Domino-tiled phased arrays (DTPA), based on two-element single polarized sub arrays, are conceived for beyond 100 GHz multi-user multiple-input multiple-output (MIMO) communication. Compared to lower frequencies, the small wave-length poses stringent requirements on the connections between the integrated circuit (IC) and antenna array, as the antenna dimensions are comparable to IC size. To address this problem, the tiling configuration of DTPAs is optimized by a genetic algorithm (GA) while jointly maximizing the spectral efficiency (SE) and reducing side lobe levels (SLLs), considering above 100 GHz channel models for multiple users. As a proof-of-concept, optimized DTPAs for an array size of 8 × 10 are realized in full-wave EM simulator through a printed circuit board (PCB) manufacturing process. Our optimized DTPAs yield more than 16% impedance bandwidth, targeting the [120–140] GHz D-band. Moreover, the optimized DTPAs reach a SE and SLL up to 3.7 b/s/Hz and -13 dB for a two-user scenario.

In this paper we study analytically the sensitivity of analog-beamformed multiuser downlink MIMO systems to imperfect beamforming (BF) state information at the transmitter and the receiver. We consider different distributions of the transmit and receive BF errors and evaluate the end-to-end performance in terms of the statistical distribution of the minimum signal-to-interference plus noise ratio (SINR) and derive accordingly, for small fluctuation errors, analytical approximation of the average SINR and the corresponding symbol error probability (SEP). The analytical approximations of the SEPs are further benchmarked with the simulated error performance for both uncoded and coded transmission and for two BF architectures namely partially (PCA) and fully (FCA) connected architectures. Simulation results in line-of-sight propagation revealed that (i) tight estimates mainly of the transmit BF angles are required in the order of a fraction of one degree for moderate number of transmit antennas especially for FCA, and (ii) the BF accuracy requirement becomes stringent as the system load increases. We argue that with a moderate to high number of transmit antennas, multiuser beam alignment achieving sub-degree BF accuracy will be one of the main challenges of future mmWave communication systems that rely on analog BF.11This research received the support of the COREnext project, funded by the European Union's Horizon Europe Research and Innovation Actions, under Grant Agreement$N^{\circ}l$01092598.

Millimeter-wave spectrum (30 to 300 GHz) is explored in order to provide higher throughput by exploiting the large bandwidth available. At those frequencies, multiple antenna systems are essential to combat severe path loss. Fully digital (FD) architectures, where each antenna connects to its own baseband chain, are considered the most energy-efficient at low frequencies while enabling multi-user multiplexing. However, unlike lower frequencies, channel propagation above 100 GHz is heavily line-of-sight dominated and the operating bandwidth is much larger which poses different constraints on signaling schemes and hardware components. The optimal beamforming architecture configuration is still an open problem above 100 GHz. To address this problem, we compare energy-efficient beamforming architectures for the D-band (110-170 GHz). We estimate the energy efficiency of future systems by following the technology trends in circuit implementation. We show that hybrid fully connected architecture is the most energy-efficient for the 7nm technology node and size-constrained antenna arrays. Hybrid partially connected architecture is the most energy-efficient for unconstrained antenna arrays. We show that FD architecture becomes energy-efficient for technology nodes better than 2nm.

Mm-wave spectrum (30 to 300 GHz) is explored in order to provide higher throughput by exploiting the large bandwidth available. Due to those higher frequencies, future systems are expected to have larger antenna arrays with more directive beams relying on line-of-sight propagation. Besides wider bandwidth, high-throughput systems also aim at serving multiple users in parallel. When those users are located at different ranges from the base station, they put very different requirements on the base station architecture and signals. This can translate, e.g., into architectures having different numbers of components and power consumption to optimally serve those users. Finding the architecture offering the best throughput vs. power consumption trade-off while serving multiple users at different distances is still an open problem in the literature. Typically, power optimization has been done considering a fixed operating distance and neglecting the user distribution which is crucial in future communication systems. To that end, we propose a novel range distribution aware (RDA) architecture dimensioning methodology that optimizes the power consumption of a transceiver architecture over the operating range by exploiting the user distribution. We show that the RDA design methodology can reduce the average power consumption of the transceiver by 20% in a typical scenario compared to traditional power optimization.

Beyond 5G systems are expected to approach 1 Tb/s throughput. This poses a significant challenge to the channel decoder. In this paper, we propose a multi-core architecture based on full row parallel layered LDPC decoder with frame interleaving. Compared with conventional partially parallel layered architectures, the proposed architecture increases the throughput by applying frame interleaving into the pipeline architecture and by using multi-core architectures. Two high rate medium size QC LDPC codes are designed with fast decoding convergence speed for this architecture. Both codes are implemented with single core and multi-core architectures to explore different trade-offs between code design, communication performance and implementation. The four decoders are implemented in 16 nm CMOS FinFET technology with a clock rate of 1 GHz. The placement and routing implementation results show that the single core decoder for the LDPC (1027, 856) code is able to provide 114 Gb/s throughput at maximum 3 iterations with an area of 0.173 mm2and energy efficiency of 1.56 pJ/bit; the multi-core decoder for the (1032, 860) code is able to provide 860 Gb/s throughput at maximum 2 iterations with an area of 1.48 mm2and energy efficiency of 3.24 pJ/bit. The multi-core decoder achieves the highest throughput in the literature for medium size (1-2k) LDPC codes. When compared with other state-of-the-art fully parallel high speed architectures, the proposed architectures bring a significant gain both in area efficiency and energy efficiency while keeping the ability to offer flexibility in code rate, number of iterations and early stop.

In order to provide an ever increasing throughput for emerging communication systems, more and more high-frequency bands are being considered, where a wide bandwidth is available, especially at mm-wave (30 to 300 GHz) to THz frequencies. Due to differences in propagation and underlying technology limitations, this leads to systems with different architecture from lower frequencies. In order to estimate the power consumption of such systems and to allow architectural and technology tradeoffs, a dedicated power model is proposed. It combines specifically the power consumption of the power amplifier (PA), analog, and digital components. It also includes overhead components such as power supply and the impact of technology scaling towards future CMOS and non-CMOS technologies.Dedicated models for the different components and their integration in the power model are described. The application of the power model is illustrated on two specific use cases. The first one investigates the trade-off between more antennas or more output power per PA for an access point. Power consumption is optimized for two different PA technologies. The second use case investigates extreme throughputs around 1 Tb/s, targeting fronthaul of future cellular systems. It illustrates the feasibility of such systems from the power consumption point of view.

Millimeter-wave is one of the technologies powering the new generation of wireless communication systems. To compensate the high path-loss, millimeter-wave devices need to use highly directional antennas. Consequently, beam misalignment causes strong performance degradation reducing the link throughput or even provoking a complete outage. Conventional solutions, e.g. IEEE 802.11ad, propose the usage of additional training sequences to track beam misalignment. These methods however introduce significant overhead especially in dynamic scenarios. In this paper we propose a beamforming scheme that can reduce this overhead. First, we propose an algorithm to design a codebook suitable for mobile scenarios. Secondly, we propose a blind beam tracking algorithm based on particle filter, which describes the angular position of the devices with a posterior density function constructed by particles. The proposed scheme reduces by more than 80% the overhead caused by additional training sequences.

We address the problem of static clutter removal in Wi-Fi-based passive bistatic radars. Our goal is to detect slowly moving targets in highly cluttered indoor environments, using Orthogonal Frequency-Division Multiplexing signals from the 802.11n and 802.11ac Wi-Fi standards as sources of opportunity. We propose alternatives to the commonly used Extended Cancellation Algorithm (ECA) clutter removal method. Those alternatives are compared to ECA with simulations using an innovative metric based on CA-CFAR detection, and validated with experimental measurements using two Universal Software Radio Peripherals, along with a fan and an electric train as radar targets. The conclusion of that analysis is that, thanks to the decoupled range and Doppler radar processing, simple novel methods such as Average Removal are efficient alternatives to the computationally intensive ECA which is currently the state-of-the-art in CR.

Multi-user MIMO at millimeter wave frequencies combines the huge bandwidth availability at millimeter wave with the spatial multiplexing gain of MIMO processing. Hybrid architectures have been proposed to reach this purpose while keeping implementation costs reasonably low. Active power splitter devices are the key components of a hybrid architecture enabling a lower implementation complexity compared to a full-digital solution. They distribute the same signal to several antenna elements shifting part of the precoding operations to the analog domain. Active power splitter devices however suffer from frequency distortion and their characterization is thus essential for new system designs. In this paper we propose a splitter model based on real hardware measurements showing that the splitter design critically affects the system performance. The frequency distortion influencing the digital channel estimation significantly distorts the precoded signal leading to an EVM floor in the high SNR region. However, when dynamically adapting the splitter operating point to control the trade-off between gain and bandwidth adaptively, the system EVM can be drastically reduced.

Given the high throughput requirement for 5G, merging millimeter wave technologies and multi- user MIMO seems a very promising strategy. As hardware limitations impede to realize a full digital architecture, hybrid MIMO architectures using both analog and digital precoding are considered a feasible solution to implement multi- user MIMO at millimeter wave. Real channel propagation and hardware non-idealities degrade the performance of such systems thus experimenting the new architecture is crucial to support system design. Nevertheless, hybrid MIMO systems are not yet understood as the effects of the wide channel bandwidths at millimeter wave, the non-ideal RF front end as well as the imperfections of the analog beamforming using phased antenna arrays are often neglected. In this paper, we present a 60 GHz multi-user MIMO testbed using phased antenna arrays at both transmitter and receivers. The base station equipped with a 32 phased antenna array allocates simultaneously two users. We show that frequency selective hybrid precoding can efficiently suppress inter-user interference enabling spatial multiplexing in interference limited scenario doubling the throughput compared to a SISO scenario and compensating the frequency fluctuation of the channel. In addition, we report an EVM constellation improvement of 6 dB when comparing the hybrid MIMO architecture with a fully analog architecture.

Distributed massive multiple-input multiple-output (DM-MIMO) gives a higher spectral efficiency and enhanced coverage area, compared to collocated massive MIMO (CM-MIMO). In general, for massive MIMO, time division duplex is preferable as it enables downlink (DL) precoding based on uplink (UL) channel estimation. A time division duplex (TDD) reciprocity calibration is then essential to compensate the gap between the UL-DL channels, which can be done completely in the base station relying on sounding reference signals (SRS). For a collocated array, relying on mutual coupling between antenna elements, each SRS is received with sufficient power in the array, enabling a reliable estimate of the calibration coefficients. Nevertheless, for DM-MIMO, much less power is collected in distant inter-cluster antennas which degrades the accuracy of the estimated calibration. In this paper, we propose a novel inter-cluster combining method (ICCM) which improves the signal-to-quantization-noise ratio (SQNR) of the SRS, and hence achieves a more robust calibration accuracy for practical DM-MIMO systems. Our experimental results of two 32-antenna arrays distributed in an indoor environment show that ICCM outperforms the existing state-of-the-art algorithms in the sense of lower DL error vector magnitude (EVM) by exploiting diversity and array gain efficiently.

Massive MIMO has the potential to bring great spectral and energy efficiency improvements, making it a very promising technology for future wireless systems. Essential to achieve the gains in practice, is the ability to realize the many antenna paths at low complexity. In this paper, we consider the potential of processing at the semiconductor edge by allowing voltage over-scaling and complete antenna signal failures, focusing on the per-antenna digital functionality that dominant the DSP complexity. The impact of the resulting hardware errors on the performance of Massive MIMO transmission is analyzed. It shows that the inherent redundancy in the system brings a solid tolerance to sporadic hardware errors. Potential control tactics are introduced, that could further optimize the operation of the error-prone circuitry. We anticipate that by exploiting the system and circuit margins, up to 40% power reduction could be achieved on the considered DSP functions without sacrificing performance in many traffic scenarios.

Massive MIMO is a promising technology for 5G. While its advantages of increasing capacity and reducing output power are clear, understanding its SNR requirements in practical cases lacks sufficient theoretical support. This paper computes the relative power levels of the different signal, interference and noise terms in a Massive MIMO system using Maximum-Ratio Transmission (MRT). This brings light on the operational region of the system. The MRT operation is shown to support a maximum number of users, which is function of the selected modulation and coding scheme. The related expression also leads to a bound for other linear precoders. The impact of channel estimation is shown to be related to the number of orthogonal pilot sequences and users. When both are equal it leads to a 3 dB degradation. At system level, this model directly impacts output power requirements and hence system trade-offs when determining the number of antennas to use.

The power efficiency of cellular base stations is a crucial element to maintain sustainability of future mobile networks. To investigate future network concepts, a good power model is required which is highly flexible to evaluate the diversity of power saving options. This paper presents an advanced power model which supports a broad range of network scenarios and base station types, features and configurations. In addition to the power consumption, the model also provides values on the hardware sleep capabilities (sleep depths, transition times, power savings). The paper also discusses the technology trends and scaling factors which are used to predict the power consumption of base stations up to the year 2020. Two use cases are described, illustrating the power savings over different sleep depths, and quantifying the power consumption evolution over different technology generations.

The LTE cellular network is designed for meeting the requirements of a broad range of applications in very dynamic conditions. This explains its great flexibility in resource allocation. In order to provide the mobile stations with the exact required services, the base station relies on feedback information reported by each mobile station. In this paper, several feedback reduction schemes are analysed and compared, for a broad range of LTE resource allocation schemes and deployment scenarios, by means of simulation and a quantitative cost model. It is concluded that feedback reduction should be adapted to the scenario, as function of users, resource allocation strategy or channel properties, and up to 139% gain in overall throughput can be obtained by doing so.

Efficient resource allocation is necessary to provide the users with the quality of service promised in modern cellular networks, such as LTE. Traditional allocation methods make use of the smallest granularity available, the physical resource block (PRB), to assign resources to each user. The selected resources assigned to a user form a transport block (TB). However, the standard constrains the use of only one modulation and coding rate per TB. This forces the channel quality of the resources to be averaged across the TB, in order to determine the best modulation and coding scheme. In state-of-the-art systems, a non-linear heuristic is used in order to perform this averaging. Unfortunately, when bad PRBs are present next to good ones, this strategy is not optimal. We show that dropping the worst PRBs can improve the performance while remaining standard-compliant. We propose a simple algorithm that can be overlaid to any existing solution and we analyse its effect on multiple state-of-the-art schedulers. A gain is obtained both in throughput (up to 8% increase) and in power consumption (up to 23% reduction).

Hybrid ARQ (HARQ) combines Automatic Repeat Request (ARQ) and Forward Error Correction (FEC) to exploit information from erroneous packets after retransmissions. Due to its superior reliability, HARQ became a crucial component of several important 3G and 4G systems such as Long Term Evolution (LTE) and LTE-Advanced. Although the performance advantage is very attractive, implementing HARQ is challenging with emerging high throughput communication systems. Knowing that LTE and LTE-A systems would provide Gbps or hundreds of Mbps, a straightforward implementation would require around 10 Mbit memory for a HARQ buffer. With cost and power-limited wireless terminals, the available memory size to support HARQ requires an efficient implementation. In this paper we propose a novel method to exploit the time coherence of the wireless channel in order to reduce the memory storage for HARQ. This method has been evaluated with a full LTE MIMO simulation chain. Compared to state-of-the-art solutions, we show a promising memory compression factor close to 4, whereas performance degradation is marginal.

Hybrid ARQ (HARQ) combines Automatic Repeat Request (ARQ) and Forward Error Correction (FEC) to exploit information from erroneous packets after retransmissions. Due to its superior reliability, HARQ became a crucial component of several important 3G and 4G systems. Although the performance advantage is very attractive, implementing HARQ is challenging with emerging high throughput communication systems such as LTE and LTE-A. Specifically, a large amount of data needs to be stored whenever there is a retransmission. Knowing that LTE/LTE-A systems would provide Gbps or hundreds of Mbps, a straightforward implementation would require around 14 Mbit memory as HARQ buffer. With cost and power limited wireless terminals, the available memory size to support HARQ is a strict and challenging constraint. Hence, it is essential to find efficient techniques to minimize the memory footprint for storing erroneous packets with marginal or, preferably, no degradation. In the context of LTE systems, we propose a novel method to reduce the memory footprint for HARQ systems. This method has been evaluated with a fully fledged practical LTE simulation chain with MIMO transmissions. Compared to state-of-the-art solutions, we show a promising memory compression factor that is close to 5, whereas communication performance degradation is marginal.

With the explosion of wireless communications in number of users and data rates, the reduction of network power consumption becomes more and more critical. This is especially true for base stations which represent a dominant share of the total power in cellular networks. In order to study power reduction techniques, a convenient power model is required, providing estimates of the power consumption in different scenarios. This paper proposes such a model, accurate but simple to use. It evaluates the base station power consumption for different types of cells supporting the 3GPP LTE standard. It is flexible enough to enable comparisons between state-of-the-art and advanced configurations, and an easy adaptation to various scenarios. The model is based on a combination of base station components and sub-components as well as power scaling rules as functions of the main system parameters.

In order to quantify the energy savings in wireless networks, the power consumption of the entire system needs to be captured and an appropriate energy efficiency evaluation framework must be defined. In this paper, the necessary enhancements over existing performance evaluation frameworks are discussed, such that the energy efficiency of the entire network comprising component, node and network level contributions can be quantified. The most important addendums over existing frameworks include a sophisticated power model for various base station (BS) types, which maps the RF output power radiated at the antenna elements to the total supply power of a BS site. We also consider an approach to quantify the energy efficiency of large geographical areas by using the existing small scale deployment models along with long term traffic models. Finally, the proposed evaluation framework is applied to quantify the energy efficiency of the downlink of a 3GPP LTE radio access network.

This paper optimizes handover decisions between WLAN (802.11) and WiMAX (802.16e) standards, for both uplink and downlink data transmission. The handover becomes part of a cross-layer approach controlling several knobs (air interface parameters or platform settings) in order to reduce the terminal power consumption. The first step is to derive detailed power and performance models for both standards, in order to correctly evaluate the opportunity of a handover. This includes channel fading fluctuations, extraction of MAC-level behavior and packet error rates, and overall power consumption from the wireless platform. Such models enable first optimal single-standard power-throughput trade-offs, that will be used as reference points before adding the handover possibility in order to assess its specific gain. The second step is the design of a handover controller that selects the network with the lowest expected power for the required rate. The proposed mechanism is based on regular scanning of both networks. It computes the expected energy in order to send a given amount of data over each network, taking handover cost into account, and selects the most appropriate one. Based on a software-defined radio platform and a typical channel coherence time of one second, simulations demonstrate a power saving factor up to 2.5 as a function of the scenario, compared to a single-standard system that is already cross-layer optimized. This illustrates the large gain that is available, besides the handover advantage in terms of improved connectivity.

Wireless communication standards are continuously evolving and getting more diverse.This requires a wide variety of baseband implementations within a short time-to-market. Besides, deep sub-micron technology significantly increases the design complexity and associated cost. These yield a growing need for reconfigurable/programmable baseband solutions. Implementing the whole base band functionality on programmable architectures, as foreseen in the tier-2 SDR, will become a must. However, the energy efficiency of SDR baseband platforms is unavoidably worse than the ASIC counterparts. This brings a challenging gap to bridge, which is even broadening further in emerging high rate standards. With a holistic view, we advocate a system level algorithmic approach to bridge this gap. Specifically, we propose to leverage the advantages (programmability) of SDR platforms to compensate for its disadvantages (energy efficiency). Highly flexible baseband algorithms are designed to exploit the abundant dynamics in the environment and the user requirements.In this way, the baseband can utilize the dynamics and substantially reduce the average energy consumption. In this paper, we present a design methodology and principles, illustrated with 3 representative case studies in HSDPA, WiMAX, and 3GPP LTE.

In thin client computing, applications are executed on a network server instead of on the user terminal. Since the amount of processing at the terminal is reduced, thin clients are potentially energy efficient devices. However, a network connection between client and server is required for the transmission of user input and display updates. The energy needed for this intense network communication might undo or even exceed the power savings achieved by the reduction in client-side processing. In this paper, we present experimental results on power efficiency of the wireless platform on the thin client in case of thin client traffic. The discussion is focused on VNC-RFB, a widespread thin client protocol, over an IEEE 802.11 link in three typical user scenarios. The results indicate that a cross-layer approach between application and wireless link layer could potentially lead to important power savings.

Mobile wireless broadband access networks are now becoming a reality, thanks to the emerging IEEE 802.16e standard. This kind of network offers different challenges when compared to the fixed ones, as power consumption becomes a major concern. In this standard, a strict organization of the downlink bursts is not guaranteed in the OFDMA frame and this may lead to extra power consumption for the receiver, decreasing the device's lifetime. In the present paper, we introduce an optimization algorithm capable of reducing the activity of each receiver in the system for decoding its addressed bursts, thanks to a better time-frequency organization of the bursts. We first work on fitting bursts within the smallest frame, and show that the minimal number of OFDM symbols is enough in 70 to 80% of the cases, while one extra is needed otherwise. Using a binary tree implementation of an exhaustive burst placement search, we also show that we can gain 20 to 30% in duty-cycling of the receivers by selecting the best configuration, hence gaining the corresponding energy. This holds for receivers decoding either their bursts only or all the bursts from the beginning of the frame up to their own bursts before sleeping, depending on the scenario. The full search is sustainable for up to 8 user bursts per frame.

Ultra-wideband (UWB) impulse radio is a promising technique for low-power bio-medical communication systems. While a range of analog and digital UWB architectures exist, the mostly-digital approach without analog down-conversion enables better technology scaling and signal processing flexibility. Furthermore, recently proposed sub-sampling schemes and advances in high-speed ADC circuit design are helping to make this approach more feasible at low power. However, architectures that directly sample the received signal are more vulnerable to sampling jitter. Currently, there does not exist a model describing the impact of sampling jitter making it difficult to determine appropriate tolerances or to establish the feasibility of digital architectures. To address this problem, we have developed a model of sampling jitter and derived a generic bit error rate expressions for a digital UWB modem with sampling jitter, additive noise, and imperfect channel estimation in a generic multipath environment. We then use this model to investigate the performance of sub-sampled digital UWB in a body area network. This paper explains this analytical model and compares it with simulations results for communication around the body.

Impulse-based ultra-wideband systems are appealing for low-power short-range communications as they can benefit from duty-cycled low-complexity analog architectures. Low-power receivers correlate in the analog domain and sample only at the pulse repetition frequency. However, achieving acquisition with such a receiver requires an efficient search strategy, as digital post-processing (equalization) of the signal is not possible. Moreover, as we target a network of ultra-low-power sensors which can only send a few pulses and do not implement receiver functionality in order to sustain ARQ protocols, the receiving base station has extra constraints on synchronization performance and maximal preamble length. This paper describes and optimizes a strategy compliant with that scenario, including pulse position and spreading code phase acquisition, end-of-preamble detection, and derivation of the corresponding detection thresholds. Based on a 300-bit preamble, it works within 1.5 dB of ideal synchronization on AWGN. EOP detection is analyzed, showing that a PN sequence of length 7 is sufficient, and also proposing optimal receive filters for EOP sequences defined in IEEE 802.15.4a. Finally, we show that on fading channels, the error floor coming from missed acquisitions can be removed by simply changing the detection threshold.

Unequal error protection (UEP) is the most practical way to achieve joint source-channel coding while keeping a clean separation between the source and channel coders. In this paper, a generic UEP allocation methodology is proposed. The methodology is based on a run-time algorithm, exploiting design-time models, and is applicable to bit-level transmission scenarios as well as packet-based transmission environments. It works with source-independent modeling and a linear complexity with respect to the number of substreams in the source coded stream and the number of protection levels available, which makes it an attractive solution for practical scenarios. We obtain a substantial complexity reduction compared to a classical Lagrangian optimization while keeping optimality in the allocation of the protection levels. Given a typical source and channel granularity, we obtain a factor 6 complexity reduction.

Using wireless sensors placed on a person to continuously monitor health information is a promising new application. Furthermore, ultra-wideband is a promising air interface for short-range low data rate communication scenarios matching the requirements of wireless bio-medical applications. However, the performance and complexity tradeoffs of ultrawideband systems has never been assessed for the special features of the body area propagation channel. To address this problem, we have measured electromagnetic wave propagation around the body and developed a statistical model. Using this model, we evaluate the performance of optimal and sub-optimal RAKE receivers. This paper describes the resulting communication performance versus complexity for ultra wideband RAKE receivers in a body area propagation environment.

Using ultra-wideband (UWB) wireless sensors placed on a person to continuously monitor health information is a promising new application. However, there are currently no detailed models describing the UWB radio channel around the human body making it difficult to design a suitable communication system. To address this problem, we have measured radio propagation around the body in a typical indoor environment and incorporated these results into a simple model. We then implemented this model on a computer and compared experimental data with the simulation results. This paper proposes a simple statistical channel model and a practical implementation useful for evaluating UWB body area communication systems.

Due to the improvement of compression technology, limited bandwidth channels can convey more data than before. However, since compressed streams are very sensitive to transmission errors, a joint optimization of the source-channel coding is required. Unequal error protection (UEP) is the most pragmatic way to do it, and exploits the embedded nature of bitstreams produced by scalable coders. However, depending on the number of substreams and protection levels, optimization of UEP can be extremely complex. A large simplification comes from considering the contribution to the global distortion of each substream as independent from the others. This paper explores this distortion additivity assumption for JPEG2000, and shows that though the assumption does not fully hold, no visible consequence on UEP performance is introduced, while reducing significantly the complexity of the optimization process.

Using wireless sensors placed on a person to continuously monitor health information is a promising new application. However, there are currently no models describing the radio channel around the human body making it difficult to design a suitable communication system. To address this problem, we have simulated electromagnetic wave propagation around the body and incorporated these results into a simple model. We then compared this model with measurements taken around the human torso and with previous studies in the literature. This paper proposes a simple statistical channel model useful for evaluating both UWB and (after resampling) narrow-band body area communication systems.

The joint linear precoder and decoder minimum mean squared error (MMSE) design represents a low complexity yet powerful solution for spatial multiplexing MIMO systems. Its performance, however, critically depends on the availability of timely channel state information (CSI) at both transmitter and receiver. In practice, the latter assumption can be severely challenged, due to channel time variations that lead to outdated CSI at the transmitter. State-of-the-art designs mistakenly use the outdated CSI to design the linear precoder and rely on the receiver to reduce the induced degradation. In this paper, we propose a robust Bayesian joint linear precoder and decoder solution that takes into account the uncertainty of the true channel, given the outdated CSI at the transmitter. We finally assess the robustness of our design to channel time variations through Monte-Carlo analysis of the system's MMSE and average bit-error rate (BER) performance.

To approach the potential MIMO capacity while optimizing the system bit-error rate (BER) performance, the joint transmits and receive minimum mean squared error (MMSE) design has been proposed. It is the optimal linear scheme for spatial multiplexing MIMO systems, assuming a fixed number of spatial streams p as well as a fixed modulation and coding across these spatial streams. However, state-of-the-art designs arbitrarily choose and fix the value of the number of spatial streams p, which may lead to an inefficient power allocation strategy and a poor BER performance. We have previously proposed to relax the constraint of fixed number of streams p and to optimize this value under the constraints of fixed average total transmit power and fixed spectral efficiency, what we referred to as spatial-mode selection. Our previous selection criterion was the minimization of the system sum MMSE. In the present contribution, we introduce a new and better spatial-mode selection criterion that targets the minimization of the system BER. We also provide a detailed performance analysis, over flat-fading channels, that confirms that our proposed spatial-mode selection significantly outperforms the state-of-the-art joint Tx/Rx MMSE design both for uncoded and coded systems, thanks to its better exploitation of the MIMO spatial diversity and more efficient power allocation.

In order to select error-correcting codes for various applications, their performances have to be determined. However, when targeting error-rate computations for block error-correcting codes, many required results are missing in the coding literature. Even in the simple case of binary codes and bounded-distance decoding, classical texts do not provide a bit-error rate (BER) expression taking into account both decoding errors and failures. In the case of nonbinary codes used to protect binary symbols, such as Reed-Solomon codes in many applications, there is no available result making realistic channel assumptions in order to derive BERs. Finally, for the more complex case of complete decoding, only some bounds are available, such as the union one. This paper presents new approximations of error rates for block error-correcting codes as a function of the channel BER (crossover probability). We extend an existing approximation in order to consider not only bounded-distance decoding, but also complete "nearest-neighbor" decoding. We also develop approximations able to deal with nonbinary codes. Combined with state-of-the-art approximations, these new results enable the computation of bit-, symbol-, and word-error rates in various decoding situations. They can consider separately errors related to erroneously decoded words and decoding failures, and they provide accurate estimates of error rates. As they do not require detailed information about the structure of codes, they are general enough to be used in simple comparisons between different codes, avoiding the need for simulations.

In this paper, we investigate the performances of Gaussian modeling and linear prediction tools for error detection and concealment in the transmission of still images. We consider the transmission of subband encoded images through two types of channels. We model the residual correlation between subband coefficients by considering them as jointly Gaussian variables. The first transmission medium considered is a packet-oriented channel, where some packets are lost during transmission. The problem is to estimate the values of missing coefficients. In this case, particular care must be taken while evaluating correlation matrices from incomplete data. The other system considered is based on a discrete memoryless noisy channel affecting the data being transmitted. The challenge is here first to determine the locations of the errors--which is done through hypotheses tests--and then to replace them by estimates based on their neighbors. The reconstruction via linear prediction is shown to give better results than median filtering based reconstruction. Error detection through this Gaussian model also shows promising results, in particular when channel statistics are taken into account in a joint source-channel decoding framework.