Publications

Average Rate and Error Probability Analysis in Short Packet Communications Over RIS-Aided URLLC Systems

In this paper, the average achievable rate and error probability of a reconfigurable intelligent surface (RIS) aided systems is investigated for the finite blocklength (FBL) regime. The performance loss due to the presence of phase errors arising from limited quantization levels as well as hardware impairments at the RIS elements is also discussed. First, the composite channel containing the direct path plus the product of reflected channels through the RIS is characterized. Then, the distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements, phase errors and the channels' path loss. Next, by considering the FBL regime, the achievable rate expression and error probability are identified and the corresponding average rate and average error probability are elaborated based on the proposed SNR distribution. Furthermore, the impact of the presence of phase error due to either limited quantization levels or hardware impairments on the average rate and error probability is discussed. The numerical results show that Monte Carlo simulations conform to matched Gamma distribution to received SNR for sufficiently large number of RIS elements. In addition, the system reliability indicated by the tightness of the SNR distribution increases when RIS is leveraged particularly when only the reflected channel exists. This highlights the advantages of RIS-aided communications for ultra-reliable and low-latency systems. The difference between Shannon capacity and achievable rate in FBL regime is also discussed. Additionally, the required number of RIS elements to achieve a desired error probability in the FBL regime will be significantly reduced when the phase shifts are performed without error.

6G System architecture: A service of services vision

The architectures of mobile networks have seen an unprecedented techno-economic transformation, fusing the telcommunications world within the cloud world, adding the spices of Software Engineering to the overall system design, and ultimately yielding the concept of Telco Cloud. This has brought significant benefits in terms of reducing expenditure and operational costs, flexibility in deployment, and a faster time to market. The key enablers are network function virtualization, software-defined networking, and edge/cloud computing. Artificial intelligence is also kicking in this arena. When all these technologies are well integrated, the creation and life-cycle management of fully programmable, flexible, service-tailored, and automated end-to-end network slices/services become possible. This will support diverse 5G and beyond 5G services, spanning from tactile IoT to pervasive robotics and immersive services. This paper introduces an unprecedented and disruptive vision for 6G that shifts the perception of future mobile networks from the old-fashioned concept of "network of networks" towards a new vision of "service of services." The paper then introduces the functional model of the envisioned system architecture, along with its components. It then provides a high-level description of the logical architecture.

Aggregation and Resource Scheduling in Machine-type Communication Networks: A Stochastic Geometry Approach

Data aggregation is a promising approach to enable massive machine-type communication (mMTC). This paper focuses on the aggregation phase where a massive number of machine-type devices (MTDs) transmit to aggregators. By using non-orthogonal multiple access (NOMA) principles, we allow several MTDs to share the same orthogonal channel in our proposed hybrid access scheme. We develop an analytical framework based on stochastic geometry to investigate the system performance in terms of average success probability and average number of simultaneously served MTDs, under imperfect successive interference cancellation (SIC) at the aggregators, for two scheduling schemes: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). We identify the power constraints on the MTDs sharing the same channel to attain a fair coexistence with purely orthogonal multiple access (OMA) setups, then power control coefficients are found so that these MTDs perform with similar reliability. We show that under high access demand, the hybrid scheme with CRS outperforms the OMA setup by simultaneously serving more MTDs with reduced power consumption.

Effective deployment of cooperative moving relay nodes in a high speed train

Recent trends show that mobile users accessing the wireless communication network from public transportation vehicles such as the high speed train are on the increase. To serve on-board users effectively, the use of a two-hop communication link is a promising solution. However existing two-hop schemes are expensive technologies and not integral part of the cellular network. We focus on a two-hop integrated cellular link which consists of the base station-to-train link and the train-to-on board users link which is created with the use of moving relay nodes (MRNs). The base station-to-train link is considered in a high speed scenario. We propose an iterative algorithm that can be used to efficiently select the appropriate number of MRNs and receive antennas in relation to the throughput performance and capital expenses.

High Reliability Downlink MU-MIMO: New OSTBC Approach and Superposition Modulated Side Information

In this paper, a proposal to improve the reliability of a downlink multiuser (MU) MIMO transmission scheme is investigated with the use of a new approach in orthogonal space-time block codes (OSTBC) and network coding with a superposition modulated system and side information. With the new encoded OSTBC approach, diversity is offered where each user receives all other users' symbols, which allows the recovery of symbols in several ways. In addition, multiple users can be accommodated with the same resource, which is quite useful in a wireless system where resources are always restricted. By employing superposition modulation, the side information needed for error recovery can be transmitted over the same resource used for the normal information frame. In addition, the proposed system exploits diversity through a novel technique of sub-constellation alignment-based signal combining for efficient side information dissemination. A detailed analysis of the new OSTBC approach is carried out. It is shown that the performance of the MU-MIMO system can be improved significantly in terms of block and frame error rates (BLER, FER) considered as reliability measures. By accommodating a reasonable number of multiple users, high reliability is achieved at the expense of the rate. To compensate for the low rate, conventional OSTBC can be considered and simulation results are shown, where, as a penalty to pay, multiple orthogonal resources are required.

Resource Allocation for Co-Primary Spectrum Sharing in MIMO Networks

We study co-primary spectrum sharing concept in two small cell multiuser network. Downlink transmission is explored with Rayleigh fading in interfering broadcast channel. Both base stations and all the users are equipped with multiple antennas. Resource allocation with joint precoder and decoder design is proposed for weighted sum rate (WSR) maximization problem. The problem becomes mixed-integer and non-convex. We factor the main objective problem into two subproblems. First subproblem is multiuser with subcarrier allocation where we assume that each subcarrier can be allocated to multiple users. Gale-Shapley algorithm based on stable marriage problem and transportation method are implemented for subcarrier allocation part. For the second subproblem, a joint precoder and decoder design is proposed to obtain the optimal solution for WSR maximization. Monte Carlo simulation is employed to obtain the results.

Practical Challenges for Reliable RIS Deployment in Heterogeneous Multi-Operator Multi-Band Networks

Ultra-low Latency, Low Energy and Massiveness in the 6G Era via Efficient CSIT-limited Schemes

Channel state information (CSI) has been a key component in traditional wireless communication systems. This might no longer hold in future networks supporting services with stringent quality of service constraints such as extremely low-latency, low-energy and/or large number of simultaneously connected devices, where acquiring CSI would become extremely costly or even impractical. We overview the main limitations of CSI at transmitter side (CSIT)-based designs toward the 6G era, assess how to design and use efficient CSIT-limited schemes that allow meeting the new and stringent requirements, and highlight some key research directions. We delve into the ideas of efficiently allocating pilot sequences, relying on statistical CSIT and/or using location-based strategies, and demonstrate viability via a selected use case.

Learning-Based Orchestration for Dynamic Functional Split and Resource Allocation in vRANs

One of the key benefits of virtualized radio access networks (vRANs) is network management flexibility. However, this versatility raises previously-unseen network management challenges. In this paper, a learning-based zero-touch vRAN orchestration framework (LOFV) is proposed to jointly select the functional splits and allocate the virtualized resources to minimize the long-term management cost. First, testbed measurements of the behaviour between the users' demand and the virtualized resource utilization are collected using a centralized RAN system. The collected data reveals that there are non-linear and non-monotonic relationships between demand and resource utilization. Then, a comprehensive cost model is proposed that takes resource overprovisioning, declined demand, instantiation and reconfiguration into account. Moreover, the proposed cost model also captures different routing and computing costs for each split. Motivated by our measurement insights and cost model, LOFV is developed using a model-free reinforcement learning paradigm. The proposed solution is constructed from a combination of deep Q-learning and a regression-based neural network that maps the network state and users' demand into split and resource control decisions. Our numerical evaluations show that LOFV can offer cost savings by up to 69\% of the optimal static policy and 45\% of the optimal fully dynamic policy.

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Throughput of wireless networks with Poisson distributed nodes using location information

This paper provides a statistical characterization of the individual achievable rates and the spatial throughput of bipolar Poisson wireless networks. We derive closed-form expressions for the probability density function of the achievable rates under two decoding rules: treating interference as noise, and jointly detecting the strongest interfering signals treating the others as noise. Based on these rules and the bipolar model, we approximate the expected maximum spatial throughput, showing the best performance of the latter decoding rule. We show that, when the same decoding rule and density are considered, the cognitive spatial throughput always outperforms the other option.

Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs

Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to jointly reconfigure the functional splits of the base stations (BSs), locations of the virtualized central units (vCUs) and distributed units (vDUs), their resources, and the routing for each BS data flow. The objective is to minimize the long-term total network operation cost while adapting to the varying traffic demands and resource availability. Testbed measurements are performed to study the relationship between the traffic demands and computing resources, which reveals high variance and depends on the platform and its load. Consequently, finding the perfect model of the underlying system is non-trivial. Therefore, to solve the proposed problem, a deep reinforcement learning (RL)-based framework is proposed and developed using model-free RL approaches. Moreover, the problem consists of multiple BSs sharing the same resources, which results in a multi-dimensional discrete action space and leads to a combinatorial number of possible actions. To overcome this curse of dimensionality, action branching architecture, which is an action decomposition method with a shared decision module followed by neural network is combined with Dueling Double Deep Q-network (D3QN) algorithm. Simulations are carried out using an O-RAN compliant model and real traces of the testbed. Our numerical results show that the proposed framework successfully learns the optimal policy that adaptively selects the vRAN configurations, where its learning convergence can be further expedited through transfer learning even in different vRAN systems. It offers significant cost savings by up to 59\% of a static benchmark, 35\% of DDPG with discretization, and 76\% of non-branching D3QN.

On Dimensions of OTA Setups for Massive MIMO Base Stations Radiated Testing

The development of base stations (BSs) with large aperture antenna arrays, enabled partially by the utilization of cmWave and mmWave frequency bands, will require radiated testing in fading conditions. In this paper, the objective is to investigate the suitable measurement distances and physical dimensions of the over-the-air setups for the performance evaluation of massive multiple-input multiple-output (MIMO) BSs in anechoic chambers with multiple probes. Setup dimension is the major cost factor in the test systems and is thus the key issue to be investigated. The purpose is to determine whether the conventional far field criteria must be followed when determining the range of the setup or if they can be relieved. The impact of limited test setup dimension on various metrics, e.g., far field criteria, field performance within the test area, system link budget analysis, direction of arrival estimation algorithm as well as multi-user MIMO sum-rate capacity are investigated to determine the range of the test setup. It was found that the link budget does not support for the measurement distances claimed by the Fraunhofer distance. Most of the utilized metrics, especially the sum rate capacity, indicate that smaller setup sizes can still yield reasonable measurement accuracy. Simulations were performed at 2.6, 3.5, and 28 GHz frequencies.

Challenges in future broadband radio access, co-operative communications and self-organising networks

Despite of great improvements in e.g. mobile cellular networks during the past decade, the changes in the future may be even more dramatic. Several enabling basic technological barriers seem to become solved in the near future. This will potentially cause dramatic changes the way commercial wireless networks will function and deliver services for users. In this paper, an overview of critical enabling technologies which are foreseen to change the future wireless networking is given.

Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mm-wave Networks

Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, we investigate the problem of path selection and rate allocation for multi-hop self-backhaul millimeter wave (mmWave) networks. Enabling multi-hop mmWave transmissions raises a potential issue of increased latency, and thus, in this work we aim at addressing the fundamental questions: how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints? In this regard, we propose a new system design, which exploits multiple antenna diversity, mmWave bandwidth, and traffic splitting techniques to improve the downlink transmission. The studied problem is cast as a network utility maximization, subject to an upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: path selection and rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a nonconvex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, we provide a comprehensive performance analysis and convergence proofs for the proposed solution. Numerical results show that our approach ensures reliable communication with a guaranteed probability of up to $99.9999\%$, and reduces latency by $50.64\%$ and $92.9\%$ as compared to baseline models. Furthermore, the results showcase the key trade-off between latency and network arrival rate.

Robust Joint Precoder-Decoder Design for PNC Based MIMO Two-Way Relaying System

In this paper, we investigate a robust joint precoder-decoder design scheme for a multiple-input multiple-output (MIMO) physical layer network coding (PNC) based two-way relay system. Precoders at the source nodes and decoder at the relay node are used to facilitate PNC operations during multiple access stage. Both channel estimation error and antenna correlations are used to formulate an optimization problem to minimize weighted mean square error (WMSE) under a total power constraint. The problem becomes non- convex, and we propose an algorithm to solve it optimally. The effect of estimation error, antenna correlation parameters, weighting parameters, and number of antennas at nodes are also considered in the numerical analysis.

Weighted Sum-Rate Maximization for Full-Duplex MIMO Interference Channels

We consider a K link multiple-input multiple-output (MIMO) interference channel, where each link consists of two full-duplex (FD) nodes exchanging information simultaneously in a bi-directional communication fashion. The nodes in each pair suffer from self-interference due to operating in FD mode, and inter-user interference from other links due to simultaneous transmission at each link. We consider the transmit and receive filter design for weighted sum-rate (WSR) maximization problem subject to sum-power constraint of the system or individual power constraints at each node of the system. Based on the relationship between WSR and weighted minimum-mean-squared-error (WMMSE) problems for FD MIMO interference channels, we propose a low complexity alternating algorithm which converges to a local WSR optimum point. Moreover, we show that the proposed algorithm is not only applicable to FD MIMO interference channels, but also applicable to FD cellular systems in which a base station (BS) operating in FD mode serves multiple uplink (UL) and downlink (DL) users operating in half-duplex (HD) mode, simultaneously. It is shown in simulations that the sum-rate achieved by FD mode is higher than the sum-rate achieved by baseline HD schemes.

A Low-Complexity Beamforming Design for Multiuser Wireless Energy Transfer

Wireless energy transfer (WET) is a green enabler of low-power Internet of Things (IoT). Therein, traditional optimization schemes relying on full channel state information (CSI) are often too costly to implement due to excessive energy consumption and high processing complexity. This letter proposes a simple, yet effective, energy beamforming scheme that allows a multi-antenna power beacon (PB) to fairly power a set of IoT devices by only relying on the first-order statistics of the channels. In addition to low complexity, the proposed scheme performs favorably as compared to benchmarking schemes and its performance improves as the number of PB's antennas increases. Finally, it is shown that further performance improvement can be achieved through proper angular rotations of the PB.