The recent paradigm shift towards transmitting 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 $10 \times 10$ 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. Moreover, for $16 \times 16$ both hard- and soft-output MIMO systems, approximate MultiSphere versions achieve similar error rate performance with state-of-the art approximate SDs with akin parallelization properties, by using only one tenth of the processing elements and to achieve up to approximately 9× increased energy efficiency.

The Open Radio Access Network (RAN) concept has been gaining wide acceptance in recent network architectures, including 5G New Radio (NR) network deployments. Current Open RAN radio implementation efforts, aim at integrating several white-box hardware elements and executing digital processing on open-source software. When building such a software-based, 5G Open RAN platform, challenges include achieving real-time execution times for demanding computational blocks of the 5G NR physical layer processing, such as Low Density Parity Check (LDPC) decoding. In this context, having already identified both the capabilities as well as the challenges that Field-programmable Gate Arrays (FPGAs) offer for accelerating LDPC, we present our novel LDPC FPGA accelerator system. In this paper, we contribute the implementation details of our FPGA accelerator design as well as the process of integrating the accelerator with OpenAirInterface (OAI), the basis for our 5G NR platform. For the first time in the literature, we show an FPGA-based LDPC accelerator fully integrated with a complete software platform, that is able to achieve more than 1.6Gbps decoding throughput and up to 13 times faster execution times compared to single core software implementations. Finally, in our results, we show that LDPC encoding is more challenging to accelerate due to lower computational complexity.

Millimeter wave (mmWave) systems with effective beamforming capability play a key role in fulfilling the high data-rate demands of current and future wireless technologies. Hybrid analog-to-digital beamformers have been identified as a cost-effective and energy-efficient solution towards deploying such systems. Most of the existing hybrid beamforming architectures rely on a subconnected phase shifter network with a large number of antennas. Such approaches, however, cannot fully exploit the advantages of large arrays. On the other hand, the current fully-connected beamformers accommodate only a small number of antennas, which substantially limits their beamforming capabilities. In this paper, we present a mmWave hybrid beamformer testbed with a fully-connected network of phase shifters and adjustable attenuators and a large number of antenna elements. To our knowledge, this is the first platform that connects two RF inputs from the baseband to a 16× 8 antenna array, and it operates at 26 GHz with a 2 GHz bandwidth. It provides a wide scanning range of 60°, and the flexibility to control both the phase and the amplitude of the signals between each of the RF chains and antennas. This beamforming platform can be used in both short and long-range communications with linear equivalent isotropically radiated power (EIRP) variation between 10 dBm and 60 dBm. In this paper, we present the design, calibration procedures and evaluations of such a complex system as well as discussions on the critical factors to consider for their practical implementation.

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.

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 nonorthogonal 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 nonorthogonal 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%.

Sphere decoding (SD) has been proposed as an efficient way to perform maximum-likelihood (ML) decoding of Polar codes. Its latency requirements, however, are determined by its ability to promptly exclude from the ML search (i.e., prune) large parts of the corresponding SD tree, without compromising the ML optimality. Traditional depth-first approaches initially find a "promising" candidate solution and then prune parts of the tree that cannot result to a "better" solution. Still, if this candidate solution is far (in terms of Euclidean distance) from the ML one, pruning becomes inefficient and decoding latency explodes. To reduce this processing latency, an early termination approach is, first, introduced that exploits the binary nature of the transmitted information. Then, a simple but very efficient SD approach is proposed that performs multiple tree searches that perform decreasingly aggressive pruning. These searches are almost independent and can take place sequentially, in parallel, or even in a hybrid (sequential/parallel) manner. For Polar codes of 128 block size, both realizations can provide a latency reduction of up to four orders of magnitude compared to state-of-the-art Polar sphere decoders. Then, a further 50% latency reduction can be achieved by exploiting the parallel nature of the approach.