimal solution, dirty paper encoding (DPC), is complicated to implement, we use the simpler linear precoding based on signal- to-leakage-plus-noise ratio (SLNR). For different choices of constellation sizes, the maximum practicable sum capacities and their corresponding optimal power allocations are obtained via individual non-convex optimization. The best constellation sizes for each user are then identified through a search for the highest maximized practicable sum capacity. Simulations demonstrate the significant performance improvement from this approach compared to existing power allocation schemes. Finally, a selection procedure between different constellation sets is presented to obtain the highest practicable sum capacity while maintaining the instantaneous BER of each user below a target value. This approach allows management of the trade-off between the capacity and error performances.

We consider a coordinated multi-point (CoMP) uplink cellular network with a Poisson point process (PPP) model for the position of BSs. Our model assumes cooperation amongst two BSs and the required density is obtained under shadowing and Rayleigh fading for different LTE-A path loss models. We obtain accurate closed-form approximations for the worst-case rate coverage probability within the cooperation region. The approximations presented are useful for a rapid assessment of network performance and can be utilized in parametric studies for network design. Here, they are applied to obtain the required density of BSs to achieve a target rate coverage probability. As an added benefit, the formulation here quantifies the penalty in moving from a regular BS deployment (the grid model) to a random BS deployment (the PPP model).

Water-filled eigenchannels offer the highest multi-input multi-output MIMO information-theoretic capacity, but digital techniques such as quadrature amplitude modulation and finite block lengths will degrade the capacity from the Shannon limit to the capacity of a digital link. Furthermore, eigen-MIMO requires channel overheads, such as estimating the channel state information CSI and feeding it back to the transmitter, which further compromise the capacity. In this paper, the joint influence of channel estimation and imperfect feedback on the information-theoretic capacity and the practicable capacity is analyzed. The channel is modeled as static over a MIMO channel block. In each block, the forward channel is used for CSI estimation and for the payload data transmission. In the back direction, the channel is used to feed back a quantized form of the CSI to the transmitter with a throughput constraint. These three channel usages are combined into an effective simplex channel simplifying the capacity analysis. The capacities are formulated as functions of the link parameters, enabling optimization of the number of training symbols, the feedback duration, and the power allocation for training and data transfer, with the criterion of maximum capacity. The results presented are subject to the usual approximations used in communications theory. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper we investigate the benefit of base station (BS) cooperation in the uplink of coordinated multi-point (CoMP) networks. Our figure of merit is the required BS density required to meet a chosen rate coverage. Our model assumes a 2-D network of BSs on a regular hexagonal lattice in which path loss, lognormal shadowing and Rayleigh fading affect the signal received from users. Accurate closed-form expressions are first presented for the sum-rate coverage probability and ergodic sum-rate at each point of the cooperation region. Then, for a chosen quality of user rate, the required density of BS is derived based on the minimum value of rate coverage probability in the cooperation region. The approach guarantees that the achievable rate in the entire coverage region is above a target rate with chosen probability. The formulation allows comparison between different orders of BS cooperation, quantifying the reduced required BS density from higher orders of cooperation.

Coordinated Multi-Point (CoMP) for Long Term Evolution Advanced (LTE-A) systems refers to a range of techniques to increase the capacity averaged over the cell, and also at the cell edge where the path loss is usually highest. In this paper, the problem of power allocation is first addressed for maximizing the sum capacity at each point of the coverage area in a CoMP multi-user downlink. Then, based on targeting the minimum value of the maximized sum capacity, a design approach is presented for obtaining an optimal size of the cells in a wireless network. The approach guarantees that the sum capacity at each point of the coverage area is above the target value. The path loss model is pivotal for the outcome of this type of performance analysis and the ensuing system design. Here, a simplistic, standard path loss model is used, but the approach can use other models.

Range‐based localisation and tracking methods use the time‐of‐arrival (TOA) between the mobile station and several base stations, but the multipath propagation of non‐line‐of‐sight channels complicates the estimation and processing. For channel modelling, the Gaussian scatterer distribution model has been reported to have a reasonable match between its TOA probability density distribution (PDF) and measured TOA data. In this study, this TOA PDF is adapted, along with selection from multiple motion models of the mobile station, for a new location and tracking algorithm. Since the TOA PDF is non‐Gaussian and is a non‐linear function of the position of the mobile, particle filtering is used which increases the complexity of the algorithm. The focus is on the tracking performance, and this is evaluated by simulation using idealised statistical channels, allowing direct comparison between different location algorithms. In this context, the presented algorithm is more accurate than the benchmarks of extended Kalman filter tracking, and positioning using least squares.

For high data rate multiple-input multiple-output (MIMO) systems, a joint blind channel estimation and data recovery algorithm is presented for where the relative speed of the transmit/receive terminals may change. This changing speed is called manoeuvering. The blind technique is based on non-stationary independent component analysis with a generalised exponential density function to separate each source signal, and it uses particle filtering to track the time-varying channel. The presented technique also uses a hard decision switching block which adaptively selects between discrete speeds of a manoeuvering terminal. The speed can be therefore tracked within a mobile data communication link, that is, by using only the received data information signal. The performance is evaluated by simulation and is compared with optimal coherent detection as benchmark. A large degradation in system error performance is observed if the switching block is disabled within the algorithm, confirming its advantage. Moreover, to assess the impact of the presented blind channel estimation on system error performance more directly, a fair comparison with a known blind technique based on Kalman filtering and two known pilot-aided systems is presented with the assumption of non-manoeuvering terminals. Improved performance is observed using the presented technique.

Eigen-multiple-input multiple-output (MIMO) with water-filling gives the maximum information-theoretic capacity, but realising this maximisation is not straightforward because practical factors are omitted. Adaptive modulation and error coding are combined for finding the maximum practicable capacity (throughput of correctly detected bits) in eigen-MIMO. Quadrature amplitude modulation (QAM) is the logical choice of modulation and here Reed–Solomon (RS) coding is used. RS coding has the advantages of algorithmic simplicity, low-memory requirements, and decoder complexity, and its unique closed-form error probability makes it possible to obtain an optimal power allocation, signal constellation size(s), and code rate(s) on the eigenchannels, for the maximum practicable capacity. The proposed adaptive scheme is applied to two architectures for the encoders/decoders (CODECs): outer coding, where a single CODEC is deployed for the overall serial data; and inner coding, where there is a CODEC for each eigenchannel. The optimal power allocation is different to the water-filling used for the information-theoretic capacity. Also, simple and accurate approximations are found for the bit-error rate (BER) of a single Rayleigh channel and of a 2×2 system. Finally, a selection procedure between different system configurations is presented for obtaining the highest practicable capacity subject to either average or instantaneous output BER.

Water-filled eigenchannels offer the highest MIMO information-theoretic capacity. However, in practice there are many factors, such as digital modulations instead of Gaussian signals, finite block lengths, and imperfect power allocation, that combine to degrade the capacity from the Shannon limit to the practicable capacity - the uncoded throughput - of a digital link. Complexity reduction is also an important factor for a practical system. In this paper, we address the problem of power allocation for maximizing the practicable capacity in eigen-MIMO with the total input power constraint and perfect channel state information (CSI) at the transmitter. We use non-adaptive modulation at the transmitter in order to avoid the high complexity in both the electronics and the protocol - of adaptive modulation. The optimum power allocation reveals new and interesting capacity behavior, and it turns out to be different than that of the water-filling.

An adaptive coding scheme for spectral efficiency improvement in eigen-MIMO is presented. It uses Reed-Solomon (RS) codes with non-adaptive QAM. Non-adaptive modulation is of interest since it reduces the high complexity - in both the electronics and the protocol - required for deploying the more commonly treated adaptive modulation. RS codes have practical advantages in terms of their algorithmic simplicity, memory requirements and decoder complexity. Unlike many codes, an analytical solution for the error probability is available for RS coding. This facilitates finding the jointly optimal code rate(s) and power allocation on the eigenchannels, with the criterion of maximum instantaneous practicable capacity (data throughput). The adaptation is applied to two different architectures of the encoders/decoders (CODECs) for eigen-MIMO, and their performances are compared with the uncoded case. The outer coding architecture refers to a single CODEC working on the overall serial data, and inner coding refers to separate CODECs for different eigenchannels. The adaptive RS system with optimum power allocation reveals new capacity behavior which is different to that of water-filling. For the moderate values of SNR (6-20 dB) typical of wireless systems, the improvement in the capacity over the no-coding case is greater for the inner coding architecture.

A new blind channel estimation technique is presented for non-linear/linear equalisation in a frequency-selective Rayleigh fading channel. At each symbol interval, a decision algorithm first makes a primary data estimate based on constrained linear minimum mean square error criterion, and then this is applied to subsequent channel estimation. Channel estimates are obtained in the form of two alternative, related methods: directly from the Wiener solution or Kalman-based recursion. The former performs better but requires more values from the normalised time-correlation function. The performance is evaluated by simulation, allowing fair comparison with the benchmark of equalised coherent detection; optimal uncoded orthogonal frequency division multiplexing with perfect channel state information at the receiver; the conventional decision-directed Kalman filtering which employs channel tracking with delay; and for the special case of flat fading, an optimised, pilot symbol-assisted modulation system.

This paper describes blind channel estimation for MIMO systems where there are nonlinear distortions in the receiver. The usual source of nonlinearity at the receiver is the front-end amplifier. A recursive algorithm estimates and tracks the channel through unscented transformation. Bussgang's theorem is used to model the in-band distortion comprising a bandpass, memoryless nonlinearity. Simulations demonstrate the excellent accuracy of the algorithm and its power in compensating the effects of distortion. A realistic nonlinearity is almost completely compensated, for small- to mid-values of SNR.

This paper presents a new iterative blind channel estimation technique for systems employing maximum-ratio combining with correlated receive antennas operating over Rayleigh flat fading channels. A decision algorithm first obtains a primary data estimate of the transmitted symbol based on the constrained linear minimum mean square error criterion. Then this estimate is applied to subsequent optimal minimum mean square error channel estimation. Performance is evaluated by simulation and favorable comparison is presented with the known results of optimal coherent detection as well as a known blind technique which employs Kalman filtering to track the channel. The impact of antenna cross correlation is also investigated by simulation.

In this paper, we study the influence of training-based channel estimation on the information-theoretic capacity of closed-loop MIMO systems with imperfect channel state information (CSI) feedback. First, a lower bound on capacity is formulated as a function of various parameters such as received signal-to-noise ratio, number of training symbols, feedback delay time, and the feedback noise variance. Next, we maximize the bound by obtaining the optimal allocation of power for training and data along with the optimal training interval. Fair comparison, through simulation, is also made with the maximized capacity of an open-loop MIMO system. An increase in capacity is still observed over the open-loop capacity for low to mid values of SNR. However, as the result of the imperfect feedback, the open-loop system has higher capacity that the closed-loop system for high SNR. This contribution helps sets the scene for looking at the realizable capacity, which involves the details of a specific communication system.

This paper presents blind channel estimation for Alamouti coding operating in time-correlated Rayleigh channels. A decision algorithm first obtains primary data estimates of the transmitted symbols based on the constrained linear minimum mean square error criterion, and these are applied to subsequent optimal minimum mean square error channel estimation. Performance is evaluated by simulation under the assumption of perfect timing, and presented against the known results of optimal coherent detection. The error performance is shown to be better than the Kalman filtering approach. The impact of finite cross correlations between the receive antennas is also investigated by simulation.

A novel approach to joint blind channel estimation and data recovery is presented for high date rate multiple-input multiple-output (MIMO) systems operating over flat Rayleigh channels. The technique is based on independent component analysis (ICA) with particle filtering to track the time-varying channel. Given one value from the channel matrix coefficients' second-order statistics, non-stationary independent component analysis with a generalized exponential density function is used to separate each source signal. The performance is evaluated by simulation and is compared with optimal coherent detection as benchmark. Improved performance is demonstrated over the "conventional" blind approach with Kalman-based estimation, and over two known pilot-aided systems. Finally, the effect of time-selectivity of the channel on error performance is also assessed by simulation.