Feasibility of the promising large intelligent surface (LIS) concept, as well as its scalability, relies on the use of low-cost hardware components, raising concerns about the effects of hardware distortion. We analyze LIS systems with receive-chain (RX-chain) hardware distortion, showing how it may limit performance gains when scaling up these systems. In particular, using the memory-less polynomial model, analytical expressions are derived for the signal to noise plus distortion ratio (SNDR) after applying maximum ratio combining (MRC). We also study the effect of back-off and automatic gain control on the RX-chains. The derived expressions enable us to evaluate the scalability of LIS when hardware impairments are present. The cost of assuming ideal hardware is further analyzed by quantifying the minimum scaling required to achieve the same performance with non-ideal hardware. The analytical expressions derived in this work are also used to propose practical antenna selection schemes for LIS, and we show that such schemes can improve the performance significantly leading to increased energy efficiency. Specifically, by turning off RX-chains with lower contribution to the post-MRC SNDR, we can reduce the energy consumption while maintaining performance. We also consider a more practical scenario where the LIS is deployed as a grid of multi-antenna panels, and we propose panel selection schemes to optimize the complexity-performance trade-offs and improve the system overall efficiency.

Large intelligent surface (LIS) has gained momentum as a potential 6G-enabling technology that expands the benefits of massive multiple-input multiple-output (MIMO). On the other hand, orthogonal space-division multiplexing (OSDM) may give a promising direction for efficient exploitation of the spatial resources, analogous as what is achieved with orthogonal frequency-division multiplexing (OFDM) in the frequency domain. To this end, we study how to enforce channel orthogonality in a panel-based LIS (P-LIS) scenario. Our proposed method consists of having a subset of active LIS-panels coherently serving a set of users, and another subset of LIS-panels operating in a novel low-power mode by implementing a receive and re-transmit (RRTx) process. This results in an inter-symbol interference (ISI) channel, where we characterize the RRTx processing required to achieve simultaneous orthogonality in time and space. We then employ the remaining degrees of freedom (DoFs) from the orthogonality constraint to minimize the RRTx processing power, where we derive a closed-form global minimizer, allowing for efficient implementation of the proposed scheme.

We envision a future in which multi-antenna technology effectively exploits the spatial domain as a set of non-interfering orthogonal resources, allowing for flexible resource allocation and efficient modulation/demodulation. We may refer to this paradigm as orthogonal space-division multiplexing (OSDM). On the other hand, reconfigurable intelligent surface (RIS) has emerged as a promising technology which allows shaping the propagation environment for improved performance. This paper studies the ability of three extended types of reconfigurable surface (RS), including the recently proposed beyond diagonal RIS (BD-RIS), to achieve perfectly orthogonal channels in a general multi-user multiple-input multiple-output (MU-MIMO) scenario. We consider practical implementations for the three types of RS consisting of passive components, and obtain the corresponding restrictions on their reconfigurability. We then use these restrictions to derive closed-form conditions and explicit expressions for achieving arbitrary (orthogonal) channels. We also study the problem of exploiting the degrees of freedom (DoFs) from the channel orthogonality constraint to maximize the channel gain while maintaining the passive RS constraints, and we propose some initial methods with satisfying performance. Finally, we provide some channel estimation and RS configuration techniques within this framework, where the computations are assumed to be performed at the BS, and we derive some limits on the amount of overhead required to achieve channel orthogonalization with RSs. The numerical results confirm the theoretical findings, showing that channel orthogonality with passive RSs can be effectively achieved in practical environments as long as the direct channel is not significant with respect to the RS cascaded channel. We thus take some important steps towards realizing OSDM.

Battery-free IoT devices are becoming increasingly interesting for environmental and practical reasons, with ambient backscatter as one of the candidate technologies. We present a class of balanced transmission patterns for ambient backscatter communication (BSC) and evaluate their performance under different signal-to-interference ratios and fading conditions. These patterns allow for bit synchronization, are resilient to high interference levels and support multiple access. In addition to these advantages, these BSC patterns also lead to optimal or near-optimal detectors of low-complexity that do not need channel state information or detection threshold estimation. The evaluation results show that using this design, our backscatter system becomes more resilient to unfavorable channel characteristics than comparable systems presented in the literature. In the extension, this means a more favorable trade-off between high data rates and better coverage.

We present a synchronized multisensory dataset for accurate and robust indoor localization: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes color images, corresponding depth maps, inertial measurement unit (IMU) readings, channel response between a 5G massive multiple-input and multiple-output (MIMO) testbed and user equipment, audio recorded by 12 microphones, and accurate six degrees of freedom (6DOF) pose ground truth of 0.5 mm. We synchronize these sensors to ensure that all data is recorded simultaneously. A camera, speaker, and transmit antenna are placed on top of a slowly moving service robot, and 89 trajectories are recorded. Each trajectory includes 20 to 50 seconds of recorded sensor data and ground truth labels. Data from different sensors can be used separately or jointly to perform localization tasks, and data from the motion capture (mocap) system is used to verify the results obtained by the localization algorithms. The main aim of this dataset is to enable research on sensor fusion with the most commonly used sensors for localization tasks. Moreover, the full dataset or some parts of it can also be used for other research areas such as channel estimation, image classification, etc. Our dataset is available at: https://github.com/ilaydayaman/LuViRA_Dataset

In this paper, we study the trade-off between complexity and performance in massive MIMO systems with neural-network based digital pre-distortion (NN-DPD) blocks at the base station. In particular, we consider a multi-user massive MIMO system with per-antenna NN-DPDs, each with an adjustable NN architecture in terms of the size and the number of NN hidden layers. We first analyze the system performance in terms of compensation of the non-linear hardware distortion for different levels of NN-DPD complexity and the number of antennas. We illustrate the required level of complexity in the trained NN-DPD blocks to approach the performance of an ideal conventional DPD. The statistics of the signal to interference and noise plus distortion ratio for a randomly located UE are selected as the performance metrics. We then assume a limited total digital computation power to be allocated among the NN-DPD blocks and propose to select the NN-DPD architecture of each TX branch based on the channel conditions of its corresponding antenna. To illustrate the importance of such a smart DPD resource allocation, we have analyzed the performance of a massive MIMO system with different NN-DPD architecture selection strategies. Numerical results indicate that by adopting the smart NN-DPD resource allocation, a significant boost in the system performance can be achieved, making room for reducing the overall system cost when scaling a massive MIMO system.

This paper presents LuMaMi28, a real-time 28 GHz multi-user (MU) multiple-input multiple-output (MIMO) testbed. In this testbed, the base station has 16 transceiver chains with a fully-digital beamforming architecture (with different pre-coding algorithms) and simultaneously supports multiple user equipments (UEs) with spatial multiplexing. The UEs are equipped with a beam-switchable antenna array for real-time antenna selection where the one with the highest channel magnitude, out of four pre-defined beams, is selected. For the beam-switchable antenna array, we consider two kinds of UE antennas, with different beam-width and different peak-gain. Based on this testbed, we provide measurement results for millimeter-wave (mmWave) MU-MIMO performance in different real-life scenarios with static and mobile UEs. We explore the potential benefit of the mmWave MU-MIMO systems with antenna selection based on measured channel data, and discuss the performance results through real-time measurements.

Peak to average power (PAPR) reduction of OFDM signals is critical in order to improve power amplifier (PA) efficiency in base stations. For massive MIMO, the complexity of these methods can become a real bottleneck in implementing low power digital signal processing chains. In this work, we consider an antenna reservation technique, which uses a low complexity clipping method to reduce signal peaks and leverages the benefit of massive antennas, by reserving a subset of antennas in order to compensate for the clipping distortion. Reserving antennas on the other hand reduces the potential array gain in the massive MIMO system, complicating the application of antenna reservation. This work explores various design space parameters in antenna reservation such as number of reserved antennas, amount of peak reduction and clipping methods. We investigate the impact of these parameters on the error vector magnitude at the user and on the adjacent channel power ratio at both transmitter and user positions. Our results enable a deeper understanding of antenna reservation as a low complexity PAPR reduction method in massive MIMO systems.

Spatially coupled serially concatenated codes (SC-SCCs) are constructed by coupling several classical turbo-like component codes. The resulting spatially coupled codes provide a close-to-capacity performance and low error floor, which have attracted a lot of interest in the past few years. The aim of this paper is to perform a comprehensive design space exploration to reveal different aspects of SC-SCCs, which is missing in the literature. More specifically, we investigate the effect of block length, coupling memory, decoding window size, and number of iterations on the decoding performance, complexity, latency, and throughput of SC-SCCs. To this end, we propose two decoding algorithms for the SC-SCCs:block-wiseandwindow-wisedecoders. For these, we present VLSI architectural templates and explore them based on building blocks implemented in 12nm FinFET technology. Linking architectural templates with the new algorithms, we demonstrate various tradeoffs between throughput, silicon area, latency, and decoding performance.

Phase-noise (PN) estimation and compensation are crucial in millimeter-wave (mmWave) communication systems to achieve high reliability. The PN estimation, however, suffers from high computational complexity due to its fundamental characteristics, such as spectral spreading and fast-varying fluctuations. In this paper, we propose a new framework for low-complexity PN compensation in orthogonal frequency-division multiplexing systems. The proposed framework also includes a pilot allocation strategy to minimize its overhead. The key ideas are to exploit the coherence bandwidth of mmWave systems and to approximate the actual PN spectrum with its dominant components, resulting in a non-iterative solution by using linear minimum mean squared-error estimation. The proposed method obtains a reduction of more than$2.5 \times $in total complexity, as compared to the existing methods. Furthermore, we derive closed-form expressions for normalized mean squared-errors (NMSEs) as a function of critical system parameters, which help in understanding the NMSE behavior in low and high signal-to-noise ratio regimes. Lastly, we study a trade-off between performance and pilot-overhead to provide insight into an appropriate approximation of the PN spectrum.

Large intelligent surface (LIS) is a technology that extends massive MIMO by considering an even greater number of antennas distributed throughout vast areas. In order to be able to implement this technology, it is crucial to consider decentralized architectures so as to make the whole system scalable. We consider a LIS divided into several LIS panels of smaller size, which can be located far away from each other. We present a modular architecture that allows combining different LIS panels using a binary tree. This architecture is also valid in a cell-free massive MIMO scenario. We make use of a newly defined matrix decomposition, the WAX decomposition, to define the modules that are used within our architecture. We also study the lossless dimensionality reduction in the data to be processed, which can be achieved using our proposed architecture.

In this paper, we study the effect of optimizing the per-antenna digital predistortion (DPD) sizes on the performance of the downlink massive MIMO system with residual hardware impairment. In particular, we first quantify the capacity after applying some per-antenna DPD with different numbers of coefficients at each antenna and then maximize the capacity by finding optimum values of the per-antenna DPD sizes under a constraint on the total number of DPD coefficients in the system. Two closed-form sub-optimal solutions are derived and numerical examples illustrate that their performance is very close to the optimal solution. It is shown that when the channel large-scale gains have high variation over the transmitter array, which is the case in practical scenarios, using our proposed optimized DPD sizes can improve the system capacity significantly. This allows us to scale down the massive MIMO system but still maintain performance. We also study the asymptotic behavior of the derived capacity and show that by using the optimized DPD sizes, the system performance can approach the asymptotic bound with a significantly smaller number of antennas.

Spatially coupled serially concatenated codes (SC-SCCs) are a class of spatially coupled turbo-like codes, which have a close-to-capacity performance and low error floor. In this paper, we perform a comprehensive design space exploration, revealing different aspects of SC-SCCs and discussing various design trade-offs. In particular, we investigate the impact of coupling memory, block length, decoding window size, and number of iterations on the performance, complexity, and latency of SC-SCCs. As a result, we propose design guidelines to make the code design independent of the block length. By introducing a modified window decoding schedule, we are able to demonstrate that the block length and coupling memory can be exchanged flexibly without changing the latency and complexity of decoding and without performance loss. Thus, thanks to spatial coupling, a certain code strength and performance can be achieved by either a very small block length or a large one, while the complexity and latency are fixed. Moreover, our results show that using higher coupling memory with smaller blocks can even improve the performance without increasing the latency and complexity. For all considered cases we observe that the performance of SC-SCCs is improved with respect to the uncoupled ensembles for a fixed latency and complexity.

In this paper, we combine practically verified results from circuit theory with communication-theoretic laws. As a result, we obtain closed-form theoretical expressions linking fundamental system design and environment parameters with the power consumption of analog front ends (AFEs) for communication receivers. This collection of scaling laws and bounds is meant to serve as a theoretical reference for practical low power AFE design. We show how AFE power consumption scales with bandwidth,SNDR, andSIR. We build our analysis based on two well established power consumption studies and show that although they have different design approaches, they lead to the same scaling laws. The obtained scaling laws are subsequently used to derive relations between AFE power consumption and several other important communication system parameters, namely, digital modulation constellation size, symbol error probability, error control coding gain, and coding rate. Such relations, in turn, can be used when deciding which system design strategies to adopt for low-power applications. For instance, we show how AFE power scales with environment parameters if the performance is kept constant and we use these results to illustrate that adapting to fading fluctuations can theoretically reduce AFE power consumption by at least 20x.

With the potential to provide a clean break from massive multiple-input multiple-output, large intelligent surfaces (LISs) have recently received a thrust of research interest. Various proposals have been made in the literature to define the exact functionality of LISs, ranging from fully active to largely passive solutions. Nevertheless, almost all studies in the literature investigate the fundamental spectral efficiency performance of these architectures. In stark contrast, this paper investigates the implementation aspects of LISs. Using the fully active LIS as the basis of our exposition, we first present a rigorous discussion on the relative merits and disadvantages of possible implementation architectures from a radio-frequency circuits and real-time processing viewpoints. We then show that a distributed architecture based on a common module interfacing a smaller number of antennas can be scalable. To avoid severe losses with analog signal distribution, multiple common modules can be interconnected via a digital nearest-neighbor network. Furthermore, we show that with such a design, the maximum backplane throughput scales with the number of served user terminals, instead of the number of antennas across the surface. The discussions in the paper can serve as a guideline toward the real-time design and development of LISs.

Phase noise (PN) estimation and compensation is needed for millimeter-wave (mmWave) communication systems to achieve high data rates. Conventional approaches for PN suppression suffer from high computational complexity. To overcome this, we present a low-complexity fully-digital PN suppression for mmWave systems. The key ideas are to exploit the coherence bandwidth of a mmWave system and an approximation of the PN spectrum, based on its Npdominant components. Utilizing these features, the joint estimation problem of PN and channel can be reformulated into a system with the same number of equations and unknowns, which enables low-complexity PN suppression by using a linear minimum mean square error (LMMSE) estimator. Furthermore, we propose a method to reduce the hardware cost of the LMMSE estimator by using a B-bit signal-to-noise ratio (SNR) quantization, which has a very low complexity of O(Np2+ NNp), where N is the number of subcarriers in an OFDM symbol. As a proof-of-concept, a low-cost VLSI architecture is presented to realize the proposed method. The proposed architecture in 28 nm CMOS (post-synthesis) results in an area cost of 258 K gate count and power consumption of 19.3 mW at 250 MHz clock rate.

The use of ultra-low power wake-up receivers (WuRx) can significantly reduce idle listening energy cost. To tailor a WuRx scheme to orthogonal frequency division multiplexing (OFDM) based systems, such as LTE-MTC or IEEE 802.11, the wake-up signal (WUS) also needs to follow OFDM principles to avoid interfering with other transmissions in the same shared bandwidth. Here, we address this particular issue and propose an approach where the OFDM transmitter is modified to also transmit WUSs designed for non-coherent low-power WuRxs, on a subset of the carriers, thus embedding the WUS correctly and ensuring orthogonality. The approach is evaluated for different system parameters and its applicability across a wide range of OFDM-based systems is verified.

This paper presents an angular-domain massive MIMO detector which exploits sparsity in massive MIMO channel along with a reconfigurable systolic array architecture to achieve high area efficiency. The underlying idea is to perform signal detection in the angular domain, where the channel matrix can have much lower dimension due the limited number of dominant angles (of arrival and departure) of the wireless signal. Evaluated using the measured massive MIMO channel, the proposed method results in 40%-70% reduction in processing complexity and memory requirements compared to traditional antenna-domain detection. This complexity reduction enables extensive hardware reuse where all the operations of detection processing are mapped to a condensed reconfigurable systolic array. The angular-domain zero-forcing detector, which supports 128 base station antennas and 16 users is implemented in a 28 nm FD-SOI technology. Synthesis result shows that our design attains a throughput of 510 MSps with an area of 537 kG.

This paper presents a 16-lane, 16-bit complex application-specific instruction processor (ASIP) for baseband processing in massive multiple-input multiple-output (MIMO). The architecture utilizes a 3/4-way very large instruction word (VLIW) with highly efficient pre- and post-processing units specifically trimmed for massive MIMO requirements. Architecture optimizations include features like single cycle vector-dot-product, vector indexing and broadcasting, hardware loops and full complex accumulator to provide high performance for various massive MIMO algorithms. Moreover, the ASIP is fully C-programmable, which is crucial for adapting to the evolving 5G standard. In our evaluation, a full massive MIMO up-link detection is executed in ≈11k clock cycles while synthesis results in ST 28 nm FD-SOI suggest a clock frequency of 900 MHz equating in a detection throughput of 330 Mb/s for a 128×16 massive MIMO system.

In this paper we present fully decentralized methods for calculating an approximate zero-forzing (ZF) equalizer in a large intelligent surface (LIS). A LIS is intended for wireless communication and facilitates unprecedented MU- MIMO performance, far superior to that of Massive MIMO. Antenna modules in the grid connect to their neighbors to exchange messages of information needed for interference cancellation in a fully-decentralized fashion, making the system scalable. By a careful design of how the messages are routed, we show that the proposed method is able to cancel inter-user interference sufficiently well without any centralized coordination, opening the door for the realization of this type of structures.

With increasing number of wirelessly connected devices having limited energy resources, energy efficient solutions become more and more important. Many studies have addressed schemes where low-power wake-up receivers are used to improve energy efficiency in networks with low traffic intensity (per node) and demands on availability. One such scheme is the duty-cycled wake-up receiver medium access (DCW-MAC) scheme, for which we have detailed understanding of achievable energy savings. In this paper we combine these with recent bounds on the characteristics of optimal low-power receiver designs. What we find is that network energy savings are always achievable with optimally designed wake-up receivers. Moreover, potential network energy savings increase as wake-up receiver power consumption decreases, despite the associated degradation in performance. The lowest network energy consumption, for this type of network, is reached for wake-up receivers pushed to the limit of functioning.

In this paper, we consider capacity degradations stemming from potential hardware impairments (HWI) of newly proposed Large Intelligent Surface (LIS) systems. Without HWI, the utility of surface-area (the first-order derivative of the capacity with respect to surface-area) is shown to be proportional to the inverse of it. With HWI, the capacity as well as the utility of surface-area are both degraded, due to a higher effective noise level caused by the HWI. After first modeling the HWI in a general form, we derive the effective noise density and the decrement of utility in closed-forms. With those the impacts of increasing the surface-area can be clearly seen. One interesting but also natural outcome is that both the capacity and utility can be decreased when increasing the surface-area in the cases with severe HWI. The turning points where the capacity and the utility start to decrease with HWI can be evaluated from the derived formulas for them. Further, we also consider distributed implementations of a LIS system by splitting it into multiple small LIS-Units, where the impacts of HWI can be significantly suppressed due to a smaller surface-area of each unit.

We consider a multipair two-way relay communication network, where pairs of user devices exchange information via a relay system. The communication between users employs time division duplex, with all users transmitting simultaneously to relays in one time slot and relays sending the processed information to all users in the next time slot. The relay system consists of a large number of single antenna units that can form groups. Within each group, relays exchange channel state information (CSI), signals received in the uplink and signals intended for downlink transmission. On the other hand, per-group CSI and uplink/downlink signals (data) are not exchanged between groups, which perform the data processing completely independently. Assuming that the groups perform zero-forcing in both uplink and downlink, we derive a lower bound for the ergodic sumrate of the described system as a function of the relay group size. By close observation of this lower bound, it is concluded that the sumrate is essentially independent of group size when the group size is much larger than the number of user pairs. This indicates that a very large group of cooperating relays can be substituted by a number of smaller groups, without incurring any significant performance reduction. Moreover, this result implies that relay cooperation is more efficient (in terms of resources spent on cooperation) when several smaller relay groups are used in contrast to a single, large group.

In this paper, we consider a wireless communication system where a large intelligent surface (LIS) is deployed comprising a number of small and distributed LIS-Units. Each LIS-Unit has a separate signal process unit (SPU) and is connected to a central process unit (CPU) that coordinates the behaviors of all the LIS-Units. With such a LIS system, we consider the user assignments both for sum-rate and minimum user-rate maximizations. That is, assuming M LIS-Units deployed in the LIS system, the objective is to select K(K ≤ M) best LIS-Units to serve K autonomous users simultaneously. Based on the nice property of effective inter-user interference suppression of the LIS-Units, the optimal user assignments can be effectively found through classical linear assignment problems (LAPs) defined on a bipartite graph. To be specific, the optimal user assignment for sum-rate and user-rate maximizations can be solved by linear sum assignment problem (LSAP) and linear bottleneck assignment problem (LBAP), respectively. The elements of the cost matrix are constructed based on the received signal strength (RSS) measured at each of the M LIS-Units for all the K users. Numerical results show that, the proposed user assignments are close to optimal both under line-of-sight (LoS) and scattering environments.

Downlink beamforming in Massive multiple-input and multiple-output (MIMO) either relies on uplink pilot measurements-exploiting reciprocity and time-division duplexing operation, or on the use of a predetermined grid of beams with user equipments reporting their preferred beams, mostly in frequency-division duplexing operation. Massive MIMO in its originally conceived form uses the first strategy, with uplink pilots, whereas there is currently significant commercial interest in the second, grid-of-beams. It has been analytically shown that with isotropic scattering (independent Rayleigh fading) the first approach outperforms the second. Nevertheless, there remains controversy regarding their relative performance in practical channels. In this contribution, the performances of these two strategies are compared using measured channel data at 2.6 GHz.

An adaptive uplink detection scheme for a Massive MIMO (MaMi) base station serving up to 16 users is presented. Considering user distribution in a cell, selective matched filtering (MF) is proposed for non-interference limited users and a Cholesky decomposition (CD) based zero-forcing (ZF) detector is implemented for the remaining users. Channel conditions such as coherence bandwidth are exploited to lower computational complexity by interpolating CD outputs. Performance evaluations on measured MaMi channels indicate a reduction in computation count by 60 times with a less than 1 dB loss at an uncoded bit error rate of 10-3. For the CD, a reconfigurable processor optimized for 8×8 matrices with block decomposition extension to support up to 16×16 matrices is presented. Circuit level optimizations in 28 nm FD-SOI resulted in an energy of 1.4 nJ/CD at 400 MHz, and post-layout simulations indicate a 50% reduction in power dissipation when operating with the proposed interpolation based detection scheme compared to traditional ZF detection.

A low-latency and area-efficient FFT/IFFT scheme is presented. The main idea is to utilize OFDM guard bands to reduce the operation counts and processing time, which results in 42% latency reduction compared to the reported pipelined schemes. To realize this idea, a modified pipelined architecture and an efficient data scheduling scheme are proposed. Furthermore, the proposed architecture is scalable to different FFT sizes and is also reconfigurable to support a wide range of applications. A 2048-point FFT/IFFT processor based on the proposed scheme has been designed, resulting in 1200 clock cycles latency, which can address the low latency demand of massive MIMO systems. Synthesis results in a 28 nm CMOS technology show that proposed design attains a throughput of 1 GS/s when clocked at 500 MHz.

The first measured results for massive multiple-input, multiple-output (MIMO) performance in a line-of-sight (LOS) scenario with moderate mobility are presented, with 8 users served by a 100 antenna base Station (BS) at 3.7 GHz. When such a large number of channels dynamically change, the inherent propagation and processing delay has a critical relationship with the rate of change, as the use of outdated channel information can result in severe detection and precoding inaccuracies. For the downlink (DL) in particular, a time division duplex (TDD) configuration synonymous with massive MIMO deployments could mean only the uplink (UL) is usable in extreme cases. Therefore, it is of great interest to investigate the impact of mobility on massive MIMO performance and consider ways to combat the potential limitations. In a mobile scenario with moving cars and pedestrians, the correlation of the MIMO channel vector over time is inspected for vehicles moving up to 29km/h. For a 100 antenna system, it is found that the channel state information (CSI) update rate requirement may increase by 7 times when compared to an 8 antenna system, whilst the power control update rate could be decreased by at least 5 times relative to a single antenna system.

In this paper, we consider capacities of single-antenna terminals communicating to large antenna arrays that are deployed on surfaces. That is, the entire surface is used as an intelligent receiving antenna array. Under the condition that the surface area is sufficiently large, the received signal after matched-filtering (MF) can be well approximated by an intersymbol interference (ISI) channel where channel taps are closely related to a sinc function. Based on such an approximation, we have derived the capacities for both one-dimensional (terminals on a line) and high dimensional (terminals on a plane or in a cube) terminal-deployments. In particular, we analyze the normalized capacity C̅, measured in nats/s/Hz/m2, under the constraint that the transmit power per m2, P̅, is fixed. We show that when the user-density increases, the limit of C̅, achieved as the wavelength λ approaches 0, is P̅/(2N0) nats/s/Hz/m2, where N0is the spatial power spectral density (PSD) of noise. In addition, we also show that the number of signal dimensions is 2/λ per meter deployed surface for the one-dimensional case, and π/λ2per m2deployed surface for two and three dimensional terminal-deployments.

We consider the potential for positioning with a system where antenna arrays are deployed as a large intelligent surface (L'S). We derive Fisher-informations and Cramέr-Rao lower bounds (CRLB) in closed-form for terminals along the central perpendicular line (CPL) of the L'S for all three Cartesian dimensions. For terminals at positions other than the CPL, closed-form expressions for the Fisher-informations and CRLBs seem out of reach, and we alternatively provide approximations (in closed-form) which are shown to be very accurate. We also show that under mild conditions, the CRLBs in general decrease quadratically in the surface-area for both the and dimensions. For the -dimension (distance from the L'S), the CRLB decreases linearly in the surface-area when terminals are along the CPL. However, when terminals move away from the CPL, the CRLB is dramatically increased and then also decreases quadratically in the surface-area. We also extensively discuss the impact of different deployments (centralized and distributed) of the L'S.

Employing a large number of antennas at the base station, massive MIMO significantly improves spectral efficiency and transmit power efficiency. On the other hand, massive MIMO also introduces unprecedented implementation challenges, especially in terms of processing and storage of large-size channel state information (CSI) matrices. Since on-chip memory is generally very expensive and has limited storage capacity, this paper uses the concept of on-chip CSI data compression and decompression to reduce memory requirements during baseband processing. To achieve this, massive MIMO channel properties are explored using a hardware-friendly DFT-based compression algorithm. The proposed method is evaluated with measured channel data at 2.6 GHz using a 128-antenna linear array. Simulation results show that aggressive CSI compression can be adopted without significant loss in communication performance, while the DFT-based compression can be conveniently integrated into the on-chip memory. This enables a large reduction of required on-chip memory, with negligible hardware overhead for compression/decompression.

The first measured results for massive multiple-input, multiple-output (MIMO) performance in a line-of-sight scenario with moderate mobility are presented, with eight users served in real time using a 100-antenna base station at 3.7 GHz. When such a large number of channels dynamically change, the inherent propagation and processing delay has a critical relationship with the rate of change, as the use of outdated channel information can result in severe detection and precoding inaccuracies. For the downlink (DL) in particular, a time-division duplex configuration synonymous with massive MIMO deployments could mean only the uplink (UL) is usable in extreme cases. Therefore, it is of great interest to investigate the impact of mobility on massive MIMO performance and consider ways to combat the potential limitations. In a mobile scenario with moving cars and pedestrians, the massive MIMO channel is sampled across many points in space to build a picture of the overall user orthogonality, and the impact of both azimuth and elevation array configurations are considered. Temporal analysis is also conducted for vehicles moving up to 29 km/h and real-time bit-error rates for both the UL and DL without power control are presented. For a 100-antenna system, it is found that the channel state information update rate requirement may increase by seven times when compared with an eight-antenna system, whilst the power control update rate could be decreased by at least five times relative to a single antenna system.

In sensor network applications with low traffic intensity, idle channel listening is one of the main sources of energy waste. The use of a dedicated low-power wake-up receiver (WRx), which utilizes duty-cycled channel listening, can significantly reduce the idle listening energy cost. Extreme low-power design typically leads to performance losses, indirectly increasing energy costs. Striking the right balance is, therefore, very important when introducing WRxs. We present a system analysis and heuristic parameter optimization for an existing duty-cycled WRx medium access control scheme. First, we introduce a framework for analysis of energy consumption and delay of an entire single-hop network where we include WRx characteristics. The WRx characteristics are condensed into two parameters: reduction in power consumption and associated loss of performance, compared with the main receiver. The analysis framework is used to find optimal wake-up beacon and protocol parameters for different WRx characteristics to minimize the total network energy consumption per data packet. The importance of the optimization is that it provides information on if, and how much, we can save in terms of energy by introducing a WRx with a certain characteristic. We also present accurate approximations of both optimal energy savings and resulting delays.

This paper presents a mutual coupling-based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An expectation-maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms the current state-of-the-art narrow-band calibration schemes in a mean squared error and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.

In massive MIMO performance evaluations it is often assumed that the terminal has a single antenna. The combination of multiple antennas in a terminal and massive MIMO precoding at the base station side can further improve overall system performance. We present measurement results for multi antenna terminals operating in different transmission schemes and how they perform under varying loading conditions. Gain expressions are derived that enable easy comparison between the transmission schemes. The evaluation is performed on realistic antennas integrated into Sony Xperia handsets tuned to 3.7 GHz and operated together with the Lund University massive MIMO (LuMaMi) test bed. It is concluded that the approach used in today's mobile systems, where up link and down link are addressed independently, will not provide the best performance. The performance can be improved by the selection of transmission schemes optimized for massive MIMO.

We investigate antenna correlation of an M-antenna massive multiple-input multiple-output (MIMO) setup with the purpose of obtaining a low-rank representation of the instantaneous massive MIMO channel. Low-rank representation bases using short-term and long-term antenna correlation statistics are defined, and their performance is evaluated with data sets obtained from channel measurements in both indoor and outdoor environments at 2.6 GHz. Our results indicate that the short-term bases can capture a larger amount of the channel energy compared to the long-term ones, but they have a limited timespan, one coherence time or less. On the other hand, the long-term bases are stable over time-spans of a few seconds. Hence, they can be obtained relatively easily. We also investigate a rank-p vector-scalar LMMSE channel estimator that exploits antenna correlation. Our results show that the investigated estimator can achieve a performance similar to that of full-rank LMMSE at a (2p + 1)/M times lower cost. The investigated estimator may be used in conjunction with estimators that exploit correlation in the frequency and time domains or, alternatively, in situations in which these estimators cannot be used, e.g., when pilot separation is larger than the channel coherence bandwidth or time.

Optimal data detection of data transmitted over a linear channel can always be implemented through the Viterbi algorithm (VA). However, in many cases of interest the memory of the channel prohibits direct application of the VA. A popular and conceptually simple method in this case, studied since the early 1970s, is to first filter the received signal in order to shorten the memory of the channel, and then to apply a VA that operates with the shorter memory. We shall refer to this general concept as a channel shortening (CS) receiver. Although studied for almost four decades, an information theoretic understanding of what such a simple receiver solution is actually doing is not available. In this paper, we show that an optimized CS receiver has a direct correspondance to the chain rule of mutual information. Furthermore, we show that the tools for analyzing the ensuing achievable rates from an optimized CS receiver are precisely the same as those used for analyzing the achievable rates of a minimum mean-square-error (MMSE) receiver.

Massive MIMO has been shown to greatly improve spectral and transmit-energy efficiency. When implementing a massive MIMO system, one challenge is high hardware complexity. A solution is to reduce the number of radio frequency (RF) transceiver chains by performing antenna selection. However, a full RF switch that connects the antennas and RF chains can be highly complex and incurs significant loss in output signal quality, especially when the number of antennas and RF chains are large. We therefore propose a simpler solution - binary switching architecture, which is suboptimal but provides better signal quality, as compared to the full switching network. To evaluate the proposed technique, we compare the sum-rate capacity when using several different configurations of binary switching with the performance of the full switching. Full MIMO performance obtained without antenna selection is also presented as a reference. The investigations in this paper are all based on measured channel data at 2.6 GHz, using a uniform linear array and a cylindrical array, both having 128 antenna elements. It is found that the proposed binary switching gives very competitive performance that are close to the full switching, for the measured channels. The results indicate a potential to simplify massive MIMO hardware by reducing the number of RF chains, and performing antenna selection with simple binary switching architecture.

Fully-synchronous measurements of a massive multi-user multiple-input multiple-output (MU-MIMO) radio propagation channel are presented. We evaluate the ability of a massive MIMO system to spatially separate users located close to each other in line-of-sight (LOS) propagation conditions. The system consists of a base-station (BS) antenna array equipped with 64 dual-polarized antenna elements (128 ports) arranged in a cylindrical configuration, and eight single-antenna users. The users are confined to a five-meter diameter circle and move randomly at pedestrian speeds. The BS antenna array is located on top of a 20 m tall building and has LOS to the users. We examine user separability by studying singular value spread of the MU-MIMO channel matrix for several subsets of BS antenna array ports, along with sum-rate capacity and achievable sum-rates with both zero-forcing and matched-filtering linear precoders. We also analyze the performance of the user with the lowest rate. Finally, a comparison between the performance offered by the massive MIMO system and that of a conventional MU-MIMO system is provided. To the best of our knowledge, this is the first report of fully-synchronous dynamic measurements of a massive MIMO system. Our investigation shows that even users located close to each other in LOS propagation conditions can be spatially separated in a massive MIMO system.

Correct estimation of symbol timing, Carrier Frequency Offset (CFO), and Signal-to-Noise Ratio (SNR) is crucial in Orthogonal Frequency Division Multiplexing (OFDM) communication. Typically, high estimation accuracy is desired, but often comes with increased complexity. Which has a direct repercussion in energy consumption. In this article, an architecture based on Sign-Bit estimation with low complexity, and hence low power dissipation, is presented. The architecture, is capable of estimating the afore-mentioned parameters in virtually any OFDM standard. The proof of concept has been fabricated in 65nm CMOS technology with low-power high-VT cells. Measurements performed with supply voltage of 1.2V. resulted in a power dissipation of 350 μW, 6 times smaller to that of an equivalent 8-bit architecture, and the lowest power density reported in literature.

This study describes a high throughput constant envelope (CE) pre-coder for Massive MIMO systems. A large number of antennas (M), in the order of 100s, serve a relatively small number of users (K) simultaneously. The stringent amplitude constraint (only phase changes) in the CE scheme is motivated by the use of highly power-efficient non-linear RF power amplifiers. We propose a scheme that computes the CE signals to be transmitted based on box-constrained regression (coordinate-descent), with an O(2MK) complexity per iteration per user symbol. A highly scalable systolic architecture is implemented, where M Processing Elements (PEs) perform the pre-coding for a system with up to K=16 users. This systolic architecture results in a very high throughput of 500 Msamples/sec (at 500 MHz clock rate) with a gate count of 14K per PE in 65nm technology.

Reducing the complexity of decoding algorithms for LDPC codes is an important prerequisite for their practical implementation. In this work we propose a reduction of computational complexity targeting the highly reliable codeword bits and show that this approach can be seamlessly merged with the forced convergence scheme. We also show how the minimum achievable complexity of the resulting scheme for given performance constraints can be found by solving a constrained optimization problem, and successfully apply a gradient-descent based stochastic approximation (SA) method for solving this problem. The proposed methods are tested on LDPC codes from the IEEE 802.11n standard. Computational complexity reduction of 55% and a 75% reduction of memory access have been observed.

Massive MIMO can greatly increase both spectral and transmit-energy efficiency. This is achieved by allowing the number of antennas and RF chains to grow very large. However, the challenges include high system complexity and hardware energy consumption. Here we investigate the possibilities to reduce the required number of RF chains, by performing antenna selection. While this approach is not a very effective strategy for theoretical independent Rayleigh fading channels, a substantial reduction in the number of RF chains can be achieved for real massive MIMO channels, without significant performance loss. We evaluate antenna selection performance on measured channels at 2.6 GHz, using a linear and a cylindrical array, both having 128 elements. Sum-rate maximization is used as the criterion for antenna selection. A selection scheme based on convex optimization is nearly optimal and used as a benchmark. The achieved sum-rate is compared with that of a very simple scheme that selects the antennas with the highest received power. The power-based scheme gives performance close to the convex optimization scheme, for the measured channels. This observation indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF chains and performing antenna selection using simple algorithms.

Massive MIMO, also known as very-large MIMO or large-scale antenna systems, is a new technique that potentially can offer large network capacities in multi-user scenarios. With a massive MIMO system, we consider the case where a base station equipped with a large number of antenna elements simultaneously serves multiple single-antenna users in the same time-frequency resource. So far, investigations are mostly based on theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients, i.e., i.i.d. Rayleigh channels. Here, we investigate how massive MIMO performs in channels measured in real propagation environments. Channel measurements were performed at 2.6 GHz using a virtual uniform linear array (ULA), which has a physically large aperture, and a practical uniform cylindrical array (UCA), which is more compact in size, both having 128 antenna ports. Based on measurement data, we illustrate channel behavior of massive MIMO in three representative propagation conditions, and evaluate the corresponding performance. The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels. It is concluded that in real propagation environments we have characteristics that can allow for efficient use of massive MIMO, i.e., the theoretical advantages of this new technology can also be harvested in real channels.

This paper describes a hardware efficient linear precoder for Massive MIMO Base Stations (BSs) comprising a very large number of antennas, say, in the order of 100s, serving multiple users simultaneously. To avoid hardware demanding direct matrix inversions required for the Zero-Forcing (ZF) precoder, we use low complexity Neumann series based approximations. Furthermore, we propose a method to speed-up the convergence of the Neumann series by using tri-diagonal precondition matrices, which lowers the complexity even further. As a proof of concept a flexible VLSI architecture is presented with an implementation supporting matrix inversion of sizes up-to 16×16. In 65 nm CMOS, a throughput of 0.5M matrix inversions per sec is achieved at clock frequency of 420MHz with a 104K gate count.

This paper presents an energy-efficient sorted QR-decomposition (SQRD) processor for 3GPP LTE-Advanced (LTE-A) systems. The processor adopts a hybrid decomposition scheme to reduce computational complexity and provides a wide-range of performance-complexity trade-offs. Based on the energy distribution of spatial channels, it switches between the brute-force SQRD and a low-complexity group-sort QR-update strategy, which is proposed in this work to effectively utilize the LTE-A pilot pattern. As a proof of concept, a run-time reconfigurable vector processor is developed to efficiently implement this adaptive-switching QR decomposition algorithm. In a 65 nm CMOS technology, the proposed SQRD processor occupies 0.71mm2core area and has a throughput of up to 100MQRD/s. Compared to the brute-force approach, an energy reduction of 5 ~ 33% is achieved.

This paper presents a link adaptive processor to perform low-complexity channel estimation and QR decomposition (QRD) in Long Term Evolution-Advanced (LTE-A) receivers. The processor utilizes frequency domain correlation of the propagation channel to adaptively avoid unnecessary computations in the received signal processing, achieving significant complexity reduction with negligible performance loss. More specifically, a windowed Discrete Fourier transform (DFT) algorithm is used to detect channel conditions and to compute a minimum number of sparse subcarrier channel estimates required for low complexity linear QRD interpolation. Furthermore, the sparsity of subcarrier channel estimates can be adaptively changed to handle different channel conditions. Simulation results demonstrate a reduction of 40%-80% in computational complexity for different channel models specified in the LTE-A standard.

This paper presents a structured optimization framework for reducing the computational complexity of LDPC decoders. Subject to specified performance constraints and adaptive to environment conditions, the proposed framework leverages the adjustable performance-complexity tradeoffs of the decoder to deliver satisfying performance with minimum computational complexity. More specifically, two constraint scenarios are studied: the “good-enough” performance and “as-good-as-possible performance”. Moreover, we also investigate the effects of different degrees of freedom in performance-complexity tradeoff adjustments. The effectiveness of the proposed method has been verified by simulating a set of LDPC codes used in IEEE 802.11 and IEEE 802.16 standards. Computational complexity reductions of up to 35% have been observed.

The use of duty-cycled ultralow-power wake-up receivers (WRxs) can significantly extend a node lifetime in low-power sensor network applications. In the WRx design, both the low-power operation of the WRx and the wake-up beacon (WB) detection performance are of importance. We present a system-level analysis of a duty-cycled WRx design, including an analog front end, a digital baseband, the WB structure, and the resulting WB detection and false-alarm probabilities. We select a low-power WRx design with about two orders of magnitude lower power consumption than the main receiver. The associated cost is an increase in the raw bit error rate (BER), as compared with the main receiver, at the same received power level. To compensate, we use a WB structure that employs spreading. The WB structure leads us to an architecture for the digital baseband with high address-space scalability. We calculate closed-form expressions for detection and false-alarm probabilities. Using these, we analyze the impact of design parameters. The analytical framework is exemplified by the minimization of the WB transmit energy. For this particular optimization, we also show that the obtained results are valid for all transmission schemes with an exponential relationship between the signal-to-noise ratio and the BER, e.g., the binary orthogonal schemes with noncoherent detection used in many low-power applications.

In very-large multiple-input multiple-output (MIMO) systems, the base station (BS) is equipped with very large number of antennas as compared to previously considered systems. There are various advantages of increasing the number of antennas, and some schemes require handling large matrices for joint processing (pre-coding) at the BS. The dirty paper coding (DPC) is an optimal pre-coding scheme and has a very high complexity. However, with increasing number of BS antennas, linear pre-coding performance tends to that of the optimal DPC. Although linear pre-coding is less complex than DPC, there is a need to compute pseudo inverses of large matrices. In this paper we present a low complexity approximation of down-link Zero Forcing (ZF) linear pre-coding for very-large multi-user MIMO systems. Approximation using a Neumann series expansion is opted for inversion of matrices over traditional exact computations, by making use of special properties of the matrices, thereby reducing the cost of hardware. With this approximation of linear pre-coding, we can significantly reduce the computational complexity for large enough systems, i.e., where we have enough BS antenna elements. For the investigated case of 8 users, we obtain 90% of the full ZF sum rate, with lower computational complexity, when the number of BS antennas per user is about 20 or more.

This paper presents a highly parallelized MIMO signal detection algorithm targeting vector-based reconfigurable architectures. The detector achieves high data-level parallelism and near-ML performance by adopting a vector-architecture-friendly technique - parallel node perturbation. To further reduce the computational complexity, imbalanced node and successive partial node expansion schemes in conjunction with sorted QR decomposition are applied. The effectiveness of the proposed algorithm is evaluated by simulations performed on a simplified 4×4 MIMO LTE-A testbed and operation analysis. Compared to the K-Best detector and fixed-complexity sphere decoder (FSD), the number of visited nodes in the proposed algorithm is reduced by 15 and 1.9 times respectively, with less than 1 dB performance degradation. Benefiting from the fully deterministic non-iterative dataflow structure, reconfiguration rate is 95% less than that of the K-Best detector and 17% less than the case of FSD.

In this paper, a number of channel estimation algorithms for iterative receivers are compared for the case of an up-link orthogonal frequency division multiplexing interleave division multiple access (OFDM-IDMA) system. Both pilot based algorithms, used to obtain an initial estimate, as well as semi-blind decision-directed algorithms working as a component of the iterative receiver are considered. Algorithms performing either joint minimum mean square error (MMSE) channel estimation, or iterative estimation using space-alternating expectation maximization (SAGE), are evaluated. The considered algorithms differ in terms of complexity, as well as performance. The main contribution of this paper is to give an overview of different channel estimation approaches for OFDM-IDMA, where the complexity versus performance tradeoff is at the focal point. There is no single channel estimator providing the best tradeoff and our analysis shows how the system load (number of users) and the SNR influence the estimator choice.

This paper describes a novel approach to a Matched Pursuit LTE Channel Estimator. It is shown that the total complexity of the estimator can be reduced by, perhaps counter-intuitively, increasing the resolution of the estimator, since for certain choices of resolution a number of multiplication factors will be zero. An architecture is presented that implements the novel algorithm. By increasing the resolution from 2048 to 2400 points it is shown that the number of multiplications in the core unit is reduced by ~40% and the total complexity of the estimator by more than 10%. The RAM memory needed increase by ~17% since more values need to be stored, but the lookup table is reduced by ~71%.

Wireless communication using very-large multiple-input multiple-output (MIMO) antennas is a new research field, where base stations are equipped with a very large number of antennas as compared to previously considered systems. In theory, as the number of antennas increases, propagation properties that were random before start to become deterministic. Theoretical investigations with independent identically distributed (i.i.d.) complex Gaussian (Rayleigh fading) channels and unlimited number of antennas have been done, but in practice we need to know what benefits we can get from very large, but limited, number of antenna elements in realistic propagation environments. In this study we evaluate properties of measured residential-area channels, where the base station is equipped with 128 antenna ports. An important property to consider is the orthogonality between channels to different users, since this property tells us how advanced multi-user MIMO (MU-MIMO) pre-coding schemes we need in the downlink. We show that orthogonality improves with increasing number of antennas, but for two single-antenna users there is very little improvement beyond 20 antennas. We also evaluate sum-rate performance for two linear pre-coding schemes, zero-forcing (ZF) and minimum mean squared error (MMSE), as a function of the number of base station antennas. Already at 20 base station antennas these linear pre-coding schemes reach 98% of the optimal dirty-paper coding (DPC) capacity for the measured channels.

In this work we present a new low-power medium access scheme for sensor-type networks specifically with low traffic intensity. We call the proposed scheme DCW-MAC where ultra-low-power wake-up receivers are combined with optimal duty-cycled listening. First we introduce a framework for the analysis of energy consumption of the studied network type, then we use it to optimize the MAC scheme to achieve very low total energy consumption per transmitted data packet. It is shown that even with large sacrifices in terms of wake-up receiver detection performance, required to achieve ultra-low-power consumption with a limited form factor, we can achieve very competitive total energy consumption and outperform other MAC schemes for scenarios with low traffic.

In this paper a number of low-complexity channel estimator structures tailored to the needs of LTE terminals have been investigated. The focus has been on the algorithms exhibiting reduced complexity while providing an acceptable level of performance. To design these estimators other factors such as interoperability with another OFDM-based standard, DVB-H, in a multi-standard environment have been taken into account. The key underlying parameter has been exploiting the pilot pattern in LTE providing the possibility of minimal filtering in time domain while exhibiting an acceptable level of performance in fast fading environments.

In this paper we evaluate, by means of Extrinsic Information Transfer (EXIT) charts, an iterative receiver that has emerged as a promising candidate for non-coherent multi-user multi-antenna OFDM systems. The receiver performs parallel interference cancellation (followed by linear filtering) and channel estimation, using soft symbols obtained from a bank of single-user decoders. For the sake of conceptual clarity we study a system with two single antenna users and a receiver with two antennas, and we demonstrate how the convergence behavior of the receiver can be visualized using paired three dimensional EXIT surfaces. Our results show that the actual decoder trajectories obtained through simulations are well predicted from the EXIT charts. For the iterative receiver under investigation we identify a very specific problem with EXIT chart generation; the EXIT curve for the inner component decoder depends on the outer encoder. To handle this problem we propose a modification to the iterative receiver which solves the aforementioned problem; the performance degradation is demonstrated to be small.

This paper proposes a low-complexity two-dimensional channel estimator for MIMO-OFDM systems derived from a time-frequency variant channel estimator previously proposed. The estimator exploits both time and frequency correlations of the wireless channel via use of Slepian-basis expansions. The computational saving comes from replacing a two-dimensional Slepian-basis expansion with two serially-concatenated one-dimensional Slepian-basis expansions. Performance in terms of normalized mean square error (NMSE) vs. signal-to-noise ratio (SNR) have been analyzed via numerical simulations and compared with the original estimator. The analysis of the performance takes into account the impact of both system and channel parameters.

This paper presents an architecture of an SVD based channel estimator. A number of simplifications of the estimator are presented. These simplifications reduces the complexity of the estimator enough to allow for a hardware implementation. A system is defined, in which the channel estimator is to be used. It is shown that with the proposed pilot symbol pattern and the usage of the channel correlation properties the estimator will reduce the number of multiplications with 66 % compared to a brute force attempt. The hardware architecture of the channel estimator is also described. The estimator will have a throughput of 1/6th of the achievable clock speed and the word lengths are chosen such that there will be only a negligible increase in mean square error compared to the floating point case and still a 5 time improvement compared to the least square estimator.

This paper aims at validation of an iterative receiver for multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) systems using real-measurement channel data from an indoor scenario. The receiver performs iterative multi-user detection (MUD) and Channel Estimation (CE) via soft information from the single- user decoders. The channel measurements were performed for a dynamic dual MIMO link scenario. The case with two users with multiple antennas interfering each other is considered. CE at the receiver exploits the frequency correlation of the MIMO link. Simulation results for the performance are shown in terms of bit- error rate (BER) vs. signal-to-noise ratio (SNR). Performance for the whole system are provided and compared with respect to the case of perfect channel-state information (PCSI) at the receiver, as well as for the single user. We also provide an analysis of BER with respect to signal-to-interference ratio (SIR). CE performance are evaluated in terms of normalized mean square error (NMSE).

This paper represents the results of a simulation study on positioning of a mobile unit in MIMO settings. We used two different approaches for modeling the mobile movement, combined with a simple geometrical model for the MIMO channel. Three different particle filters were implemented for the position estimation. The results show that all three filters are able to achieve estimation accuracy required by Federal Communication Commission. The dimensionality of the particle filter state space is independent of the number of antenna elements, and it is possible to increase the number of antennas and use more sophisticated channel models without changing the filtering algorithms.

In this paper two simplified transform based estimators for MIMO OFDM systems using the DFT and an SVD based transform are compared over a tapped delay line channel model. In the resulting symbol error rate plots, it is seen that the DFT based estimator experiences an error floor caused by the mismatch between the discrete time model and a continuous time reality. This error floor becomes a problem at high SNR levels where high data-rate systems can be expected to operate. When using an SVD based estimator it is seen that this error floor is reduced at the cost of a somewhat increased estimator complexity

In this paper, measurement results are presented providing experimental support for the validity of a theoretical framework for analysis of heterogeneous systems of interfering packet radio networks. Specifically, an analytical Bluetooth interference model is considered in this paper, where calculated distributions of energy quantities from interfering Bluetooth networks are compared with distributions obtained from measurements. The measurements indicate that the closed form expressions for the distributions of the received interfering energy in the analytical model capture important mechanisms in real Bluetooth networks. It is also evident from the measurements that adjacent channel interference must be taken into account when analyzing interfering Bluetooth networks

While the use of radio technology for wireless data communications has increased rapidly, the wide variety of radio interfaces being used has made interference investigations hard to perform. With that in mind, we present a novel approach for analyzing packet radio communications, applicable to interfering heterogeneous networks, which leads to tractable analytical expressions. The core of the approach is an analytical framework modeling each network with individual properties for the packet types and the channel sets used, while taking path loss between nodes into account. Furthermore, we present a derivation of closed-form expressions for the throughput of the networks, thus allowing for the investigation of important mechanisms limiting network and system performance; the expressions enable fast and flexible analysis to be performed without extensive computer simulations or measurement campaigns. To illustrate the use of the framework and the strength of the closed-form expressions, we analyze a heterogeneous example system consisting of the IEEE 802.llb network and multiple Bluetooth networks that use multiple packet types. In the analysis, we also take the adjacent channel interference into account when calculating network throughput as functions of number of interferers in the system.

While the use of radio technology for wireless data communications has increased rapidly, the wide variety of radio interfaces being used has made interference investigations hard to perform. With that in mind, we present a novel approach for analyzing packet radio communications, applicable to interfering heterogeneous networks, which leads to tractable analytical expressions. The core of the approach is an analytical framework modeling each network with individual properties for the packet types and the channel sets used, while taking path loss between all network nodes into account. Furthermore, we present a derivation of closed-form expressions for the throughput of the networks, thus allowing for the investigation of important mechanisms limiting network and system performance. The expressions enable fast and flexible analysis to be performed without extensive computer simulations or measurement campaigns. To illustrate the use of the framework and the strength of the closed-form expressions, we analyze a heterogeneous example system consisting of one IEEE 802.11b network and multiple Bluetooth networks that use multiple packet types. In the analysis, we also take the adjacent channel interference into account when calculating network throughput as functions of the number of interferers in the system

In this paper, a novel peak-to-average power reduction approach for orthogonal frequency division multiplexing (OFDM) has been addressed. Two-dimensional pilot-symbol assisted modulation (2D-PSAM) is employed in coherent OFDM for channel estimation, and it is based on inserting known symbols spread throughout the 2D time-frequency grid. These pilot symbols are employed to simultaneously perform distortionless peak power reduction with a suboptimum technique named orthogonal pilot sequences (OPS), which reduces additional system complexity and side information compared to optimum pilot values. This proposal attains a further step over other previous works, since this set of sequences allows blind detection at the receiver without prior knowledge of any side information.

The increasing use of wireless technology utilizing unlicensed frequency bands calls for more in-depth analysis of interference and coexistence between systems. In this paper a framework is presented for detailed analysis of the performance of coexisting networks in shared frequency bands. The framework allows for multiple packet lengths to be used by the communicating devices and the analysis is performed with respect to the received interfering energy, which in effect leads to a link budget analysis on a packet basis. A system of interfering Bluetooth piconets is analyzed to illustrate the use of the framework and the conceptual difference in basing the analysis on link budgets, rather than on packet collisions. Furthermore, some indications on throughput saturation in the analyzed Bluetooth system are presented.

The increasing use of unlicensed frequency bands for radio communications calls for investigations of the interference between coexisting radio networks. The wide variety of radio interfaces makes such investigations hard to perform. We present a general framework for the analysis of heterogeneous interfering radio networks, where the networks are modeled with individual properties for the packet types used, transmitted power distributions in time and frequency from the packet transmissions, and with path loss between network nodes. By using closed form expressions for the throughput of the networks, important mechanisms limiting their performance can be investigated. The closed form expressions enable fast and flexible analysis to be performed without extensive computer simulations. To illustrate the use of the framework we analyze an example system of interfering IEEE 802.11b and Bluetooth networks.

With the increasing use of short-range wireless devices for high-data-rate communication in the shared frequency bands, the level of interference can be expected to increase. The ability to estimate the performance degradation of these devices due to increasing interference is, therefore, important. In this paper, the throughput of devices that perform frequency hops after each transmitted packet in order to achieve diversity is investigated. The system model allows for an analysis of systems where packets of variable durations are used, and the throughput derivation is based on the assumption that collisions result in a total loss of the data in the colliding packets. The resulting expression for the throughput is given as a function of the number of frequency channels used for frequency hopping, the number of interfering networks, the durations of the packet types available, and the probability of networks selecting a certain packet type for transmission. An approximation of the exact expression for the throughput is also derived, and the results are applied to an example system consisting of Bluetooth piconets.

The impact of the quantization of SNR measurements on the throughput of a multiuser diversity scheme for constant-rate transmission is investigated under a block-Rayleigh fading assumption. In the downlink, each user measures its SNR, quantizes it, and feeds it back to the transmitter, which transmits a packet to the user with the highest quantized SNR. In the case of several users having the same quantized SNR, one of them is selected at random. It is concluded that using only a few quantization levels can yield a throughput that is only slightly less than the throughput obtained by using unquantized feedback.

We analyze the channel capacity of multiple-input multiple-output (MIMO) systems in frequency selective channels, with channel knowledge at the transmitter side. An optimum transmission scheme uses Shannon's principle of water filling jointly in the frequency and the spatial domain. However, we show that the largest gain by using water filling resides in the spatial domain, and that the capacity gain from the frequency domain is very small for MIMO systems. We quantify the gain for a theoretical Rayleigh fading channel model and for microcellular channels as measured in an enclosed courtyard.

The impact of interference from Bluetooth networks on the throughput of IEEE 802.11 FHSS networks is investigated. This is done by deriving an analytical approximation of the throughput of slow frequency-hopping systems. The derivation in itself provides valuable insights into the mechanisms of interference between systems employing the frequency-hopping technique. In deriving the approximation, it is assumed that packet collisions result in total loss of all information contained in the packets involved in the collisions, regardless of the distance between the networks. The results indicate that the Bluetooth networks may have a negative effect on the throughput of an IEEE 802.11 network using long packet types.

A new peak-to-average power reduction approach for OFDM has been addressed. Two-dimensional pilot-symbol assisted modulation (2D-PSAM) is employed in coherent OFDM for channel estimation and it is based on inserting known symbols spread throughout the 2D time-frequency grid. We show that these scattered symbols can also be employed to perform distortionless peak power reduction. To reduce additional system complexity and side information, a finite set of orthogonal pilot sequences is proposed, and also blind detection can be performed without prior knowledge of redundancy at the receiver.

The performance of a turbo code can be severely degraded if no trellis termination is employed. This paper investigates the implications of the choice of trellis termination method for turbo codes, and explains the origin of the performance degradation often experienced without trellis termination. An efficient method to derive the distance spectrum of turbo codes for different trellis termination methods is presented. Further, we present interleaver design rules that are tailored to each termination method. Using interleavers designed with these restrictions, we demonstrate that the performance difference between various termination methods is very small, including no trellis termination at all. For example, we demonstrate a turbo code with a 500-bit interleaver that exhibits no sign of an error floor for frame error rates as low as 10/sup -8/, even though no trellis termination is employed.

A new frequency offset tracking approach for OFDM has been addressed. Two-dimensional pilot-symbol assisted modulation (2D-PSAM) is employed in coherent OFDM for channel estimation and it is based on inserting known symbols spread out through the 2D time-frequency grid. We show that these scattered symbols can also be employed to perform frequency synchronization. Maximum likelihood estimation has been derived for both time and frequency domains. Also, if a decision-guided strategy is used jointly with the 2D-PSAM signalling, an improvement in performance can be attained.

The performance of a turbo code is dependent on two properties of the code: its distance spectrum and its suitability to be iteratively decoded. Both these properties are dependent on the specific interleaver used in the turbo-coding scheme. This paper describes a strategy for interleaver design that includes a criterion for the performance of iterative decoding. This criterion is based upon correlation properties of the extrinsic information, which is used as a priori inputs to the constituent decoders. Simulations comparing interleaver choices indicate an improved turbo code performance for interleavers designed with algorithms including a correlation criterion.

It is essential to find suitable modes (combinations of alphabet sizes and code rates) for transmission in cellular systems. Bad choices result in low spectrum efficiency. An analytical method is proposed to find the modes which give the highest spectrum efficiency when using adaptive modulation. It is demonstrated that data rates considerably higher than 271 and 384 kbps are feasible in GSM and EDGE systems by using adaptive modulation. It is further demonstrated that the spectrum efficiency can be improved more than 100% by using three modes in a 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).

Combined linear-Viterbi equalizer (CLVE) is a term often used for a class of digital receivers reducing the complexity of the Viterbi detector by assuming an approximate channel model together with linear pre-equalization of the received data. The authors reconsider a weighted least squares design technique for CLVEs by introducing a minimax criterion for suppressing the strongest component of the residual intersymbol interference. Odling (1993) studied the performance of some proposed CLVE design methods and evaluated them by simulated bit error rates. The present authors investigate the performance of the minimax design and of the CLVE designs found in literature for two GSM test channels. They also present a comparison of the CLVE designs based on a common quadratic optimization criterion for the selection of the channel prefilter and the desired impulse response.>