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The proliferation of mobile devices has fueled the demand for indoor location-based services. Consequently, a plethora of techniques have emerged to facilitate object and device localization in indoor environments. Among these, fingerprint-based indoor localization systems, which leverage machine learning, stand out as a promising solution for providing accurate localization. Nonetheless, their performance is inherently reliant on the accuracy of the underlying database, and any changes in the indoor layout can significantly impact the wireless signals, subsequently affecting the localization accuracy. To circumvent this issue, this work proposes a novel access point (AP) selection framework to enhance the robustness of fingerprint-based indoor positioning systems in dynamic indoor environments. More specifically, a hybrid Wi-Fi and BLE fingerprint database is constructed by collecting received signal strength (RSS) from the pre-defined reference points (RPs). To ensure that the fingerprint database remains relevant over time, some RPs are designated as known points so that the system can periodically collect new RSS at the known points. Subsequently, the proposed scheme computes the differences between the RSS of the database and the updated RSS from the new layout to account for the changes occurred. The building-based, floor-based, and zone-based implementation modes determine which APs are reliable to be utilized during localization based on the RSS discrepancies observed in building, floor, and zone, respectively. Results demonstrate that the proposed building-based, floor-based, and zone-based AP selection schemes could achieve reduction in positioning error up to 28.86%, 33.53%, and 39.66%, respectively, compared to the baseline technique without AP selection schemes.

Distributed MIMO systems leverage multiple access points (APs) distributed across a geographical area to enhance system performance and provide robust connectivity to users by transmitting independent data streams simultaneously, thus improving coverage and capacity. The nonlinear power amplifiers (PAs) employed at the APs can significantly distort the transmitted signal and degrade the spectral efficiency (SE), as PAs are more efficient when operating close to saturation. In this paper, we present an efficient power allocation framework in a multi-stream distributed massive MIMO system with nonlinear PAs, where APs send multiple spatial streams to the user equipment (UE). With the objective of maximizing the SE while adhering to total input power constraints, we first formulate the power allocation problem as nonlinear programming, which is first solved using the multiplier punitive function-based method. Moreover, we focus on the asymptotic analysis of the SE when multi-stream interference among APs is negligible. This can be achieved through ideal spatial multiplexing with interference nulling, assuming a sufficiently large antenna at the UE, or through interference-free frequency multiplexing. We examine the optimality of the water-filling method in power allocation and extend it by considering the PA’s nonlinearity. Simulation results show that the proposed power allocation method can maximize the achievable rate while considering the PA nonlinearity, thereby preventing degradation in performance.

Received signal strength indicator (RSSI) of WiFi access point (AP) is a primary sensor data used for indoor fingerprint localization. User sends its online RSSI to server to estimate its position by matching with RSSI fingerprints database which built in the offline phase. An important goal of indoor fingerprint localization is to increase the accuracy while reduce the storage cost. Meanwhile, APs perform different effects on target estimation and mapping metric in RSSI fingerprint localization. In this paper, a novel localization model based on the nonuniform quantization RSSI entropy (NQRELoc) is proposed to address these problems. First, to select the APs that contribute more to the localization, the nonuniform quantization RSSI entropy (NQRE) is introduced to quantify AP’s discernibility and select APs whose signals show sufficient differentiation to construct an offline fingerprints database. Then, the entropy-weighted euclidean distance (EWED) is used as a metric to measure the similarity of online RSSI vectors and offline RPs fingerprints. Finally, NQRELoc locates the target by the improved entropy-weighted K nearest neighbor (IEWKNN) algorithm, which takes the APs effect into target estimation. To validate the proposed algorithm, a large scale of experiments and simulations are implemented. The results demonstrate that NQRELoc can not only reduce the storage overhead but also improve the positioning accuracy compared to the other existing techniques. [ABSTRACT FROM AUTHOR]

Next-generation Home WiFi networks have to step forward in terms of performance. New applications such as on-line games, virtual reality or high quality video contents will further demand higher throughput levels, as well as low latency. Beyond physical (PHY) and medium access control (MAC) improvements, deploying multiple access points (APs) in a given area may significantly contribute to achieve those performance goals by simply improving average coverage and data rates. However, it opens a new challenge: to determine the best AP for each given station (STA). This article studies the achievable performance gains of using secondary APs, also called Extenders, in Home WiFi networks in terms of throughput and delay. To do that, we introduce a centralized, easily implementable channel load aware selection mechanism for WiFi networks that takes full advantage of IEEE 802.11k/v capabilities to collect data from STAs, and distribute association decisions accordingly. These decisions are completely computed in the AP (or, alternatively, in an external network controller) based on an AP selection decision metric that, in addition to RSSI, also takes into account the load of both access and backhaul wireless links for each potential STA-AP/Extender connection. Performance evaluation of the proposed channel load aware AP and Extender selection mechanism has been first conducted in a purpose-built simulator, resulting in an overall improvement of the main analyzed metrics (throughput and delay) and the ability to serve, at least, 35% more traffic while keeping the network uncongested when compared to the traditional RSSI-based WiFi association. This trend was confirmed when the channel load aware mechanism was tested in a real deployment, where STAs were associated to the indicated AP/Extender and total throughput was increased by 77.12%.

In this paper, we investigate the selection problem of access points (APs) in cell-free massive multiple-input multiple-output (MIMO) systems, where APs equipped with a large number of antennas are geographically distributed over a wide area with no cell border. These APs simultaneously serve many users, which are randomly distributed all over the area. We first derive formulas to calculate two proposed metrics used to measure the effective channel gain from all users to all APs and the channel quality of each user. Moreover, these metrics are only based on large-scale fading coefficients, which change very slowly in time. Next, we propose an algorithm to effectively sort and connect users to each AP in a sequential manner using these proposed metrics. Simulation results show that cell-free massive MIMO systems using proposed scheme have better performance compared to existing schemes.

How to meet the demand for increasing number of users, higher data rates, and stringent quality-of-service (QoS) in the beyond fifth-generation (B5G) networks? Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising solution, in which many wireless access points cooperate to jointly serve the users by exploiting coherent signal processing. However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital. In this paper, we propose a new framework for structured massive access in cell-free massive MIMO systems, which comprises one initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and one fractional power control policy. New closed-form spectral efficiency (SE) expressions with maximum ratio (MR) combining are derived. The simulation results show that our proposed framework provides high SE when using local partial minimum mean-square error (LP-MMSE) and MR combining. Specifically, the proposed initial access algorithm and pilot assignment schemes outperform their corresponding benchmarks, P-LSFD achieves scalability with a negligible performance loss compared to the conventional optimal large-scale fading decoding (LSFD), and scalable fractional power control provides a controllable trade-off between user fairness and the average SE. [ABSTRACT FROM AUTHOR]

To ensure an efficient coverage and allow transparent mobility, it is common to find a number of access points (APs) deployed in the neighborhood of each other in current WLANs. A mobile terminal (MT) often has multiple potential APs that it can connect to, and the selection of an appropriate AP becomes an important factor affecting both the network performance and user experience. This paper proposes a new AP selection scheme, which is based on the analysis of achievable normalized throughputs (ANTs) that the MT could obtain in the network. Specifically, we first compute the normalized throughput that characterizes the channel competition among multiple basic service sets (BSSs) as well as MTs within the same BSS. By evaluating ANTs in situations where the MT associates with different APs, the most appropriate AP for the MT can then be selected. Then, we present the implementation of the proposed AP selection scheme under the SDN-WiFi framework, in which the AP selection process can be performed in a software manner. The experimental results show that our computations on ANTs are of high accuracy and the proposed AP selection scheme can achieve a better performance than the existing schemes in terms of both the quality of service of MTs and the fairness among MTs.

An efficient pilot assignment scheme based on graph coloring is proposed to mitigate the severe pilot contamination effect in cell-free massive multiple-input multiple-output systems. By exploiting the large-scale fading coefficients between the access points (APs) and the user equipments, we first utilize an AP selection algorithm to construct an interference graph. Then, the optimal pilot assignment can be achieved by updating the interference graph. In addition, we consider a fair policy for pilot reuse to improve the system performance. Numerical results verify that, compared with conventional pilot assignment schemes, the proposed graph coloring based pilot assignment scheme can significantly increase the 95 $\%$ -likely per-user throughput and reduce the complexity of assigning pilot sequences. [ABSTRACT FROM AUTHOR]

As the deployment of WLANs becomes extensive, users commonly have to select one access point (AP) for association among multiple available APs. However, the existing AP selection policies are not sufficient enough due to their drawbacks (e.g., imbalanced load and time consuming for association). In this paper, we present a collaborative filtering (CF) recommendation-based AP selection strategy in wireless local area networks (WLANs) with differentiated access service. At first, a differentiated AP selection strategy by utilizing analytic hierarchy process (AHP) is leveraged to optimize AP selection metric with multiple objectives for different users. Furthermore, an improved differentiated AP selection approach by CF recommendation is investigated. Based on our method, the new user can associate with the recommended AP promptly instead of channel scanning and selection metric computation. Meanwhile, load prebalancing module periodically excludes the AP considered to be overloaded from the recommendation list, while the pioneer user (PU) updates the corresponding module periodically with an adaptive method to replace the AP with low recommendation efficiency. Simulation results demonstrate that our presented strategies can provide differentiated quality-of-service (QoS) assurance to users, improve overall network throughput, balance network traffic, and degrade association time cost. [ABSTRACT FROM PUBLISHER]

With the development of IEEE 802.11 MAC (Medium Access Control) protocols, efficient utilization of wireless local area networks (WLANs) has become a very important issue. In typical IEEE 802.1] networks, the association between mobile users (MUs) and access point (AP) is based on the signal strength information. As a result, it often results in the extremely unfair bandwidth allocation among MUs. In this paper, we propose a distributed association algorithm to achieve load balancing among the APs. The proposed algorithm gradually balances the AP loads for the available multiple bit rate choices in a distributed manner. We analyze the stability and overhead of the proposed algorithm, and show the improvement of the fairness via computer simulation. Additionally, we have implemented a prototype on a testbed to prove its feasibility. [ABSTRACT FROM AUTHOR]