Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically considering correlated eavesdropping channels. Aiming to maximize the SKR, a joint design problem of the BS’s precoding matrix and the IRS’s phase shift vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimization to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS's precoding matrix and the IRS's reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.

One effective way of generating and distributing secret keys for secure communication is to exploit the randomness of wireless channels. Since a potential eavesdropper has a different propagation channel than two legitimate users, it can be used to generate unique and secure keys. After reciprocal channel sounding, a frequency-selective channel can be quantised to produce the secret keys. Due to different sampling times at the two legitimate devices, the phase of the frequency response will not be equal and hence compromise the secret key generation. In this paper, we show how to overcome this problem and verify the results through both simulations and hardware measurements. We also compare scalar and vector quantisation, and show how the latter can be designed to be robust to synchronisation offsets.

Device authentication is essential for securing Internet of things. Radio frequency fingerprint identification (RFFI) is an emerging technique that exploits intrinsic and unique hardware impairments as the device identifier. The existing RFFI literature focuses on experimental exploration but comprehensive modelling is missing. This paper systematically models impairments of transmitter and receiver in narrowband systems and carries out extensive experiments and simulations to evaluate their effects on RFFI. The modelled impairments include oscillator imperfections, imbalance of inphase (I) and quadrature (Q) branches of mixers and power amplifier (PA) nonlinearity. We then propose a convolutional neural network-based RFFI protocol. We carry out experimental measurements over three months and demonstrate that oscillator imperfections are not suitable for RFFI due to their unpredictable time variation caused by temperature change. Our simulation results show that our protocol can classify 50 and 200 devices with uniformly and randomly distributed IQ imbalances and PA nonlinearities with high accuracy, namely 99% and 89%, respectively. We also show that the RFFI has some tolerance on different receiver imbalances during training and classification. Specifically, the accuracy is shown to degrade less than 20% when the residual receiver's gain and phase imbalances are small. Based on the experimental and simulation results, we made recommendations for designing a robust RFFI protocol, namely compensate carrier frequency offset and calibrate IQ imbalances of receivers.

This paper deals with IQ imbalance in wideband MIMO OFDM systems. In systems with a large bandwidth, the IQ imbalance will be frequency-dependent and hence cannot be described by a single parameter. Furthermore if there is a carrier-frequency offset (CFO), the IQ imbalance estimation in a MIMO system becomes a challenging task. However the CFO introduces a time-varying component to the received signal which can be exploited to estimate the model parameters using a carefully designed training sequence. This approach obtains the TX and RX IQ imbalance separately in an accurate fashion. We verify this by simulations and also show the estimated IQ imbalance in a customised hardware prototype.

Considerable research efforts have been made into 5G systems, in which wireless backhaul plays a key part. This paper presents a novel E-band testbed for 5G backhaul applications with wideband IQ imbalance compensation. It is based on OFDM to compensate the effect of wideband channel distortions as well as RF impairments. Moreover, line-of-sight polarization MIMO with two orthogonal polarizations is applied to enhance the data rate, which in combination with OFDM results in a simple equalization approach. In this testbed, carrier frequency offset and Tx/Rx IQ imbalance are jointly estimated and compensated. We describe how the testbed was designed. Then, we apply the IQ imbalance compensation and demonstrate that a data rate of 20 Gbps can be achieved in an actual outdoor environment distances of 900 m.

This paper deals with IQ imbalance in wideband MIMO OFDM systems. In systems with a large bandwidth, the IQ imbalance will be frequency-dependent and hence can not be described by a single parameter. Furthermore if there is a carrier-frequency offset (CFO), the IQ imbalance estimation in a MIMO system with multiple channels becomes a challenging task. However the CFO introduces a time-varying component to the received signal which we exploit to estimate the model parameters using a carefully designed training sequence. This approach derives the TX and RX IQ imbalance separately in an accurate fashion. We verify this by simulations and also show the estimated IQ imbalance in a hardware prototype we have built.

We consider a single-source, multiple-relay, and single-destination lossy network employing random linear network coding at all transmitting nodes. We address the problem of calculating the probability of successful decoding at the destination node. In contrast to some previous studies, we assume the classical random linear network coding scheme, in which the relaying nodes simply recode packets, without resorting to decoding. In addition, we consider an arbitrary field size and take into account the correlation between the relay nodes. We propose a novel upper bound for an arbitrary number of relays, which becomes exact for a single relay. Using Monte Carlo simulations, we show that the proposed bound is very accurate, exhibiting the mean square error as low as 10-6. We also demonstrate the throughput gain of the proposed scheme over alternative coding and relaying strategies.

Tone reservation (TR) is a well-known and practical strategy to reduce peak-to-average power ratio (PAPR) in OFDM signals. In TR some unused or reserved subcarriers are used to accommodate a peak-cancelling signal that lowers the PAPR of the time domain OFDM signal. Several methods have been proposed to optimise the peak-cancelling signal, amongst which the so called active-set method has been shown to converge very quickly to a minimum PAPR solution for a real baseband signal with no additional power constraints. However, for a general complex baseband signal and/or when the peak-cancelling signal has to be power limited, there is no guarantee of optimality. Moreover, in the complex baseband model, the TR optimisation problem is a quadratically constrained quadratic program (QCQP), which has a prohibitive computational cost. In this paper we introduce a tree-search approach to designing the TR signal based on the sphere decoding/encoding principle. The method is proved to provide the minimum PAPR solution in the limit of fine lattice quantisation. Simulation results show a significant margin of improvement over the best known active-set method.

Precoding is a common technique used in wireless transmissions where multiple data streams are spatially multiplexed by an access point or base station to a number of receiving stations by using multiple transmit antennas. The design of the precoder can be divided into two separate functional blocks: 1) the transmit filters, for example zero-forcing or minimum mean-square error, with unit norm columns and 2) the power loading. In a conventional system the transmit filters and power loading strategy are applied on individual OFDM subcarriers or groups of subcarriers. In this paper we study a codeword based power loading strategy combined with precoding. In this new technique the power allocation is done jointly across the subcarriers spanned by the encoded transmissions to optimise some codeword-related performance requirement. Simulations show that this strategy offers performance gain in terms of both packet error rate and throughput over conventional per-subcarrier power loading. The saving in computational complexity can also be significant, particularly for the MMSE precoder design.

In this letter, we introduce a novel method for demodulation of general amplitude- and phase-shift keying (APSK) constellations. By noting that the max-log log-likelihood ratio (LLR) is a piecewise linear function of the received signal, we devise a scheme to determine in which linear region that the received signal falls, obtain the linear parameters from a lookup table and compute the LLR. This method is general and works for any APSK (two-dimensional) constellation. By pruning the search tree, it also provides a mechanism for performance-complexity tradeoff and we show that its complexity is much lower than a previously published scheme (which only works for regular APSK constellations).

In this paper, we extend the concept of maximum-distance separable (MDS) array codes to a larger class of codes, where the array columns contain a variable number of data and parity symbols and the codewords cannot be arranged, in general, in a regular array structure with equal column length. These new codes, named irregular MDS array codes, find applications in problems of distributed data storage with multiple sources of information generating data at unequal rates. We solve the problem of finding optimal parity symbol allocations that achieve minimum redundancy for a given level of protection against block erasures. We provide a classification of irregular MDS array codes according to the parameters of their parity symbol allocation and we show how regular MDS array codes are a special case of this wider class. We derive necessary and sufficient conditions for such irregular array codes to be MDS and extend the concept of the lowest density generator matrix. Finally, we show how a simple constructive method allows to design irregular lowest density MDS array codes with alphabet size independent of the size of the array columns.

A simple but effective way of precoding signals in multiantenna OFDM systems using antenna subset selection (fewer RF chains than antennas) is per-tone antenna selection, which can be applied to the chosen subset. In this paper we study the performance of coded systems using this type of precoding and compare it to bulk selection and per-tone selection when all antennas are used. The diversity is the same for all three schemes but the analysis shows that the coding gain of the combined scheme is superior to the bulk selection and not much worse than per-tone selection. These conclusions are verified by simulations and show that bulk antenna selection with subsequent per-tone selection is an effective way of achieving good performance with limited cost.

We study the effect on diversity of using the sphere encoder-based vector perturbation (VP) technique of Peel and the lattice reduction-aided techniques (Babai approximations) of Windpassinger on a subset of the transmitted symbols. For a system withndiversity channels, we prove that the diversity order gap between the maximum diversity ofnachieved by the full-size VP and the minimum diversity of 1 provided by linear precoding can be bridged by applying VP to a group of transmitted symbols. In particular, we prove that the diversity order isn-l, wherelis the number of symbols that are not vector-perturbed. We show that this result holds true for lattice-reduction with both the rounding-off and nearest plane Babai approximations. The result shows that in terms of diversity, the group of vector-perturbed users counts as a single user. In fact, in a multiple-input and multiple-output Gaussian broadcast channel withllinearly precoded users, we can add up ton-lVP users and the diversity order for all users will decrease only by one, irrespective of how many VP users are added.

Antenna subset selection is a cost-effective way of exploiting the spatial dimension by only using a subset of the available antennas and thereby reducing the number of RF chains. A simple but effective way of precoding signals in multiantenna OFDM systems using antenna subset selection is per-tone antenna selection, which can be applied to the chosen subset. In a TDD system, estimates of the strength of the antennas can easily be obtained from the uplink and then used for the downlink precoding. However if only a subset of the antennas is active, no information is available for the remaining antennas; this may result in poor downlink performance. In this paper we propose a simple modification to the uplink reception which allows channel sounding of inactive antennas; this will aid the per-tone selection process in the downlink. With simulations we show that this scheme outperforms other alternatives, such as switch-and-stay.

Multiuser MIMO vector perturbation precoding facilitates multiple representations of the transmit symbols in order to improve transmit power normalization. When reference symbols (pilots) are considered, they must remain unperturbed. For conventional dedicated pilot schemes, where reference and data symbols are separated, the pilot vector is effectively linearly precoded, which degrades the performance. In this paper we propose a new pilot allocation scheme for OFDM systems, where reference and data symbols use the same time/frequency resource and are therefore precoded together to enable this we also present a means for enforcing a zero perturbation restriction upon the perturbation vector for the reference symbols. We support our findings by simulation results for zero-forcing precoding. It is shown that the linearly precoded conventional pilots severely degrade diversity of the system, which can be overcome by our proposed method.

If antenna selection in OFDM is done on a per-tone basis, the frequency domain can be exploited to achieve very good performance. However this means that the channel after antenna selection will have discontinuities in the frequency domain. In this paper we present a novel phase precoding that ensures that the channel after antenna selection is smooth enough in the frequency domain for advanced channel estimation. We analyse its performance and show that it is robust to channel estimation and reciprocity errors.

This paper presents a novel method for overlaying an analogue value on top of a digital signal. By adding a phase rotation and a signature sequence to a block of digitally modulated data symbols, this analogue value can be conveyed to a receiver without significant degradation to the underlying digital data. A near-ML estimator is derived which is non-data driven and hence independent of the digital data sequence. It is shown that this method of signalling a value is superior to quantising it and adding it to the digital data stream. We analyse the performance of the analogue and digital methods and also compare them using simulations.

The linear minimum mean-squared error (MSE) channel estimator for systems employing per-subcarrier transmit antenna selection is developed. Frequency domain correlations after the selection process are shown to be approximated well using a simple function, which makes near-optimal channel estimation practically possible. However, the resultant estimators are not robust to errors in the assumed model, in terms of the antenna-to-subcarrier assignment used at transmission, as we motivate both analytically and via simulations. This is an issue when the channels for conveying this information to the receiver are severely constrained. We present windowed channel estimations and new robust estimation algorithms, either individually or in combination, as solutions to counter this sensitivity. The robust estimators are developed based on the principles of 1) minimising the worst case MSE over the set of possible models and 2) minimising the expected MSE over the set of possible models. The latter estimator is preferred due to the lower implementation complexity and better MSE performance. We also prove theoretical asymptotic (in signal-to-noise ratio) performances of the two estimators used with the proposed correlation model. Simulation results illustrate the performance gains and improved robustness offered by the developed estimators.

This paper presents a novel VLSI implementation of a MIMO detector for OFDM systems. The proposed architecture is able to perform both linear MMSE and reduced lattice-aided MIMO detection, making it possible to adjust the balance between performance and power consumption. In order to facilitate real-time detection in reduced lattice mode of operation, a novel fixed-complexity version of the LLL lattice reduction algorithm has been developed, allowing for strict practical timing requirements, such as those specified for new generation IEEE 802.11n wireless LAN systems, to be met. An implementation of the MIMO detector for a system employing up to 4 transmit and receive antennas is described and its complexity and performance are evaluated.

Codebook-based limited feedback precoded MIMO systems involve vector quantization, which may require intensive computations, especially for large codebooks. In this paper we show how the complexity and codebook storage can be significantly reduced by using a special alphabet to generate the codebook. Simulation results show that the performance with the generated codebook is comparable with other codebooks which have higher complexity.

This paper analyses the estimation of residual frequency offset in MIMO OFDM systems using pilot subcarriers embedded in the data payload. A closed-form expression for the MSE is derived and it shows how the frequency and spatial correlation influence performance. It is also shown that an orthogonal design ensures robust performance, which is independent of the frequency correlation of the channel.

In an OFDM system, antenna selection can be performed on a per-subcarrier basis, which can exploit the frequency selectivity of the channel. However, this may cause power imbalance across the antennas, which may lead to problems with the power amplifiers (PA). To avoid this problem, we devise a scheme that allocates the same number of subcarriers to all antennas, while still offering substantial diversity gains. This is achieved by using integer optimization and we show that this is solvable by linear relaxation, which results in a reduced complexity. The constrained antenna selection is analyzed and it is shown that choosing the cost function as BER offers better diversity gains than using SNR. Simulations with a nonlinear PA show that the integer optimization scheme performs better than unconstrained antenna selection and a previously published ad hoc method.

This paper investigates the combination of beamforming and channel state information (CSI) smoothing for OFDM. While both can offer significant performance gains, care must be taken when using both of them at the same time. Two different sets of beamforming weights can produce the same beamforming gains but be unsuitable for CSI smoothing due to sudden changes in the frequency domain. By using orthogonal iterations to compute the beamforming weights, it is shown that a smooth beamformer is produced which is suitable for CSI smoothing at the receiver. An additional benefit of this technique is that the beamforming weights are easy to compute by exploiting the frequency correlation of the channel. The performance is evaluated for an OFDM system based on the IEEE 802.11n draft standard for which the gains are quantified. We also present results obtained from a hardware test bed, which verify the performance of the proposed smooth beamforming technique.

Ultra wide-band (UWB) devices conforming to the WiMediareg specification are set to become ubiquitous, with the adoption of WiMedia for wireless USB and Bluetooth 3.0. However, the performance of these devices at high data rates may be poor, as they operate at low signal powers and experience frequency selective fading. This paper therefore proposes a new transceiver design for improving the robustness and mean throughput when communicating with these devices. Performance is improved using multiple antennas, in conjunction with per subcarrier antenna selection based on channel state information at the transmitter. Legacy compliance is accomplished by using phase precoding to make transmissions appear to have originated from a single antenna system, albeit one with an associated channel that exhibits significantly reduced fading. Performance is benchmarked against legacy operation; and the impact of channel estimation and hardware calibration errors is investigated. Results show that, even with pessimistic assumptions about calibration errors, performance is improved dramatically.

Antenna selection can be an efficient way of exploiting spatial diversity while offering low feedback rates and simple receivers. In an OFDM system, antenna selection can be performed on a per-subcarrier basis, which can exploit the frequency-selectivity of the channel. However, this will cause a power imbalance across the antennas, which may lead to problems with the power amplifiers. An increased dynamic range may require power back-off, which results in a performance loss. To avoid this problem, we devise an optimum scheme that allocates the same number of subcarriers to all antennas, while still offering substantial diversity gains. This is achieved by using integer optimization. We show that this is solvable by linear relaxation, which results in a reduced complexity. Simulation results illustrate that the proposed scheme performs better than a previously published ad hoc method and only slightly worse than the impractical conventional per-subcarrier antenna selection where no subcarrier allocation constraints are imposed.

The multilevel structure of the N-QAM modulation constellations is exploited to significantly reduce the complexity of the sequential Gaussian approximation (SGA) algorithm for near optimal symbol detection in spatial multiplexing multiple- input multiple-output (MIMO) system. We propose two multilevel SGA algorithms (MSGA) which are based on depth- first search (DFS) and breadth-first search (BFS) respectively. Additionally, an important methodological contribution to this multilevel technique is proposed where the mismatch between the pseudo symbols and the true symbols is taken into consideration for the computation of posterior probabilities of symbol combinations. We justify this from a theoretical perspective as well as with numerical results. Simulation results show that the performance of the two proposed multilevel algorithms can approach that of the optimal a posteriori probability (APP) detector while its total computation cost is at most 81% and 48% of that of the original SGA algorithm for 16QAM and 64QAM modulation MIMO systems with 4 transmit/receive antennas respectively.

This paper investigates an efficient way of computing the beamforming weights for a MIMO OFDM system. It is shown that computing the weights incrementally across correlated channels can significantly reduce the complexity. An additional benefit of this technique is that it provides a "smooth" beamformer, i.e., it does not significantly change the correlation of the beamformed channel, which makes it suitable for advanced channel estimation techniques. We evaluate its performance in the OFDM-based IEEE802.11n system, where the computations are done across the subcarriers, and show that significant gains from channel smoothing at the receiver are possible due to the smooth beamformer. We also show that linear interpolation of beamforming matrices between subcarriers can be used, which only has a small degradation compared to optimal interpolation but a much simple implementation.

Lattice reduction (LR) aided MIMO detection has been shown to provide near-optimal hard outputs. However soft outputs are required in practical systems to fully exploit gains from forward error correction. Due to the non-linear nature of LR aided MIMO detection, generating soft information on the estimates it provides is not trivial. Consequently this problem has not been treated much in the literature. In this paper we present two related algorithms for computing soft outputs on estimates of LR-aided MIMO detection. It is shown through computer simulations that the proposed soft output generation algorithms allow LR-aided MIMO detection to achieve near- optimal performance for coded systems.

This paper investigates a blind MIMO precoding technique and benchmarks its performance in a realistic environment. The use of pseudo-random unitary precoding matrices (called "channel scrambling" in this paper) has been proposed to combat quasi- static channels with low diversity, both for the purpose of diversity coding and spatial multiplexing. In this paper we analyse the problem of poor soft output when the channel matrix is almost singular and how channel scrambling can help.The benefits of channel scrambling are quantified in a ray-traced channel and it is shown that substantial gains can be obtained from this simple technique.

This paper analyses lattice reduction for MIMO decoding and investigates how different parameters affect the complexity. Lattice reduction is a promising technique and a common approach to implementing it is to use the LLL algorithm. In this paper we show how the complexity of the LLL algorithm depends on such parameters as the number of antennas, SNR, channel correlation and channel matrix column sorting. Both real- and complex-valued versions are considered and complexity-performance trade-offs are also studied.

A novel Gaussian approximation based mixture reduction algorithm is proposed for semi-blind joint channel tracking and symbol detection for spatial multiplexing multiple-input multiple-output (MIMO) systems with frequency-flat time-selective channels. The proposed algorithm is based on a modified sequential Gaussian approximation detector (SGA) which takes into account channel uncertainty, and the first order generalized pseudo-Bayesian (GPB1) channel estimator. Simulation results show that the proposed algorithm performs better than the conventional and computationally expensive decision-directed method with Kalman filter based channel estimation and a posteriori probability (APP) symbol detection.

This paper analyses the estimation of residual frequency offset in MIMO OFDM systems using pilot subcarriers. A closed-form expression for the MSE is derived and this is used to study the pilot design problem. An orthogonal design is proposed and simulation results show that this is superior to using the same pilots on all pilot subcarriers

This paper investigates iterative channel estimation and symbol detection for spatial multiplexing multiple input multiple output (MIMO) systems with frequency flat block fading channels using the expectation-maximization (EM) algorithm. The maximum likelihood (ML) estimation of the MIMO channels via the EM algorithm requires the computation of the posterior mean and covariance of transmit symbol vectors which involve an exhaustive search of all possible symbol combinations and are computationally prohibitive for large systems. However, most of the symbol combinations contribute very little to the estimation. Therefore, we suggest that sequential Gaussian approximation (SGA) algorithm can be used to identify the M most significant symbol combinations and we can approximate the mean and covariance based on those symbol combinations. Simulation results are provided to illustrate the proposed algorithm.

Link adaptation is used to dynamically adjust the modulation and coding in a wireless system to reflect the characteristics of the wireless link and maximise throughput. Usually the SNR is used as a channel quality indicator but in MIMO systems, the interference between subchannels must be taken into account for efficient link adaptation. In this paper we propose a simple, low complexity technique which uses the soft output from the space-time decoder to perform link adaptation. A lookup table is created off-line and by estimating the channel quality, the most appropriate transmission mode can be selected. A comparison between this new technique and using SNR and capacity as channel quality indicators is presented. Numerical results are given for an OFDM system

This paper presents an analysis of frequency-offset estimation in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Tracking of the frequency offset between a transmitter and a receiver is often aided by transmitting pilots embedded in the data payload of a packet. The extension to multiple-antenna OFDM systems means that spatial beamforming will occur when transmitting pilots from all antennas simultaneously, which can reduce the robustness of frequency-offset estimation. This paper presents a thorough analysis of the spatial beamforming problem, and shows how the spatial correlation of the MIMO channel impacts performance. Simulation results are presented for both synthetic and measured channels, which agree well with the theoretical results

Single-carrier (SC) wireless communication systems generally require knowledge of the channel and the variance of the additive noise process to equalize a received message. Obtaining this information can be straightforward in stationary environments; however, these parameters constantly change in mobile environments. In this paper, we propose novel algorithms for estimating and tracking the channel and noise variance in SC systems by exploiting a unique word (UW) extension. These UW-based algorithms benefit from low complexity and lend themselves to SC systems employing frequency-domain equalization at the receiver

In MIMO OFDM systems, the quality of channel estimation becomes important, especially for a large number of antennas. On the other hand, it is desirable to minimise the overhead, making the design of training sequences a crucial task. Although this problem has been solved efficiently for OFDM systems using all subcarriers, it is still an open question for systems with nulled subcarriers (such as IEEE802.11a and IEEE802.11n), where some of the subcarriers are left unmodulated in order to fit specified spectrum masks. In this paper we present a numerical method for designing MIMO OFDM training sequences for arbitrary systems using nulled subcarriers. It is shown that substantial gains can be had over simple training sequences, such as only transmitting on non-overlapping subcarriers. The performance of these new training sequences are measured in both mean-squared error and bit-error rate

This paper presents an analysis of frequency offset estimation in MIMO OFDM systems. Tracking of frequency offsets is often aided by pilots transmitted in the data payload of a packet. The extension to multiple antenna OFDM systems means that spatial beamforming will occur when transmitting pilots from all antennas simultaneously, which can reduce the robustness of frequency offset estimation. We present a thorough analysis of the spatial beamforming problem and show how the spatial correlation of the MIMO channel impacts the performance. Simulation results are presented for measured channels recorded in an office environment, which agree well with the theoretical results.

On basis of the GSM/GPRS system, we investigate the application of the delay diversity scheme. The delay diversity scheme is a simple special case of a space-time trellis code (STTC). Delay diversity may be offered by network operators even in existing systems, since the standard is not affected at all. The performance improvements obtainable by means of this technique are demonstrated both on basis of analytical and simulation results. A lower bound on the bit error probability is derived and an optimization of the intrinsic delay parameter is considered.

On the basis of the GSM/EDGE system, the application of space-time coding techniques is investigated, especially with regard to aspects of compatibility with current specifications. In this paper, simple special cases of space-time trellis codes and space-time block codes are considered, namely the delay-diversity and the Alamouti scheme. The performance improvements obtainable by means of these techniques in the case of a typical urban (TU) wireless channel are demonstrated both on the basis of analytical and simulation results. For the delay-diversity scheme, a lower bound on the bit-error probability is derived and an optimization of the intrinsic delay parameter is considered. For the Alamouti scheme, a novel trellis-based soft-output equalization and detection algorithm is presented, which is of same complexity as in the single transmit antenna case. Moreover, in the case of the Alamouti scheme, appropriate training sequence pairs are required. In this context, optimized partner sequences with respect to the eight GSM sequences are introduced yielding pairs that significantly outperform any pair of GSM sequences. Furthermore, the sensitivity of the delay-diversity and the Alamouti scheme to a time-variant TU channel is addressed as well as to nonperfect knowledge of the channel coefficients at the receiver.

Substantial progress has been made in the receiver signal-processing algorithms for wireless communications to minimize the requirements on signal-to-noise (and/or interference) power ratio and computational complexities for the same quality of service. In cellular infrastructure systems, one of the key system design objectives in the base stations is to maximize the receiver sensitivity, so that the required signal level from the mobile stations can be minimized. The use of advance signal-processing algorithms, based on maximum a posteriori (MAP) estimation, iterative (turbo) channel estimation, equalization, and decoding, allows for a reduction of the required transmitter power by one-third to one-half. Lower computational complexities in the terminals, which implies a reduced power drain on the digital circuits, can be achieved by using techniques that adapt the state complexity of the receiver to the propagation channel. We give an in-depth review of these algorithms, and discuss their performance and implementation requirements.

In this paper we investigate source controlled channel decoding with a hard output channel decoder. Various methods have been devised in the past for source controlled channel decoding, but most of them assume that a soft output channel decoder is used. Most receivers in mobile wireless communications have a standard Viterbi channel decoder which produces only hard outputs. It is shown that a simple sliding histogram is capable of improving the speech quality significantly. The ideas and methods in this paper are applied for the full-rate and enhanced full-rate speech codecs in the GSM system.

We present and analyze low-rank channel estimators for orthogonal frequency-division multiplexing (OFDM) systems using the frequency correlation of the channel. Low-rank approximations based on the discrete Fourier transform (DFT) have been proposed, but these suffer from poor performance when the channel is not sample spaced. We apply the theory of optimal rank-reduction to linear minimum mean-squared error (LMMSE) estimators and show that these estimators, when using a fixed design, are robust to changes in channel correlation and signal-to-noise ratio (SNR). The performance is presented in terms of uncoded symbol-error rate (SER) for a system using 16-quadrature amplitude modulation (QAM).