Massive multiple-input multiple-output (MIMO) stands as a key technology for advancing performance metrics such as data rate, reliability, and spectrum efficiency in the fifth generation (5G) and beyond of wireless networks. However, its efficiency depends greatly on obtaining accurate channel state information (CSI). This task becomes particularly challenging with increasing user mobility. In this paper, we focus on an uplink scenario in which a massive MIMO base station serves multiple high-mobility users. We leverage variational Bayesian (VB) inference for joint channel estimation and data detection (JED), tailored for time-varying channels. In particular, we use the VB framework to provide approximations of the true posterior distributions. To cover more real-world scenarios, we assume the time correlation coefficients associated with the channels are unknown. Our simulations demonstrate the efficacy of our proposed VB-based approach in tracking these unknown time correlation coefficients. We present two processing strategies within the VB framework: online and block processing strategies. The online strategy offers a low-complexity solution for a given time slot, requiring only the knowledge of the parameters/statistics within that time slot. In contrast, the block processing strategy focuses on the entire communication block and processes all received signals together to reduce channel estimation errors. Additionally, we introduce an interleaved structure for the online processing strategy to further enhance its performance. Finally, we conduct a comparative analysis of our VB approach against the linear minimum mean squared error (LMMSE), the Kalman Filter (KF), and the expectation propagation (EP) methods in terms of symbol error rate (SER) and channel normalized mean squared error (NMSE). Our findings reveal that our VB framework surpasses these benchmarks across the performance metrics.

We propose a new reconfigurable intelligent surface (RIS) structure, referred to as interconnected RIS (I-RIS), which allows the RIS elements to be interconnected and share the inci-dent signals using simple binary radio frequency (RF) switches and mix them into the reflecting signals. This structure enables multi-user scaling and requires fewer elements (i.e., a compact structure) compared to standard RIS (S-RIS), which assumes no interconnection between the elements. The I-RIS compact design makes it practical for deployment on space-limited nodes, e.g., unmanned aerial vehicles (UAVs). Hence, in this work, we propose a beamforming design based on I-RIS in a multi-user network, where we use binary RF switches as RIS elements. We show that our switch-based I-RIS offers a higher gain compared to an S-RIS using phase shifters. Finally, we introduce two optimization methods, sigmoid filled function (SFF) and semi-definite binary optimization (SBO), to optimize the RIS elements and evaluate their performance in terms of sum-rate and comolexity.

In this article, we study reconfigurable intelligent surface (RIS)-assisted networks to support Internet of Things (IoT) devices. We propose RIS beamforming strategies to maximize sum rate and fairness and analyze the RIS location. We derive a theoretical lower bound of the minimum number of RIS elements needed to guarantee specific network performance metrics and validate our results via simulations. We present two RIS scenarios in this study, both with the same total number of RIS elements: 1) centralized RIS, where a single RIS assists the network and 2) distributed RIS, where each transmitter has its own dedicated RIS. We study addressing two practical challenges related to RIS elements and channel state information (CSI) assumptions. First, we consider hardware limitations by assuming that each RIS element is equipped with a discrete phase shifter (PS). Second, we investigate the impact of CSI perfectness and availability in the network; therefore, we evaluate the performance of the RIS-assisted network under two scenarios: 1) centralized RIS with imperfect global CSI and 2) distributed RIS, where imperfect local CSI is available at each transmitter.

Motivated by the DNA storage paradigm, we consider the torn-paper channel (TPC), which models data storage in long DNA molecules and breaks the input sequence into a random number of out-of-order variable-length non-overlapped fragments. We propose a computationally-efficient code construction for this model. More specifically, we introduce a family of nested Varshamov-Tenengolts (VT) codes to merge and sort the fragments in order to recover the stored data. We numerically show that our scheme (i) obtains rates that are higher than in prior results, (ii) has a decoding complexity that is cubic in the number of codeword fragments, which is significantly lower than the complexity of the brute-force approach, and (iii) offers decreasing and negligible error rates as the codeword length increases. We also propose a new construction for VT codes, quantify the number of required parity bits, and show that our approach requires fewer parity bits compared to known results.

With the turn of new decade, wireless communications face a major challenge on connecting many more new users and devices, at the same time being energy efficient and minimizing its carbon footprint. However, the current approaches to address the growing number of users and spectrum demands, like Massive MIMO, demand exorbitant energy consumption. The reason is that traditionally Massive MIMO requires a digital beamforming architecture that needs a separate RF chain per antenna, so the power consumption scales with number of antennas. Instead, GreenMO creates a new Massive MIMO architecture with just a single physically laid RF chain, shared by all the antennas and introduces for the first time, the concept of virtualizing the RF chain hardware. That is, GreenMO creates an optimal number of virtual RF chains to serve a given number of spatial streams, depending on channel conditions and network load. Due to efficient, softwarized control over the number of virtual RF chains, GreenMO paves the way for green and flexible massive MIMO. We prototype GreenMO on a PCB with eight antennas and evaluate it with a WARPv3 SDR platform in an office environment. The results demonstrate that GreenMO is 3× more power-efficient than traditional Massive MIMO and 4× more spectrum-efficient than traditional OFDMA systems, while multiplexing 4 spatial streams, and can save upto 50% power in modern 5G NR base stations.

Digital and analog beamforming are well-known methods to suppress interference using multiple-antenna structures, but they have practical limitations: (i) Digital beamforming requires multiple analog-to-digital converters (ADCs) to enable digital conversion, which increases the cost and complexity; (ii) Although analog beamforming does not require expensive ADCs, it uses phase shifters, which cause quantization errors, insertion losses, and reduced power efficiency. In this paper, we consider a$K$-user uplink interference channel and propose a low-complexity algorithmic interference-suppression solution relying on simple switch-based reconfigurable antennas at the receivers. We utilize switches to enable/disable antennas to maximize each user’s signal-to-interference-plus-noise ratio (SINR). We present an optimization approach to approximate the optimal solution. To evaluate the results, we compare our method with relevant benchmarks. Moreover, we derive a lower bound on the minimum number of antenna elements per receiver to attain the desired SINR and verify the findings via simulations.

Recent information-theoretic studies have resulted in several interference management (IM) techniques that promise significant capacity improvements over interference avoidance techniques. However, in practice, the stable throughput region is a more relevant metric compared to the capacity region. In this work, we focus on the stable throughput region of a two-pair intermittent interference network with distributed transmitters and propose a queue-based transmission protocol in different regimes to handle the data between queues. In this context, we translate physical-layer IM protocols to accommodate stochastic message arrivals. To evaluate our proposed techniques, we compare the stable throughput region to the capacity region and show, through simulations, that the stable throughput region matches the capacity region when the latter is known. We show that in order to achieve the optimal stable throughput region, new ingredients are needed when compared to prior results. We quantify the trade-off between encoding/decoding complexity of the proposed scheme (in terms of number of required algebraic operations), and the achievable rates. Finally, we study the lifetime of messages (i.e. the duration from arrival to successful delivery) versus the total communication time, and we observe that the average lifetime scales as the square root of the total communication time.