Towards low complexity matching theory for uplink wireless communication systems

Millimetre wave (mm-Wave) technology is considered a promising direction to achieve the high quality of services (QoSs) because it can provide high bandwidth, achieving a higher transmission rate due to its immunity to interference. However, there are several limitations to utilizing mm-Wave technology, such as more extraordinary precision hardware is manufactured at a higher cost because the size of its components is small. Consequently, mm-Wave technology is rarely applicable for long-distance applications due to its narrow beams width. Therefore, using cell-free massive multiple input multiple output (MIMO) with mm-Wave technology can solve these issues because this architecture of massive MIMO has better system performance, in terms of high achievable rate, high coverage, and handover-free, than conventional architectures, such as massive MIMO systems’ co-located and distributed (small cells). This technology necessitates a significant amount of power because each distributed access point (AP) has several antennas. Each AP has a few radio frequency (RF) chains in hybrid beamforming. Therefore more APs mean a large number of total RF chains in the cell-free network, which increases power consumption. To solve this problem, deactivating some antennas or RF chains at each AP can be utilized. However, the size of the cell-free network yields these two options as computationally demanding. On the other hand, a large number of users in the cell-free network causes pilot contamination issue due to the small length of the uplink training phase. This issue has been solved in the literature based on two options: pilot assignment and pilot power control. Still, these two solutions are complex due to the cell-free network size. Motivated by what was mentioned previously, this thesis proposes a novel technique with low computational complexity based on matching theory for antenna selection, RF chains activation, pilot assignment and pilot power control. The first part of this thesis provides an overview of matching theory and the conventional massive MIMO systems. Then, an overview of the cell-free massive MIMO systems and the related works of the signal processing techniques of the cell-free mm-Wave massive MIMO systems to maximize energy efficiency (EE), are provided. Based on the limitations of these techniques, the second part of this thesis presents a hybrid beamforming architecture with constant phase shifters (CPSs) for the distributed uplink cell-free mm-Wave massive MIMO systems based on exploiting antenna selection to reduce power consumption. The proposed scheme uses a matching technique to obtain the number of selected antennas which can contribute more to the desired signal power than the interference power for each RF chain at each AP. Therefore, the third part of this thesis solves the issue of the huge complexity of activating RF chains by presenting a low-complexity matching approach to activate a set of RF chains based on the Hungarian method to maximize the total EE in the centralized uplink of the cell-free mm-Wave massive MIMO systems when it is proposed hybrid beamforming with fully connected phase shifters network. The pilot contamination issue has been discussed in the last part of this thesis by utilizing matching theory in pilot assignment and pilot power control design for the uplink of cell-free massive MIMO systems to maximize SE. Firstly, an assignment optimization problem has been formulated to find the best possible pilot sequences to be inserted into a genetic algorithm (GA). Therefore, the GA will find the optimal solution. After that, a minimum-weighted assignment problem has been formulated regarding the power control design to assign pilot power control coefficients to the quality of the estimated channel. Then, the Hungarian method is utilized to solve this problem. The simulation results of the proposed matching theory for the mentioned issues reveal that the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes for antenna selection and RF chain activation. In addition, the proposed matching schemes outperform the state-of-the-art techniques concerning the pilot assignment and the pilot power control design. This means that network scalability can be guaranteed with low computational complexity

Complexity results for the Pilot Assignment problem in Cell-Free Massive MIMO

Wireless communication is enabling billions of people to connect to each other and the internet, transforming every sector of the economy, and building the foundations for powerful new technologies that hold great promise to improve lives at an unprecedented rate and scale. The rapid increase in the number of devices and the associated demands for higher data rates and broader network coverage fuels the need for more robust wireless technologies. The key technology identified to address this problem is referred to as Cell-Free Massive MIMO (CF-mMIMO). CF-mMIMO is accompanied by many challenges, one of which is efficiently allocating limited resources. In this paper, we focus on a major resource allocation problem in wireless networks, namely the Pilot Assignment problem (PA). We show that PA is strongly NP-hard and that it does not admit a polynomial-time constant-factor approximation algorithm. Further, we show that PA cannot be approximated in polynomial time within $\mathcal{O}(K^2)$ (where $K$ is the number of users) when the system consists of at least three pilots. Finally, we present an approximation lower bound of $1.058$ (resp. $\epsilon|K|^2$, for $\epsilon >0$) in special cases where the system consists of exactly two (resp. three) pilots.Comment: 20 pages, 0 figure

Soft Handover Procedures in mmWave Cell-Free Massive MIMO Networks

This paper considers a mmWave cell-free massive MIMO (multiple-input multiple-output) network composed of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) via coherent joint transmission. We address UE mobility in the downlink (DL) with imperfect channel state information (CSI) and pilot training. Aiming at extending traditional handover concepts to the challenging AP-UE association strategies of cell-free networks, distributed algorithms for joint pilot assignment and cluster formation are proposed in a dynamic environment considering UE mobility. The algorithms provide a systematic procedure for initial access and update of the serving APs and assigned pilot sequence to each UE. The principal goal is to limit the necessary number of AP and pilot changes, while limiting computational complexity. We evaluate the performance, in terms of spectral efficiency (SE), with maximum ratio and regularized zero-forcing precoding. Results show that our proposed distributed algorithms effectively identify the essential AP-UE association refinements with orders-of-magnitude lower computational time compared to the state-of-the-art. It also provides a significantly lower average number of pilot changes compared to an ultra-dense network (UDN). Moreover, we develop an improved pilot assignment procedure that facilitates massive access to the network in highly loaded scenarios.Comment: 14 pages, Accepted in IEEE Transactions on Wireless Communication

Massive Unsourced Random Access: Exploiting Angular Domain Sparsity

This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced K-means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes

A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO

Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios. Recently, open-RAN (O-RAN) techniques have begun adding enormous flexibility to RAN implementations. O-RAN is a natural architectural fit for cell-free massive multiple-input multiple-output (CFmMIMO) systems, where many geographically-distributed access points (APs) are employed to achieve ubiquitous coverage and enhanced user performance. In this paper, we address the decentralized pilot assignment (PA) problem for scalable O-RAN-based CFmMIMO systems. We propose a low-complexity PA scheme using a multi-agent deep reinforcement learning (MA-DRL) framework in which multiple learning agents perform distributed learning over the O-RAN communication architecture to suppress pilot contamination. Our approach does not require prior channel knowledge but instead relies on real-time interactions made with the environment during the learning procedure. In addition, we design a codebook search (CS) scheme that exploits the decentralization of our O-RAN CFmMIMO architecture, where different codebook sets can be utilized to further improve PA performance without any significant additional complexities. Numerical evaluations verify that our proposed scheme provides substantial computational scalability advantages and improvements in channel estimation performance compared to the state-of-the-art.Comment: This paper has been submitted to IEEE Journal on Selected Areas in Communications for possible publicatio

Resource Allocation for Cell-Free Massive MIMO-aided URLLC Systems Relying on Pilot Sharing

Resource allocation is conceived for cell-free (CF) massive multi-input multi-output (MIMO)-aided ultra-reliable and low latency communication (URLLC) systems. Specifically, to support multiple devices with limited pilot overhead, pilot reuse among the users is considered, where we formulate a joint pilot length and pilot allocation strategy for maximizing the number of devices admitted. Then, the pilot power and transmit power are jointly optimized while simultaneously satisfying the devices’ decoding error probability, latency, and data rate requirements. Firstly, we derive the lower bounds (LBs) of ergodic data rate under finite channel blocklength (FCBL). Then, we propose a novel pilot assignment algorithm for maximizing the number of devices admitted. Based on the pilot allocation pattern advocated, the weighted sum rate (WSR) is maximized by jointly optimizing the pilot power and payload power. To tackle the resultant NP-hard problem, the original optimization problem is first simplified by sophisticated mathematical transformations, and then approximations are found for transforming the original problems into a series of subproblems in geometric programming (GP) forms that can be readily solved. Simulation results demonstrate that the proposed pilot allocation strategy is capable of significantly increasing the number of admitted devices and the proposed power allocation achieves substantial WSR performance gain