Large multiple-input multiple-output (MIMO) systems rely on efficient downlink precoding to enhance data rates and improve connectivity through spatial multiplexing. However, currently employed linear precoding techniques, such as minimum mean square error (MMSE) precoding, significantly limit the achievable spectral efficiency. To meet practical error-rate targets, existing linear methods require an excessively high number of access point (AP) antennas relative to the number of supported users, leading to disproportionate increases in power consumption. Efficient non-linear processing frameworks for uplink MIMO transmissions, such as NL-COMM, have been proposed. However, downlink non-linear precoding methods, such as Vector Perturbation (VP), remain impractical for real-world deployment due to their exponentially increasing computational complexity with the number of supported MIMO streams. This work presents ViPer NL-COMM, the first practical algorithmic and implementation framework for VP-based downlink precoding. ViPer NL-COMM extends the core principles of NL-COMM to the precoding problem, enabling scalable parallelization and real-time computational performance while maintaining the substantial spectral-efficiency benefits of VP precoding. ViPer NL-COMM consists of a novel mathematical framework and an FPGA prototype capable of supporting large MIMO configurations (up to 16×16), high-order modulation (256-QAM), and wide bandwidths (100 MHz) within practical power and resource budgets. System-level evaluations demonstrate that ViPer NL-COMM achieves target error rates using only half the number of transmit antennas required by linear precoding, yielding net power savings on the order of hundreds of Watts at the RF front end. Moreover, ViPer NL-COMM enables supporting more information streams than available AP antennas when the streams are of low-rate, paving the way for enhanced massive-connectivity scenarios in next-generation wireless networks.

With video streaming now accounting for the majority of internet traffic, wireless networks face increasing demands, especially in densely populated areas where limited spectral resources are shared among many devices. While multi-user (MU)-MIMO technology aims to improve spectral efficiency by enabling concurrent transmissions over the same frequency and time resources, traditional linear processing methods fall short of fully utilizing available channel capacity. These methods require a substantial number of antennas and RF chains, to support a much smaller number of MIMO streams, leading to increased power consumption and operational costs, even when the supported streams are of low rate. In this demo, we present NL-COMM, an advanced non-linear MIMO processing framework, demonstrated for the first time with commercial off-the-shelf (COTS) user equipment (UEs) in a fully 3GPP-compliant environment. In addition, also for the first time, the audience will compare and assess the quality of live, over-the-air video transmission from four concurrently transmitting UE devices, alternating between current state-of-the-art MIMO detection algorithms and NL-COMM. Key gains of NL-COMM include improved stream quality, halving the number of required base station antennas without compromising stream quality compared to linear approaches, as well as achieving antenna overloading factors of 400%.

The Open Radio Access Network (RAN) paradigm envisions a more flexible, interoperable, and intelligent RAN ecosystem via new open interfaces and elements like the RAN Intelligent Controller (RIC). However, the impact of these elements on Open RAN's power consumption remains heavily unexplored. This work for the first time evaluates the impact of Key Performance Indicator (KPI) monitoring on RIC's power consumption using real traffic and power measurements. By analyzing various RIC-RAN communication scenarios, we identify that RIC's power consumption can become a scalability bottleneck, particularly in large-scale deployments, even when RIC is limited to its core operational functionalities and without incorporating application-specific processes. In this context, we also explore potential power savings through the elimination of redundant KPI transmissions for the first time, extending existing techniques for identical subscription removal and KPI selection. We achieve significant power consumption gains exceeding 87% of the overall RIC power consumption.

Multi-user (MU)-multiple-input, multiple-output (MIMO) technology has been central to the evolution of wireless networks, since it can provide substantial network gains by enabling the concurrent transmission of a large number of information streams, over the same frequency. However, reliably detecting these mutually interfering streams comes at a very high computational cost that increases exponentially with the number of concurrently transmitted streams. This makes the corresponding MU-MIMO systems highly inefficient in terms of power consumption and processing latency. In this context, and in order to unlock the full MU-MIMO potential, alternative computing architectures are required, able to efficiently detect a large number of information streams, in a power-efficient manner. In this context, NeuroMIMO, is the first attempt to apply the principles of neuromorphic computing to achieve highly efficient MIMO detection. NeuroMIMO suggests and evaluates two different ways to translate the MIMO detection problem into a neuromorphic one. The first (i.e., Massive-NeuroMIMO) is appropriate for massive MIMO systems, where the number of receive, base-station/access-point antennas is much higher than the number of information streams. The second (i.e., Highly-Efficient-NeuroMIMO) is appropriate for the case where the number of transmitted streams approaches the number of base station antennas, and can reach the performance of the optimal Maximum-Likelihood detector. We discuss the trade-offs between the two NeuroMIMO approaches, and we show that both can provide substantial power gains compared to their traditional counterparts, while accounting for the preprocessing overhead required to translate the MIMO detection problem into a neuromorphic one. In addition, despite the current limitations in the "speed" of existing neuromorphic chips, we discuss that real-time processing detection can be achieved, even for a 5G NR system with 100 MHz operating bandwidth.

Non-linear detection schemes can substantially improve the achievable throughput and connectivity capabilities of uplink MU-MIMO systems that employ linear detection. However, the complexity requirements of existing non-linear soft detectors that provide substantial gains compared to linear ones are at least an order of magnitude more complex, making their adoption challenging. In particular, joint soft information computation involves solving multiple vector minimization problems, each with a complexity that scales exponentially with the number of users. This work introduces a novel ultra-low-complexity, non-linear detection scheme that performs joint Detection and Approximate Reliability Estimation (DARE). For the first time, DARE can substantially improve the achievable throughput (e.g., $40 \%$) with less than $2 \times$ the complexity of linear MMSE, making non-linear processing extremely practical. To enable this, DARE includes a novel procedure to approximate the reliability of the received bits based on the region of the received observable that can efficiently approach the accurately calculated soft detection performance. In addition, we show that DARE can achieve a better throughput than linear detection when using just half the base station antennas, resulting in substantial power savings (e.g., 500 W). Consequently, DARE is a very strong candidate for future power-efficient MU-MIMO developments, even in the case of software-based implementations, as in the case of emerging Open-RAN systems. Furthermore, DARE can achieve the throughput of the state-of-the-art non-linear detectors with complexity requirements that are orders of magnitude lower.

Future autonomous transportation systems necessitate network infrastructure capable of accommodating massive vehicular connectivity, despite the scarce availability of frequency resources. Current approaches for achieving such required high spectral efficiency, rely on the utilization of Multiple-Input, Multiple-Output (MIMO) technology. However, conventional MIMO processing approaches, based on linear processing principles, leave much of the system’s capacity heavily unexploited. They typically require a large number of power-consuming antennas and RF-chains to support a substantially smaller number of concurrently connected devices, even when the devices are transmitting at low rates. This translates to inflated operational costs that become substantial, particularly in ultra-dense, metropolitan-scale deployments. Therefore, the question is how to efficiently harness this unexploited MIMO capacity and fully leverage the available RF infrastructure to maximize device connectivity. Addressing this challenge, this work proposes an Open Radio Access Network (Open-RAN) deployment, with Massively Parallelizable Non-linear (MPNL) MIMO processing for densely deployed, and power-efficient vehicular networks. For the first time, we quantify the substantial gains of MPNL in achieving massive vehicular connectivity with significantly reduced utilized antennas, compared to conventional linear approaches, and without any throughput loss. We find that an Open-RAN-based realization exploiting the MPNL advancements can yield an increase of over $300 \%$ in terms of concurrently transmitting single-antenna vehicles in urban mobility settings and for various Vehicle-to-Infrastructure (V2I) and Network (V2N) use cases. In this context, we discuss how implementing MPNL within the Open-RAN ecosystem allows for simpler and more densely deployed radio units, paving the way for fully autonomous and sustainable transportation systems.

Terabits per second (Tbps) wireless links are essen-tial for realizing emerging mobile immersive experiences such as holographic telepresence. A promising approach to achieve such data rates is to exploit ultra-wide bandwidths. However, the need for ultra-fast, high-precision analogue-to-digital converters (ADCs) in digital systems to exploit such ultra-wide bandwidths becomes impractical in terms of power consumption. In addition, the increased sampling rates that challenge the speed of modern digital processors and the algorithmic complexity make performing ultra-fast digital signal processing challenging in terms of processing latency and power consumption. A promising approach to overcome these bottlenecks is DigiLogue processing, which suggests to remove ADCs and to perform signal processing directly in the analogue domain. However, practical DigiLogue receivers need to support fading channels, in which accurate channel estimation becomes crucial for reliable signal detection. In addition, systems operating in high-frequency bands (e.g., mm-Wave and THz), where ultra-wide bandwidths are available, experience a small number of channel taps, in which analogue-based channel estimation approaches can be realized with low complexity. However, existing analogue-based channel estimation approaches target hard detection/decoding and they are not able to provide the channel information required to perform “soft” processing in DigiLogue receivers. In this work, we propose an analogue-based channel estimation approach based on utilizing Golay sequences employed in recent high-speed standards, which is suitable for DigiLogue receivers. Our approach achieves a 1 dB better signal-to-noise ratio (SNR) in error performance compared to existing analogue methods and saves more than 29 x power than the digital counterpart.

Open Radio Access Networks (Open-RAN) trigger a shift from conventional monolithic RAN architectures to disaggregated designs with open interfaces, diversifying the 5G supply chain and boosting innovation. It is envisaged that Open-RAN deployments will be heavily software-based, allowing for higher flexibility and faster integration of new features. However, existing software-based solutions, seem to be unable to realize practical and standard-compliant Multiple-Input, Multiple-Output (MIMO) designs with a large number of concurrently transmitted information streams, as the 5G New Radio standard requires in order to substantially improve connectivity and throughput. In this context, we introduce MIMO-SoftiPHY, the first 3GPP and Open-RAN compliant, software-based physical layer (PHY) design and implementation framework that can practically realize MIMO designs with large numbers of information streams in a power-efficient manner. MIMO-SoftiPHY is inherently integrated with OpenAirInterface, enabling practical, software-based MIMO deployments with commercial-off-the-shelf user equipment. Specifically, MIMO-SoftiPHY achieves real-time performance for 12 MU-MIMO streams at a 10 MHz bandwidth and 8 streams at a 20 MHz. In addition, and in contrast to existing designs, MIMO-SoftiPHY can also support non-linear base-station processing in real-time that, as we show, can result in substantial power savings at the radio side, by obviating the need for a “massive” number of base-station antennas.

Multi-user (MU) MIMO-OFDM systems with aggressive spatial multiplexing are promising to enhance throughput and enable massive connectivity. In such systems, residual carrier frequency offsets (CFOs), due to the instability of oscillators and doppler shifts, can substantially degrade the achievable uplink throughput, especially when the number of connected devices becomes large. Existing approaches to mitigate CFOs in MU scenarios, typically involve closed-loop feedback that can result in high signaling overhead and/or significant residual CFO. Being able to compensate for the CFO of the multiple users at the receiver side, can enable the joint transmission of frequency asynchronous users, can obviate the need for high overhead synchronization procedures, can enable the use of cheaper oscillators, and can potentially unlock new user access schemes. However, as we discuss here in detail, compensating for the multiple user CFOs at the receiver is currently impractical due to the corresponding exponential complexity requirements. At the same time, methods that are typically used in single-user MIMO-OFDM systems are inappropriate for MU-MIMO scenarios and, as we show, can result in substantial (e.g., 80%) throughput degradation. To fill this gap, for the first time, we propose a joint CFO compensation and MU detection scheme that can support a large number of spatially transmitted information streams with practical processing complexity and latency requirements. We show that the proposed scheme enables frequency asynchronous user transmission and approaches the performance of perfectly synchronized systems with complexity requirements that are comparable to current MU-MIMO detection schemes that assume perfect synchronization.

Multi-user multiple-input, multiple-output (MU-MIMO) designs can substantially increase the achievable throughput and connectivity capabilities of wireless systems. However, existing MU-MIMO deployments typically employ linear processing that, despite its practical benefits, can leave capacity and connectivity gains unexploited. On the other hand, traditional non-linear processing solutions (e.g., sphere decoders) promise improved throughput and connectivity capabilities, but can be impractical in terms of processing complexity and latency, and with questionable practical benefits that have not been validated in actual system realizations. At the same time, emerging new Open Radio Access Network (Open-RAN) designs call for physical layer (PHY) processing solutions that are also practical in terms of realization, even when implemented purely on software. This work demonstrates the gains that our highly efficient, massively parallelizable, non-linear processing (MPNL) framework can provide, both in the uplink and downlink, when running in real-time and over-the-air, using our new 5G-New Radio (5G-NR) and Open-RAN compliant, software-based PHY. We showcase that our MPNL framework can provide substantial throughput and connectivity gains, compared to traditional, linear approaches, including increased throughput, the ability to halve the number of base-station antennas without any performance loss compared to linear approaches, as well as the ability to support a much larger number of users than base-station antennas, without the need for any traditional Non-Orthogonal Multiple Access (NOMA) techniques, and with overloading factors that can be up to 300%.

Multi-user (MU), multiple-input, multiple-output (MIMO) detection has been extensively investigated, and many techniques have been proposed. However, further performance improvements may be constrained by limitations in classical computation. The motivation for this work is to test whether a machine that exploits quantum principles can offer improved performance over conventional detection approaches. This paper presents an evaluation of MIMO detection based on quantum annealing (QA) when run on an actual QA quantum processing unit (QPU) and describes the challenges and potential improvements. The evaluations show promising results in some cases, such as near-optimality in a QPSK-modulated 8×8 MIMO case, but poor results in other cases, such as for larger systems or when using 16-QAM. We show that some challenges of QA detection include dealing with integrated control errors (ICE), the limited dynamic range of QA QPUs, an exponential increase in the number of qubits to the problem size, and a high computation overhead. Solving these challenges could make QA-based detection superior to conventional approaches and bring a new generation of MU-MIMO detection methods.

The intensifying demand for data rate and connectivity has resulted in multi-user multiple-input multiple-output (MU-MIMO) deployments. MU-MIMO allows multiple data streams to transmit concurrently in the same spectrum band. These mutually interfering streams need to be processed at the base station (BS), leading to substantial computational complexity requirements. Linear MIMO detectors/precoders are popular due to their relatively low complexity. However, matrix inversion is a challenging task in linear detectors/precoders. Especially in experimental platforms, software-based inversions are infeasible for a large number of users. This work presents Matrix Inversion on Channel Approximation (MICA), a novel method that aims to reduce the complexity of matrix inversion by exploiting the characteristics of channel correlation in the time domain. In low-mobility scenarios (user speeds less than 20km/h), MICA can reduce the average complexity and processing latency required for computing the inverse of 64 × 12 channel matrices by about 90% compared to a conventional scheme, while maintaining almost the same error rate performance.

In this work, Generalized Space-Time Super-Modulation (GSTSM) is introduced which enables the transmission of an additional flexible-rate and highly-reliable information stream concurrently with the conventionally transmitted symbols, without the need for increasing the corresponding packet length. This is attained by jointly exploiting the spatial and temporal dimensions of multiple-antenna systems, which enables efficient detection for conventional and additional information subchannels even in highly correlated channel conditions or AWGN channels. In the context of machine-type communications, GSTSM enables grant-free medium access without transmitting additional headers to convey each machine's signature information. Hence, it is shown that even at an extreme case where the data packets of two users are always colliding, GSTSM offers throughput gains of up to 33% compared to the best examined header-based scheme. For the same scenario, it is shown that GSTSM based on joint multi-user detection provides throughput gains of up to 2.5× compared with the case where users' signals are detected independently. In addition, it yields over 90% improvement in achievable rates compared with the schemes that require centralized medium-access coordination. For both joint and independent signal detection schemes, it is also shown that adopting an iterative detection/decoding approach allows to further improve the throughput gains.

In conventional hybrid beamforming approaches, the number of radio-frequency (RF) chains is the bottleneck on the achievable spatial multiplexing gain. Recent studies have overcome this limitation by increasing the update-rate of the RF beamformer. This paper presents a framework to design and evaluate such approaches, which we refer to as agile RF beamforming, from theoretical and practical points of view. In this context, we consider the impact of the number of RF-chains, phase shifters' speed, and resolution to design agile RF beamformers. Our analysis and simulations indicate that even an RF-chain-free transmitter, which its beamformer has no RF-chains, can provide a promising performance compared with fully-digital systems and significantly outperform the conventional hybrid beamformers. Then, we show that the phase shifter's limited switching speed can result in signal aliasing, in-band distortion, and out-of-band emissions. We introduce performance metrics and approaches to measure such effects and compare the performance of the proposed agile beamformers using the Gram-Schmidt orthogonalization process. Although this paper aims to present a generic framework for deploying agile RF beamformers, it also presents extensive performance evaluations in communication systems in terms of adjacent channel leakage ratio, sum-rate, power efficiency, error vector magnitude, and bit-error rates.

This paper presents a DSP acceleration and assessment framework targeting SDR platforms on x86_64 architectures. Driven by the potential of rapid prototyping and evaluation of breakthrough concepts that these platforms provide, our work builds upon the well-known OpenAirInterface codebase, extending it for advanced, previously unsupported modes towards large and massive MIMO such as non-codebook-based multi-user transmissions. We then develop an acceleration/profiling framework, through which we present finegrained execution results for DSP operations. Incorporating the latest SIMD instructions, our acceleration framework achieves a unitary speedup of up to 10×. Integrated into OpenAirInterface, it accelerates computationally expensive MIMO operations by up to 88% across tested modes. Besides resulting in a useful tool for the community, this work provides insight on runtime DSP complexity and the potential of modern x86_64 systems.

It is well documented that the achievable throughput of MIMO systems that employ linear beamforming can significantly degrade when the number of concurrently transmitted information streams approaches the number of base-station antennas. To increase the number of the supported streams, and therefore, to increase the achievable net throughput, non-linear beamforming techniques have been proposed. These beamforming approaches are typically evaluated via simulations or via simplified over-the-air experiments that are sufficient for validating their basic principles, but they neither provide insights about potential practical challenges when trying to adopt such approaches in a standards-compliant framework, nor they provide any indication about the achievable performance when they are part of a standards-compliant protocol stack. In this work, for first time, we evaluate non-linear beamforming in a 3GPP standards-compliant framework, using our recently-proposed SWORD research platform. SWORD is a flexible, open for research, software-driven platform that enables the rapid evaluation of advanced algorithms without extensive hardware optimizations that can prevent promising algorithms from being evaluated in a standards-compliant stack. We show that in an indoor environment, vector perturbation-based non-linear beamforming can provide up to 46% throughput gains compared to linear approaches for 4×4 MIMO systems, while it can still provide gains of nearly 10% even if the number of base-station antennas is doubled.

The increasing demand for connectivity and throughput, despite the spectrum limitations, has triggered a paradigm shift towards non-orthogonal signal transmissions. However, the complexity requirements of near-optimal detection methods for such systems becomes impractical, due to the large number of mutually interfering streams and to the rank-deficient or ill-determined nature of the corresponding interference matrix. This work introduces g-MultiSphere; a generic massively parallel and near-optimal sphere-decoding-based approach that, in contrast to prior work, applies to both well- and ill-determined non-orthogonal systems. We show that g-MultiSphere is the first approach that can support large uplink multi-user MIMO systems with numbers of concurrently transmitting users that exceed the number of receive antennas by a factor of two or more, while attaining throughput gains of up to 60% and with reduced complexity requirements in comparison to known approaches. By eliminating the need for sparse signal transmissions for non-orthogonal multiple access (NOMA) schemes, g-MultiSphere can support more users than existing systems with better detection performance and practical complexity requirements. In comparison to state-of-the-art detectors for NOMA schemes and non-orthogonal signal waveforms (e.g., SEFDM) g-MultiSphere can be up to an order of magnitude less complex, and can provide throughput gains of up to 60%.

The flexibility in supporting heterogeneous services with vastly different technical requirements is one of the distinguishing characteristics of the fifth generation (5G) communication systems and beyond. One viable solution is to divide the system bandwidth into several bandwidth parts (BWPs), each having a distinct numerology optimized for a particular service. However, multiplexing of mixed numerologies over a unified physical infrastructure comes at the cost of induced interference. In this paper, we develop an analytical system model for inter-numerology interference (InterNI) analysis in orthogonal frequency-division multiplexing (OFDM) systems with and without filter processing in the presence of mixed numerologies. With the analytical model, the level of InterNI is quantified by the developed analytical metric, which is expressed as a function of several system parameters. This leads to an analysis and evaluation of these parameters for meeting a given distortion target. Moreover, a case study on power allocation utilizing the derived analysis is presented, where an optimization problem of maximizing the sum rate is formulated, and a solution is also provided. It is also demonstrated that a filtered-OFDM system better accommodates the coexistence of mixed numerologies. The proposed model provides an accurate analytical guidance for the multi-service design in 5G and beyond systems.

An index modulation (IM) assisted Discrete Cosine Transform based Orthogonal Frequency Division Multiplexing (DCT-OFDM) with Enhanced Transmitter Design (termed as EDCT-OFDM-IM) is proposed. It amalgamates the concept of Discrete Cosine Transform assisted Orthogonal Frequency Division Multiplexing (DCT-OFDM) and Index Modulation (IM) to exploit the design freedom provided by the double number of available subcarrier under the same bandwidth. In the proposed EDCT-OFDM-IM scheme, the maximum likelihood (ML) detector used for symbol bits and index bits recovering is derived and the sophisticated designing guidelines for EDCT-OFDM-IM are provided. Based on the derived pairwise error event probability, a theoretical upper bound on the average bit-error probability (ABEP) of EDCT-OFDM-IM is provided over multipath fading channels. Furthermore, the maximum peak-to-average power ratio (PAPR) of our proposed EDCT-OFDM-IM scheme is derived and compared to than the general Discrete Fourier Transform (DFT) based OFDM-IM counterpart.

The recent paradigm shift towards the transmission of large numbers of mutually interfering information streams, as in the case of aggressive spatial multiplexing, combined with requirements towards very low processing latency despite the frequency plateauing of traditional processors, initiates a need to revisit the fundamental maximum-likelihood (ML) and, consequently, the sphere-decoding (SD) detection problem. This work presents the design and VLSI architecture of MultiSphere; the first method to massively parallelize the tree search of large sphere decoders in a nearly-concurrent manner, without compromising their maximum-likelihood performance, and by keeping the overall processing complexity comparable to that of highly-optimized sequential sphere decoders. For a 10 × 10 MIMO spatially multiplexed system with 16-QAM modulation and 32 processing elements, our MultiSphere architecture can reduce latency by 29× against well-known sequential SDs, approaching the processing latency of linear detection methods, without compromising ML optimality. In MIMO multicarrier systems targeting exact ML decoding, MultiSphere achieves processing latency and hardware efficiency that are orders of magnitude improved compared to approaches employing one SD per subcarrier. In addition, for 16×16 both “hard”and “soft”-output MIMO systems, approximate MultiSphere versions are shown to achieve similar error rate performance with state-of-the art approximate SDs having akin parallelization properties, by using only one tenth of the processing elements, and to achieve up to approximately 9× increased energy efficiency.

Large multi-user MIMO systems with spatial multiplexing are among the most promising approaches for increasing wireless throughput while serving many clients. Yet, the achievable spectral efficiency of current large MIMO systems is limited by the adoption of simple, but sub-optimal, linear precoding techniques (e.g, minimum-mean-square-error (MMSE)). Nonlinear precoding methods, like Vector Perturbation (VP), claim to be able to provide improved network throughput. However, such methods are still purely theoretical and they do not account for the practical aspects of actual wireless systems, as the corresponding complexity and latency requirements, or the need for feasible rate adaptation. This paper presents ViPer, the first practical VP-based MIMO system design. ViPer substantially reduces the latency requirements of VP by employing massively parallel processing and realizes a practical rate adaptation method that efficiently translates VP's signal-to-noise-ratio (SNR) gains into actual throughput gains. In our first systematic experimental evaluation of VP-based precoders, we show that ViPer can deliver in practice up to 30% higher throughput than MMSE precoding with comparable latency requirements. In addition, ViPer can match the performance of state-of-the-art parallel VP precoding schemes, by utilizing less than one tenth of the processing elements.

This work introduces Generalized Space-Time Super- Modulation (GSTSM), a generalization of the recently proposed Space-Time Super-Modulation scheme that enables the transmission of additional, highly-reliable information on the top of conventionally transmitted symbols, without increasing the corresponding packet length. GSTSM jointly exploits the spatial and temporal dimensions of multiple-antenna systems but, in contrast to the initially proposed approach, it does not require the use of space-time block codes. Instead, GSTSM jointly elaborates on the concepts of spatial modulation and spatial diversity, while intentionally introducing temporal correlation to the transmitted symbol sequence. In the context of machine-type communications, GSTSM enables one-shot and grant- free medium access without transmitting additional headers to convey each machines ID. As a result, we show that GSTSM can provide throughput gains of up to 2.5× compared to conventional header-based schemes, even in the case of colliding packets.

The discrete cosine transform (DCT) based multicarrier modulation (MCM) system is regarded as one of the promising transmission techniques for future wireless communications. By employing cosine basis as orthogonal functions for multiplexing each real-valued symbol with symbol period of T, it is able to maintain the subcarrier orthogonality while reducing frequency spacing to 1/(2T) Hz, which is only half of that compared to discrete Fourier transform (DFT) based multicarrier systems. In this paper, following one of the effective transmission models by which zeros are inserted as guard sequence and the DCT operation at the receiver is replaced by DFT of double length, we reformulate and evaluate three classic detection methods by appropriately processing the post-DFT signals both for single antenna and multiple-input multiple-output (MIMO) DCT-MCM systems. In all cases, we show that with our reformulated detection approaches, DCT-MCM schemes can outperform, in terms of error-rate, conventional OFDM-based systems.

This paper presents a complex-valued discrete multicarrier modulation (MCM) system based on the real-valued discrete Hartley transform (DHT) and its inverse (IDHT). Unlike the conventional discrete Fourier transform (DFT), the DHT cannot diagonalize multipath fading channels due to its inherent properties, and this results in mutual interference between subcarriers of the same mirror-symmetrical pair. We explore this interference pattern in order to seek an optimal solution to utilize channel diversity for enhancing the bit error rate (BER) performance of the system. It is shown that the optimal channel diversity gain can be achieved via pairwise maximum likelihood (ML) detection, taking into account not only the subcarrier's own channel quality but also the channel state information of its mirror-symmetrical peer. Performance analysis indicates that DHT-based MCM can mitigate fast fading effects by averaging channel power gains of each mirror-symmetrical pair of subcarriers. Simulation results show that the proposed scheme has a substantial improvement in BER over the conventional DFT-based MCM system.

We introduce the concept of space-time super-modulation according to which additional low-rate and highly reliable information can be transmitted on top of traditionally modulated and space-time encoded information, without increasing the transmitted block length or degrading their error-rate performance. This is achieved by exploiting the temporal redundancy introduced by the space-time block codes and, specifically, by efficiently mapping transmission patterns to specific information content. We show that space-time super-modulation can be efficiently used in the context of machine-type communications to enable one-shot grant-free joint medium access and rateless data transmission while reducing or even eliminating the need for transmitting preamble sequences. As a result, compared with traditional approaches that use correlatable preamble sequences or encoded preambles to transmit the signature information of transmitted packets, space-time super-modulation can achieve significant throughput gains. For example, we show up to 35% throughput gains from the second best examined preamble-based scheme when transmitting blocks of 200 bits.

This work introduces MultiSphere, a method to massively parallelize the tree search of large sphere decoders in a nearly-independent manner, without compromising their maximum-likelihood performance, and by keeping the overall processing complexity at the levels of highly-optimized sequential sphere decoders. MultiSphere employs a novel sphere decoder tree partitioning which can adjust to the transmission channel with a small latency overhead. It also utilizes a new method to distribute nodes to parallel sphere decoders and a new tree traversal and enumeration strategy which minimize redundant computations despite the nearly-independent parallel processing of the subtrees. For an 8 × 8 MIMO spatially multiplexed system with 16-QAM modulation and 32 processing elements MultiSphere can achieve a latency reduction of more than an order of magnitude, approaching the processing latency of linear detection methods, while its overall complexity can be even smaller than the complexity of well-known sequential sphere decoders. For 8 × 8 MIMO systems, MultiSphere's sphere decoder tree partitioning method can achieve the processing latency of other partitioning schemes by using half of the processing elements. In addition, it is shown that for a multi-carrier system with 64 subcarriers, when performing sequential detection across subcarriers and using MultiSphere with 8 processing elements to parallelize detection, a smaller processing latency is achieved than when parallelizing the detection process by using a single processing element per subcarrier (64 in total).

We introduce the concept of Space-Time Super- Modulation according to which additional low rate and highly reliable information can be transmitted by further super-modulating blocks of traditionally modulated and space-time encoded information. This is achieved by exploiting the redundant information introduced by the space-time block codes and, specifically, by efficiently mapping transmission patterns to specific information content. It is shown that Space-Time Super-Modulation can be efficiently used in the context of machine-type communications to enable joint medium access and rateless data transmission while minimizing or even eliminating the need for transmitting preamble sequences. Compared with traditional approaches that use encoded preambles or preambles based on Zadoff-Chu sequences to transmit the signature information of transmitted packets, Space-Time Super-Modulation can achieve throughput gains of more than 35% when transmitting blocks of 200 symbols.

This paper presents the design and implementation of Geosphere, a physical- and link-layer design for access point-based MIMO wireless networks that consistently improves network throughput. To send multiple streams of data in a MIMO system, prior designs rely on a technique called zero-forcing, a way of "nulling" the interference between data streams by mathematically inverting the wireless channel matrix. In general, zero-forcing is highly effective, significantly improving throughput. But in certain physical situations, the MIMO channel matrix can become "poorly conditioned," harming performance. With these situations in mind, Geosphere uses sphere decoding, a more computationally demanding technique that can achieve higher throughput in such channels. To overcome the sphere decoder's computational complexity when sending dense wireless constellations at a high rate, Geosphere introduces search and pruning techniques that incorporate novel geometric reasoning about the wireless constellation. These techniques reduce computational complexity of 256-QAM systems by almost one order of magnitude, bringing computational demands in line with current 16- and 64-QAM systems already realized in ASIC. Geosphere thus makes the sphere decoder practical for the first time in a 4 × 4 MIMO, 256-QAM system. Results from our WARP testbed show that Geosphere achieves throughput gains over multi-user MIMO of 2× in 4 × 4 systems and 47% in 2 × 2 MIMO systems.

Multi-user detection is an efficient approach proposed to boost the spectral efficiency of a wireless communication system. While multi-user detection in synchronous systems or in flat fading environments has been successfully addressed, it is still an open and challenging problem in the practical case of asynchronous MIMO systems employing space-frequency (time) block coding and operating in frequency selective environments. In this paper, we show how the concept of multi-user detection can be efficiently extended to the latter case with a low complexity overhead and a small performance loss compared to the synchronous case.

In this paper, the iterative scheduling issue for multiple-input multiple-output (MIMO) systems with turbo codes is addressed based on the informed asynchronous scheduling, initially used for scheduling low-density parity-check (LDPC) decoding. For the iterative MIMO demapping and turbo decoding, the convergence behavior by using the informed asynchronous scheduling is first demonstrated to be similar to the standard sequential scheduling. As the extrinsic log-likelihood ratio (LLR) calculation at both the MIMO demapper and turbo decoder costs high complexity, an informed message update (IMU) rule is further proposed to select only a subset of extrinsic LLRs for updates within each iteration. By avoiding the re-calculations for the extrinsic LLRs with small changes between successive iterations, significant complexity savings are achieved with negligible error rate performance loss.

This paper introduces a new framework of the multiple-input multiple-output (MIMO) detection in iterative receivers. Unlike the conventional methods processing with symbol lattice, we consider the delta symbol lattice, i.e., the difference between two arbitrary points in the symbol lattice. The inherent flexible, symmetric, and sparse properties of the delta lattice enhance the detection in both complexity and performance aspects. Consequently, we propose a delta-list MIMO (DL-MIMO) detection which separately exploits the channel information and the a priori information so that a soft-input soft-output sphere decoder is dispensable. Simulation results demonstrate this hardware-friendly DL-MIMO detection delivers nearly-optimal performance at affordable cost in a practical scenario.

Using the Schnorr-Euchner (SE) order for soft-input sphere decoders is inefficient for implementation, because it requires exhaustive calculation and sorting of partial metrics of all constellation points. Instead, low-complexity methods can be applied by separating the partial metric into channel information and a priori information and solely enumerating based on one of them. With such an orthogonalization, this paper presents an algorithm that effectively combines these two enumerations to deliver an order close to the SE one. Mathematical analyses and simulation results demonstrate that this is the first algorithm allowing for a low-complexity implementation with optimal error rate performance for any number of iterations.

A new, reduced complexity algorithm is proposed for compensating the Inter-Carrier Interference (ICI) caused by severe PHase Noise (PHN) and Residual Frequency Offset (RFO) in OFDM systems. The algorithm estimates and compensates the most significant terms of the frequency domain ICI process, which are optimally selected via a Minimum Mean Squared Error (MMSE) criterion. The algorithm requires minimal knowledge of the phase process statistics, the estimation of which is also considered. The scheme outperforms previously proposed compensation methods of similar complexity, when severe phase impairments are present.

This paper proposes an enhanced receiver (Rx) configuration for multiple-input, multiple-output (MIMO) OFDM systems, operating under the composite effect of phase noise (PHN), residual frequency offset (RFO) and the transmission channel, herein modeled as quasi-static but unknown. The proposed Rx identifies the different impairments by exploiting their different time constants and compensates for each one accordingly. It includes a novel inter-frame fine frequency synchronization (FFS) scheme, which is closely coupled to an intra-frame adaptive phase synchronizer/channel estimator. The proposed scheme is evaluated for a 2 times 2, Alamouti space-time code (STC), and is shown to provide significant performance gain. The theory can be employed with any other STC scheme.

The simultaneous perturbation of an orthogonal frequency-division multiplexing receiver by phase noise plus a residual frequency offset (due to synchronization errors) is modeled here as a combined phase impairment, whose effect is evaluated analytically for the case of a frequency-selective fading channel. A nonpilot-aided (decision-directed) scheme is proposed, which compensates for the common (over all the subcarriers) phase-impairment effect. By representing the resulting intercarrier interference as an uncorrelated, unequal-variance process in the frequency domain, maximum-likelihood (ML) and approximate ML estimators of the complex-vector and phase-only types are derived and analytically evaluated. The present schemes are also compared with other current methods based on individual phase trackers, one per subcarrier. Finally, two suggestions are introduced for increasing the robustness of the algorithms to tentative-decision errors. It is demonstrated through simulations that the analysis is accurate, and that the proposed schemes achieve error-rate performance close to that of ideal compensation.

It is shown that a random phase noise Wiener process and a fixed but unknown residual (post-FFT) frequency offset, jointly perturbing an OFDM receiver, manifest themselves as a phase-rotation random variable which is common to all subcarriers, provided that their defining values (variance and fixed amount, respectively) are small, in which case the ICI (intercarrier interference) and the per-subcarrier amplitude distortion induced by them are both negligible. Assuming a known arbitrary channel profile, we propose and evaluate by simulations two decision-directed (i.e., non-pilot-based) compensation schemes, one intuitive and the other based on ML theory. The algorithms exhibit robustness despite the lack of pilots, whereas uncompensated performance degrades rapidly.