Cell-free massive MIMO (CF-mMIMO) is emerging as a technological pillar for future sixth generation (6G) mobile networks, promising consistently high spectral and energy efficiencies across the coverage area. Despite the reported advantages of CF-mMIMO over traditional cellular-based massive MIMO (mMIMO), the extensive deployment of access points (APs) and the associated fronthaul links present significant economic and logistical challenges. This paper proposes a transitional framework to facilitate the gradual integration of CF-mMIMO into existing cellular infrastructures, allowing mobile network operators to progressively realize the benefits of a distributed network topology. A comprehensive analysis of the spectral, energy, and computational efficiencies in heterogeneous network scenarios, incorporating both macrocellular and cell-free components, is presented. Our contributions include a unified assessment framework encompassing spectral, energy and computational aspects, a novel channel virtualization mechanism for effective downlink precoding, and a realistic industry-backed power consumption model for joint network operation. The potential performance gains are demonstrated and guidelines for the incremental deployment of CF-mMIMO are provided through detailed numerical results, ensuring a balanced trade-off between integration costs and operational benefits. This approach aims to leverage the capabilities of emerging network architectures to achieve a seamless evolution towards fully distributed 6G networks.

This paper derives an achievable rate for centralized uplink cell-free massive MIMO (CF-mMIMO) networks when using superimposed pilots in combination with advanced combining schemes. This rate relies on a novel channel estimation technique that uncorrelates the combiner applied to any given information symbol from the channel estimate and its corresponding channel estimation error. The performance of powerful combining strategies such as minimum mean square error (MMSE) or zero-forcing (ZF) can then be assessed in the context of superimposed pilot transmission. Results show that despite superimposed training might be suitable when using the simple maximum ratio combining (MRC) technique, its performance when compared with regular pilot transmission falls short when advanced combining strategies are used.

This work exposes and tackles the problem of providing wireless service to a mixed population of indoor and outdoor users using an outdoor cell-free massive MIMO (CF-mMIMO) network. Since cell-free networks are typically designed to provide near-uniform quality-of-service to all users, it is shown here that such strategy, when applied blindly to indoor and outdoor users, causes the network performance to dramatically degrade. A framework is then introduced that treats indoor and outdoor users separately in such a way that they both see their performance very significantly enhanced. In particular, user connectivity, power and pilot allocation are conducted for each user class senarately while preserving scalability.

A mobile edge computing (MEC) aided cell-free massive MIMO network is investigated in this paper that aims at optimizing the task offloading process from the wireless devices to the MEC server. Partial computation offloading strategies are designed that aim at optimizing either the aggregated latency or the energy consumption by considering the finite computational capability of the MEC server and assuming that the tasks are subject to a maximum latency constraint. The proposed optimization approach considers the joint optimization of both the per-task offloading ratio and the corresponding computational resources allocated to the tasks at the MEC server. This optimization is performed by taking into account the computing capabilities of both the wireless devices and the MEC server and both the UL and DL spectral efficiencies provided by the cell-free massive MIMO network. The proposed optimization problems are shown to be convex and, inspired by the well-known waterfilling algorithm, optimal solutions based on low-complexity iterative algorithms are devised. Extensive numerical results reveal the potential of the proposed MEC-enabled cell-free massive MIMO network.

Cell-free massive multiple-input multiple-output (MIMO) networks, where a massive number of geographically distributed access points cooperate to jointly serve the users, have been proved to solve many of the interference related issues associated to cellular networks. One of the main challenges of cell-free massive MIMO networks, however, is to guarantee that the potential benefits this architecture entails can be achieved with signal processing computational complexities and fronthaul resource requirements that are scalable as the number of users goes to infinity. User-centric architectures have been proposed in the literature that potentially solve the scalability issues related to, first, the signal processing associated to channel estimation and massive MIMO combining/precoding, second, the power allocation algorithms and, third, the fronthaul signaling for data and channel state information sharing. One of the best centralized combiners/precoders that have been proposed in practice, however, which is termed as partial minimum mean square error (P-MMSE), does not allow obtaining expressions of its spectral efficiency in a scalable way except when using lower bounds based on the well-known use-and-then-forget (UatF) approach. These lower bounds, however, in addition to being rather inaccurate, are not suited for the design and analysis of non-linear combining/precoding schemes based on successive interference cancellation (SIC) when users are equipped with multiple antennas. In this paper, we propose novel linear and non-linear combining/precoding schemes, which we term improved P-MMSE (IP-MMSE), whose achievable spectral efficiency can be accurately analyzed in a scalable manner. The proposed IP-MMSE combiner/precoder achieves spectral efficiencies close to that provided by MMSE-based unscalable solutions, and outperforms to a great extent the P-MMSE-based counterparts.

This paper considers the design of beam squint-aware channel covariance-based hybrid beamformers for wideband cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. Single and double phase shifter-based analog radio frequency (RF) precoder/combiner strategies are proposed that are based on dissimilar beam squint awareness assumptions. The digital precoding/combining stage is implemented using centralized designs maximizing the signal-to-interference-plus-noise-ratio (SINR) and relying on the uplink (UL)/downlink (DL) duality enabled by the time division duplex (TDD) protocol and the use of either phase shift-aware minimum mean square error (MMSE) or phase shift-unaware linear MMSE (LMMSE) channel estimators. The performance of the proposed beam squint-aware hybrid beamforming strategies is assessed through extensive numerical simulations under different scenarios. Numerical results show that beam squint-aware designs outperform the beam squint-unaware strategies, specially in typical wideband cell free mmWave massive MIMO-OFDM scenarios where a combination of a very high carrier frequency, a large system bandwidth, and a large-scale antenna array causes the spatial-wideband effect.

Simultaneous wireless information and power transfer (SWIPT) has been advocated as a highly promising technology to provide near-perpetualoperation to low-powered wireless devices in Internet-of-Things (IoT)-based wireless networks. In this paper, a SWIPT-enhanced cell-free massive MIMO network is proposed. In such a network, a large set of spatially distributed access points (APs) interconnected via a central processing unit (CPU) can collaboratively serve a large number of both energy harvesting mobile stations (MSs) (requiring wireless energy transfer) and conventional MSs (not requiring wireless energy transfer) on the same time-frequency resources. We consider spatially correlated Rician fading channels and the use of different precoding schemes that are based on different channel estimators differing on the assumed knowledge of the line-of-sight component. Mathematically manageable expressions are derived for the harvested energy during the downlink (DL) energy harvesting phase and theachievablespectral and energy efficiencies during the uplink (UL) payload transmission phase. A coupled UL/DL optimization problem is formulated aiming at finding the power control coefficients that maximize the minimum of the weighted achievable UL signal-to-interference-plus-noise ratios (SINRs) of all MSs. Extensive numerical results are presented that serve to highlight the existing trade-offs among the achievable spectral and energy efficiencies, the harvested energy, the energy dedicated to UL pilot transmission or the system configuration.

Network densification is one of the most promising solutions to address the high data rate demands in 5G and beyond (B5G) wireless networks while ensuring an overall adequate quality of service. In this scenario, most users experience significant interference levels from neighbouring mobile stations (MSs) and access points (APs) making the use of advanced interference management techniques mandatory. Clustered interference alignment (IA) has been widely proposed to manage the interference in densely deployed scenarios with a large number of users. Nonetheless, the setups considered in previous works are still far from the densification levels envisaged for 5G/B5G networks that are considered in this paper. Moreover, prior designs of clustered‐IA systems relied on oversimplified channel models and/or enforced single‐stream transmission. In this paper, we explore an ultradense deployment of small cells (SCs) to provide coverage in 5G/B5G wireless networks. A novel cluster design based on a size‐restricted k‐means algorithm to divide the SCs into different clusters is proposed taking into account path loss and shadowing effects, thus providing a more realistic solution than those available in the current literature. Unlike previous works, this clustering method can also cater for spatial multiplexing scenarios. Also, several design parameters such as the number of transmit antennas, multiplexed data streams, and deployed APs are analyzed in order to identify trade‐offs between performance and complexity. The relationship between density of network elements per area unit and performance is investigated, thus allowing to illustrate that there is an optimal coverage area value over which the network resources should be distributed. Moreover, it is shown that the spectral‐efficiency degradation due to the intercluster interference in ultradense networks (UDNs) points to the need of designing an interference management algorithm that accounts for both intracluster and intercluster interferences. Simulation results provide key insights for the deployment of small cells in interference‐limited dense scenarios.

Coordinated multipoint transmission (CoMP) and soft frequency reuse (SFR) techniques are two common mechanisms that are often combined with the aim of reducing the deleterious effects caused by inter-cell interference in current and envisaged cellular networks (4G/5G). The performance of this combined mechanism has been shown to be strongly influenced by the specific choice of the parameters determining the frequency reuse settings. So far, their optimization has relied on time-consuming simulations that make the CoMP-SFR optimization a challenging issue. In an attempt to revert this situation, this paper introduces a novel analytical framework that, for a specific set of SFR parameters, allows the computation of closed-form approximations to relevant performance metrics such as average total capacity, worst-users rate, and backhaul requirements. Remarkably, these approximations exhibit a dependence on the SFR parameters that match that of the true metrics, hence allowing the optimization of the SFR-related parameters based on the proposed analytical expressions. Furthermore, and very important from a practical perspective, the availability of these closed-form expressions allows the efficient application of multi-objective optimization techniques to realistic scenarios with irregularly deployed base stations that serve to evaluate the various tradeoffs the designer is facing when optimizing multiple, and often conflictive, performance metrics.

Coordinated multipoint transmission (CoMP) and soft frequency reuse (SFR) are often combined with the aim of reducing the deleterious effects caused by inter-cell interference in current and envisaged cellular networks (4G/5G). The performance of this combined mechanism has been shown to be strongly influenced by the specific choice of the parameters determining the frequency reuse settings. So far, the optimization of this combined setup has relied on time consuming numerical simulations that make it a challenging issue specially in realistic non-regular network deployments where network symmetry cannot be exploited. In an attempt to revert this situation, this paper introduces a novel analytical framework that, for a specific set of SFR parameters, allows the computation of closed-form approximations to relevant performance metrics such as average total capacity, worst-users rate and backhaul requirements. Remarkably, these approximations exhibit a dependence on the SFR parameters that matches that of the true metrics, hence allowing the optimization of the SFR-related parameters based on the proposed analytical expressions. Furthermore, and very important from a practical perspective, the availability of these closed-form expressions allows the efficient application of multiobjective optimization techniques to realistic scenarios with irregularly-deployed base stations that serve to evaluate the various trade-offs the network designer is facing when optimizing multiple, and often conflictive, performance metrics.

One of the most important factors affecting the quality of service (QoS) in orthogonal frequency division multiple access (OFDMA) networks is the intercell interference (ICI). An efficient technique for mitigating ICI is the well-known fractional frequency reuse (FFR), wherein cells are partitioned into center and edge spatial regions and different frequency reuse factors are applied to both of them. This work presents an analytical characterization of FFR-aided OFDMA-based networks that, unlike most previous studies, incorporates shadowing effects. The FFR design parameters maximizing the overall cell spectral efficiency, are determined for both round robin (RR) and maximum signal to interference plus noise ratio (MSINR) scheduling policies. Simulation results serve to validate the accuracy of the introduced model while at the same time reveal which are the optimum FFR settings.

Coordinated multipoint transmission (CoMP) and soft frequency reuse (SFR) are two popular techniques used to control the deleterious effects caused by inter-cell interference in current 4G and envisaged 5G cellular networks. The performance of the combination of CoMP and SFR has been shown to be strongly influenced by the specific choice of the parameters governing the action of both mechanisms. Unfortunately, the optimization of these parameters has so far relied on time consuming numerical simulations that even forbid the utilization of all the degrees of freedom these two techniques bring along. In an attempt to revert this situation, this paper introduces a novel analytical framework that allows the off-line computation of the optimal parameters governing the SFR parameters in irregular CoMP-aided MIMO-OFDMA-based networks. In particular, the proposed model provides an upper-bound of the average capacity of the central and edge regions. Interestingly, the derived model opens the door to far more sophisticated configurations than the ones currently available when optimization can only be conducted via simulations.

OFDMA-based multi-cellular standards incorporate interference coordination techniques that allow near universal frequency reuse while preserving reasonably high spectral efficiencies. In particular, frequency reuse approaches are employed to mitigate the inter-cell interference and thereby enhancing cell-edge users performance. Two representative strategies are fractional frequency reuse (FFR) and soft frequency reuse (SFR), which are deemed to play a key role in the next generation of cellular networks. This paper introduces an analytical framework allowing the analytical evaluation of performance metrics such as the spectral efficiency or user throughput CDF in the context of FFR/SFR-aided OFDMA networks. Remarkably, the proposed analytical framework is used to design optimal schemes by suitably dimensioning the inner and outer cellular areas, the frequency allocation to each of these regions and the corresponding transmit power. The performance evaluation results combine metrics such as spectral efficiency and the 5th-percentile of users rate in order to characterize and optimize the performance of FFR/SFR-aided OFDMA networks.

This paper introduces a cross-layer framework for scheduling and resource allocation suitable for base station (BS)-cooperative wideband wireless cellular systems supporting heterogeneous traffic. To this end, advanced cluster-based multiple-antenna techniques such as network MIMO (N-MIMO) or large-scale MIMO (LS-MIMO) are considered for the physical layer of the system. The framework is general enough to be able to cater for different forms of power allocation, rate allocation or scheduling policy while providing critical performance metrics such as throughput or fairness measures while being able to incorporate per-cluster as well as per-BS power constraints. Remarkably, different greedy scheduling algorithms are proposed for both N-MIMO and LS-MIMO that are able to significantly exploit the multiuser and frequency diversity of the system while remaining computationally feasible. A particularly interesting application of the proposed framework, explored in detail in this paper, is a thorough and realistic comparison of the performance of N-MIMO and LS-MIMO that reveals the strengths and weaknesses of each strategy in terms of throughput and fairness.

Multiuser MIMO (MU-MIMO) in general, and block diagonalization (BD) in particular, are playing a prominent role toward the achievement of higher spectral efficiencies in modern OFDMA-based wireless networks. The utilization of such techniques necessarily has implications in the scheduling and resource allocation processes taking care of assigning subcarriers, power, and transmission modes to the different users. In this paper, a framework for channel- and queue-aware scheduling and resource allocation for BD-based MU-MIMO-OFDMA wireless networks is introduced. In particular, using an SNR-based abstraction of the physical layer, the proposed design is able to cater for different BD-MU-MIMO processing schemes [co-ordinated Tx-Rx (CTR) or receive antenna selection (RAS)], uniform or adaptive power allocation (UPA/APA), continuous or discrete rate allocation (CRA/DRA), and many different scheduling rules. Additionally, the different strategies are complemented by a new greedy user/stream selection algorithm that is shown to perform very close to the optimal user/stream selection policy at a much lower complexity. Results using system parameters typically found in 4G networks reveal that, in most cases, low-complexity solutions (RAS-, UPA-based) achieve a performance close to the one attained by their more complex counterparts (CTR-, APA-based).

Two-tier wireless architectures where the macro- cellular network is complemented with a deployment of femto-cells are becoming an attractive solution to increase the global throughput in the context of OFDMA-based 4G/5G networks. This paper presents an analytical framework allowing the performance evaluation of FFR-aided OFDMA-based two-tier HetNets. A worst-case scenario in terms of inter- tier interference is evaluated in which macrocell and femtocell tiers are assumed to be uncoordinated and co-channel deployed. The proposed framework allows the evaluation of the impact produced by both inter- and co-tier interferences on either the macro-user-equipments (MUEs) or the femto-user-equipments (FUEs). Analytical performance results are used to optimise the FFR parameters as a function of, for instance, the resource block scheduling policy used at both the MBSs and the FBSs, the number of MUEs per cell, the density of FBSs per area unit or the degree of isolation provided by wall penetration losses.

Fractional frequency reuse (FFR) is an intercell interference coordination technique that has become increasingly popular in the context of 4G systems. The combination of FFR with advanced multiple-antenna technology in the form of multiuser MIMO (MU-MIMO) and base station cooperation using coordinated multipoint transmission (CoMP), paves the way to realize the ambitious goals in terms of area spectral efficiency contemplated within Long Term Evolution Advanced (LTE-A). This work proposes an FFR-aided scheme suitable for multicell networks in which central users are served using MU-MIMO and edge users rely on CoMP, with both techniques depending on block diagonalization. A physical layer abstraction is provided in the form of signal-to-interference-plus- noise ratio (SINR) expressions that can then be used to derive optimal values for different parameters, most notably, the threshold SINR delimiting the central and edge areas of the cell.

Interference coordination techniques are typically incorporated in OFDMA-based multi-cellular networks in order to preserve high spectral efficiencies over the whole coverage area and to improve the system capacity. Fractional frequency reuse (FFR) and its variants are employed to mitigate the inter-cell interference that particularly suffer cell-edge users. This paper presents an analytical framework that is used to optimise the FFR parameters in OFDMA networks when using channel-aware scheduling policies. The downlink performance evaluation results are obtained as a function of, for instance, the resource block scheduling policy or the density of UEs per cell. Furthermore, different optimisation designs of the FFR component are proposed that allow a tradeoff between throughput performance and fairness by suitably dimensioning the FFR inner and outer areas and the corresponding frequency allocation to each of these regions.

Recent years have seen an upsurge in novel techniques to satisfy the ambitious requirements of modern wireless cellular systems in terms of area spectral efficiency whereby users located anywhere in the cell, even at the edge, should be able to obtain reasonably large throughputs. In particular, interference cancellation techniques based on different forms of frequency reuse (FR) and coordinated multipoint transmission (CoMP) have shown great potential to realize such goal. This paper proposes a framework to evaluate the combination of FR and CoMP from a multi-objective performance point of view, where different metrics related to capacity and fairness can be incorporated. This framework rests upon a physical layer abstraction derived for the particular case of block diagonalization (BD)-based MIMO processing, a widely used technique known to perform close to optimality yet remaining computationally simple. Numerical results show that the design of the FR parameters play a key role in the overall network performance and suggest the use of utility-based functions combining various metrics as a suitable mechanism to conduct this optimization.

In a recent paper published in these Transactions, Xu et al. (see ibid., vol. 11, no. 8, pp. 2776-2785, Aug. 2012) proposed an analytical framework to calculate the throughput and optimal threshold for fractional frequency reuse (FFR) schemes in orthogonal frequency division multiple access (OFDMA) cellular networks. This correspondence points out a problem in the original derivation of Xu et al. and how this affects the claimed performance of the maximum signal-to-interference-plus-noise ratio (MSINR) scheduling rule. A fix to this problem is presented in this work, which turns out to extend the applicability of the analysis beyond the case studied by Xu et al. and at the same time shows how the performance of the round robin (RR) scheduling rule can be obtained as a special case of the MSINR scheduler. Monte Carlo simulation results demonstrate the validity and merits of our proposed approach.

This work considers the design of multiuser MIMO (MU-MIMO) schemes suitable for orthogonal frequency division multiplex (OFDM) systems such as those being currently discussed within IEEE 802.11ac. A general framework is proposed showing that the singular value decomposition (SVD), the geometric mean decomposition (GMD), and the uniform channel decomposition (UCD) are all viable mechanisms on which to lay the design. Nonetheless, some striking differences in terms of performance among these three schemes are uncovered. In particular, a novel adaptive transmission scheme based on the UCD is introduced that enables multiuser MIMO-OFDM gains to be realized, clearly outperforming those obtained using the SVD or GMD counterparts while remaining fully compliant with the latest IEEE 802.11ac specification. Relying on the UCD properties, the proposed technique takes into account the availability of a finite modulation and coding set and restrictions on transmission mode selection defined within the standard, to incorporate an optimisation step in charge of selecting the number of transmitted data streams to each user to maximise the system throughput. Comprehensive simulation results in the context of IEEE 802.11ac confirm the applicability and advantage of the adaptive UCD-based design.

In MIMO-OFDMA systems, scheduling and resource allocation algorithms are crucial to optimize the resource utilization while providing support to applications with heterogeneous QoS requirements. To this end, the transmitter is assumed to have channel state information (CSIT) that will typically be imperfect. This paper introduces a unified analytical framework for channel- and queue-aware QoS-guaranteed cross-layer scheduling and resource allocation algorithms for MIMO-OFDMA networks with imperfect CSIT. The framework encompasses different types of traffic, uniform and continuous power allocation, discrete and continuous rate allocation, and protocols with different amounts of channel- and queue-awareness. Simulation results demonstrate the validity and merits of the proposed cross-layer unified approach.

This paper explores in detail the application of optimum detection based on maximum likelihood principles in linearly precoded subcarrier cooperative MIMO-OFDM systems. Two different strategies, full spreading and efficient spreading, are presented to disperse the information in the frequency and space domains with the objective of increasing the diversity gain of the system under the assumption of optimum detection at the receiver. The application of efficient spreading at the transmitter allows the implementation of a low-complexity optimal detector that combines linear and non-linear processing. These two techniques, full and efficient spreading, represent different operating points in the performance-complexity plane as shown by the analytical derivation of their diversity order. Remarkably, this analysis serves to reveal and quantify the benefits of subcarrier cooperation with optimum reception. Finally, and in order to highlight the benefits of the proposed approach in practical setups such as those found in contemporary wireless standards, the receiver architectures are extended to operate with soft information, thus allowing the use of iterative soft based receivers. Simulation results demonstrate that efficient spreading in combination with iterative decoding is an attractive combination enabling the implementation of high throughput wireless systems with a large degree of robustness.

The performance of fast link adaptation (FLA)algorithms in wireless local area networks (WLANs) is affected by the delay of the modulation and coding scheme (MCS) feedback information. In this paper, a novel FLA strategy is shown to be able to cope with expired MCS information. This strategy relies on a timeout-based mechanism that weights down the importance of the MCS feedback information as this becomes outdated. Moreover, the performance enhancement of using FLA in carrier sense multiple access with enhanced collision avoidance (CSMA/E2CA) protocol is also presented. Remarkably, CSMA/E2CA- based networks drastically reduce the number of collisions in comparison to the IEEE 802.11 standard mechanism, the carrier sense multiple access with collision avoidance (CSMA/CA) protocol. For completeness, the performance of FLA is compared with the ubiquitous auto rate fallback (ARF) algorithm, showing that FLA clearly outperforms open-loop techniques such as ARF.

Maximum likelihood detection (MLD) has been shown to offer large performance gains within the context of linearly precoded MIMO-OFDM systems with multiple-stream transmission when compared to an all-linear setup (i.e., linear transmitter/receiver), although at the cost of a higher computational complexity. To overcome this handicap, this paper proposes a method to selectively choose the detection strategy (ML or linear) as a function of the instantaneous channel conditions and the accuracy of the channel state information at the transmitter (CSIT). The proposed technique is suitable for both hard- and soft-decoded setups. Numerical results show that a cautious and selective use of ML detection substantially reduces complexity while still reaping most of the performance advantage.

This paper presents a MAC-PHY cross-layer semianalytical model allowing the goodput performance evaluation of open- and closed-loop link adaptive schemes in WLAN environments based on IEEE 802.11n. Without loss of generality, this paper considers the well-known open-loop automatic rate fallback (ARF) algorithm and a closed-loop fast link adaptation (FLA) algorithm based on exponential effective SNR mapping (EESM). In contrast to previous works, the channel conditions are not assumed to be ideal and users are allowed to use different transmission modes. Results are presented demonstrating the accuracy of the proposed model. Moreover, results serve to show the superiority of closed-loop over open-loop techniques, specially when the number of stations contending for the medium grows.

A cross-layer performance analysis of a wireless network using adaptive modulation and coding at the physical layer and a truncated Chase combining hybrid automatic repeat request (HARQ-CC) scheme for error control at the data link layer is developed. Based on a Markov chain queueing model, analytical expressions for performance metrics such as throughput, average packet delay and packet loss rate are derived and then used to formulate a constrained optimization problem to maximize the system throughput under the prescribed Quality-of-Service constraints. Numerical results reveal that HARQ-CC consistently outperforms the classical Type-I Hybrid forward error correction/automatic repeat request schemes.

Recent research has shown that code-division multiplex orthogonal frequency-division multiplex (CDM-OFDM) significantly improves the robustness of conventional OFDM against subcarrier fading. In particular, the combination of CDM with multiple transmit antennas has been shown to be very effective in the context of IEEE 802.11n. In this paper another distinctive feature of the forthcoming WLAN standard, namely, the existence of a feedback channel, is exploited to implement a frequency diversity adaptive technique. In the proposed method the receiver estimates an effective diversity order of the underlying wireless channel and feeds it back to the transmitter, which then uses it to appropriately dimension the CDM component. At the same time, the receiver can switch between iterative or noniterative processing depending on the CDM dimension of the forthcoming packets. Results show that this adaptivity can bring along important reductions in complexity without compromising error performance.

Code-division multiplex orthogonal frequency-division multiplex (CDM-OFDM) has received considerable attention due to its superior resilience against subcarrier fading in comparison with conventional OFDM. More recently, group-orthogonal CDM-OFDM (GO-CDM-OFDM) has been considered as a suitable method to extract additional frequency diversity of the wireless channel by splitting the available subcarriers into (orthogonal) groups and applying optimum detection at the receiver on a per-group basis. One critical parameter of this scheme is the group size as it can effectively act like a complexity-performance trading knob. In this work an adaptive technique to dynamically adjust the group size as a function of the instantaneous operating scenario is presented. It is shown that this adaptivity can bring along important reductions in complexity without compromising error performance.

Wireless architectures combining the use of spatial division multiplexing (SDM) and space-time block coding (STBC) have begun to appear in the latest communications standards. In recent years, different detection strategies for these schemes have been proposed which can be broadly categorised as group-based or direct-detection techniques. While the former class aims at separating the different STBC streams and then apply conventional simple Alamouti decoding, the latter type directly estimates the transmitted symbols without fully exploiting the Alamouti structure. Previous publications have shown that direct-detection outperform group-based detectors with the penalty of a higher computational complexity. This letter presents a unified view of the detection strategies for hybrid SDM/STBC systems and presents two new families of group-based detectors. It is shown that linear group detection, when properly implemented, attains the same performance as linear direct detectors at a significant lower complexity.

This paper proposes the use of group-orthogonal frequency diversity combined with space-time block coding (STBC) to improve the performance of IEEE 802.11n wireless networks (WLANs). The extra diversity is achieved by using code-division multiplexing whereby each symbol is transmitted on more than one subcarrier. At reception, following the STBC decoding, a maximum likelihood-based turbo receiver is used to take full advantage of the available diversity. The performance of the proposed scheme is investigated over the two different operating bandwidths specified in 802.11n, (20 or 40 MHz) and with different number of transmit antennas. Simulation results using the current IEEE 802.11n specifications serve to illustrate the benefits of the proposed scheme under realistic conditions.

Group-orthogonal multicarrier code-division multiple access (GO-MC-CDMA) has been proposed as an attractive multiplexing technique for the uplink segment of wireless systems. More recently, a variant of this scheme has also been proposed for the downlink. This paper presents a unified bit error rate (BER) performance analysis of group-orthogonal wireless systems when using maximum likelihood (ML) multiuser/multisymbol detection covering both link directions. Valuable design rules regarding the number of subcarriers per group and the selection of spreading codes are derived. Simulations results using realistic system parameters and ETSI BRAN channel models are also presented which serve to validate the analytical results.

Group-orthogonal multicarrier code-division multiple access (GO-MC-CDMA) has been proposed as a multiplexing technique for the uplink segment of wireless systems. The distinctive feature of GO-MC-CDMA with respect to MC-CDMA is that users do not employ all the available subcarriers, instead, they are collected in orthogonal groups with each group functioning as an independent MC-CDMA system. When distributing users among groups, and in order to simplify network management and to increase system flexibility, it is important to allow users in the same group to employ different modulation formats and/or power constraints. This paper generalizes previous analyses of maximum likelihood (ML) multiuser detection (MUD) in GO-MC-CDMA to the case where, due to QoS requirements, users in the same group employ different modulation formats and/or receive different power levels. Simulations results using realistic parameters over an ETSI BRAN channel model are presented which serve to validate the analytical results.

Turbo coding in correlated Rayleigh fading channels is improved by outer block interleaving, but with added complexity and delay. Choosing the wrong number of columns can increase the BER, so a reconfigurable block interleaver with column number optimized for different operating environments is useful. Simulation results are presented for the four block interleaver configurations specified for the 3GPP mobile standard in the case of correlated Rayleigh fading. Using different data frame lengths in various scenarios, the advantages of adapting to an optimum number of columns are demonstrated.

In this paper we investigate the relationship between the decision delay and the convergence properties of DFE equalizers when using gradient algorithms to perform the coefficient adaptation. We show how the delay affects the structure of the input data correlation matrix and how a poor choice of the decision delay may dramatically increase its eigenvalue spread which in tum causes a significant increase in the convergence time. Rules are derived showing how the delay can be selected to minimize, or at least greatly reduce, the eigenvalue spread while at the same time keep the steady-state mean squared error (MSE) level low.

Outer block interleaving improves the performance of turbo codes in correlated Rayleigh fading channels. The longer the outer interleaver, the better is the performance, with the penalty of more delay. Power could be saved by choosing the optimum interleaver for different operating environments. The four possible outer block interleaver lengths specified for 3GPP are examined for correlated Rayleigh fading channels with different normalized fade rates f/sub d/T/sub s/. SOVA and log-MAP algorithms are used in the turbo decoder, with a turbo interleaver length of 5114 bits. Our results show that choosing a TTI (time transmission interval) of 40 msec instead of 80 msec does not make a very large difference in performance, while the highest performance gain occurs for a TTI of 20 msec. We discuss the selection of the optimum interleaver length for the same bit-rate, but different scenarios.