In this paper, we present a method for resource allocation for small cells that integrate licensed and unlicensed RF operations motivated by the widespread WiFi hotspots and the common inclusion of WiFi interface in most cellular terminals. Small cells have proven popular for cell coverage enhancement and traffic offloading from macrocells. We formulate an optimization problem that jointly allocates resources over both licensed and unlicensed bands with the goal of maximizing sum small cell user equipment (SUE) rate while achieving fairness among these user equipments and controlling inter-cell interference to neighboring macrocell users. The proposed solution further considers the quality of service (QoS) requirement of SUE traffics to be distributed over both licensed and unlicensed bands. We show the formulation of the proposed optimization problem as an efficient and low complexity linear programming. We further show that our problem formulation can be modified to maximize the revenue of mobile network operators. Our proposed solution achieves better performance than several existing solutions.

We consider a femto cellular network consisting of multiple neighboring femtocells, e.g., in an enterprise deployment such as shopping malls, stadiums, or corporate premises. We present a practical but suboptimal channel assignment and interference management algorithm for fractional frequency reuse (FFR) wireless networks. More specifically, we propose an adaptive graph coloring approach for resource allocation with the goal of interference management among femtocells as well as achieving fairness among users. While the global-optimum solution has exponential complexity, our proposed scheme has a linear complexity in the number of femtocells. Although suboptimal, we have evaluated our algorithm in small scenarios, where direct evaluation is possible, and found that the achieved minimum user rate using the proposed algorithm is 85% of the optimal minimum rate. Additionally, we have analyzed several practical design considerations of our proposal such as channel feedback, latency, and computational complexity. We demonstrate the performance of our proposed solution against various alternatives and show that it provides better performance under various environment parameters. For example, in a dense femtocell deployment, the performance was improved by 47% over a full frequency reuse scheme.

In this paper, we propose a resource allocation scheme for interference management in heterogeneous networks. We particularly consider downlink interference from a Home eNB (HeNB) to macrocell user equipments (MUEs) in the coverage of the traditional macrocell basestation (MBS). By overhearing uplink feedback information from MUEs and Home User Equipments (HUEs) together with downlink control information (DCI) from the MBS, the HeNB formulates a dynamic programming problem with the objective of maximizing the total reward over an infinite horizon. By exploiting the feedback information as well as the DCI at the HeNB, we show that the solution of the dynamic programming problem follows a greedy policy at the HeNB, which simplifies the solution of the infinite horizon dynamic programming problem. We also examine the effect of uncertainty in the feedback information on the performance of our proposed scheme.

This paper studies interference mitigation in heterogeneous cellular networks consisting of traditional macrocells and newly envisioned femtocells. The mutual interference between macrocells and femtocells arises as a result of decentralized femtocell deployment and backhaul delay. To mitigate downlink interference between the femtocell clients, known as Home User Equipments (HUEs), and macrocell clients, known as Macrocell User Equipments (MUEs), we present methods of dynamic distributed beamforming that are fully compatible with MIMO precoding mechanisms in existing LTE standard releases. We develop three MIMO beamforming schemes for interference mitigation that take into account the Quality of Service (QoS) requirement of both femtocell and macrocell clients. These new heterogeneous MIMO precoding strategies improve flexibility in resource provisioning and signaling requirement while responding to different QoS needs. We also present MUE mean throughput analysis by applying order statistics to our proposed methods. Moreover, we provide an approximate closed form for the mean throughput in terms of basic transmitter, channel, and receiver parameters. Furthermore, we extend our proposed interference control precoding schemes to spatial multiplexing for MIMO transmissions. Finally, we extend our solution to tackle the more general case involving multiple MUEs, multiple HUEs, and multiple femtocells.

WiFi interfaces have been recently incorporated in most cellular user equipments (UEs). In current practice, the UE selects either the licensed band for cellular technologies or the unlicensed WiFi band depending on the signal quality of both bands. At the same time, small (pico or femto) cells have also become popular means to offload traffic from traditional macro-cell networks and to improve cell coverage. This work presents a method for dynamic switching and aggregation of licensed and unlicensed bands in small cells for traffic offloading and per-user throughput enhancement. Our proposed method allows small cells to jointly control transmission in both licensed and unlicensed bands in order to maximize the sum of small cell user throughputs over both bands while constraining the interference effect to maintain the Quality of Service (QoS) requirements for macrocell user equipments. Performance evaluation shows that our proposed scheme outperforms other existing solutions.

For an LTE user equipment to be able to connect to a cell, cell search must first be performed to obtain the cell identity and timing required to connect to the cell. In this paper, a complete solution is proposed for the detection of the Primary Synchronization Signal that carries the sector identity, which is part of the cell identity. A novel adaptive technique that adapts its parameters according to the channel conditions is presented. A blind channel delay spread estimation technique is also proposed and used for adapting the PSS detection technique. The proposed adaptive technique allows supporting a wide range of channel delay spreads. A method for coarse frequency offset estimation is also presented in the paper.

This work studies dual-access-technology femtocells equipped with both cellular and non-cellular air interfaces with the goal of increasing per-user throughput as well as achieving user fairness. We target enterprise environments characterized by high data rates and/or dense user terminals such as corporate premises, shopping malls, or stadiums. To meet this high data demand, multiple femtocells are deployed for which the major challenge is interference management. We propose an architecture for interference management and resource allocation over both cellular and non-cellular bands. Numerical evaluation shows that our proposed solution outperforms other existing solutions.

This work studies femtocell resource allocation in shared spectrum heterogeneous networks. We propose an enhanced shadow chasing method for femtocell resource allocation to achieve interference mitigation in heterogeneous networks serving both Macrocell User Equipments (MUEs) and Home User Equipments (HUEs). Shadow chasing home eNB (HeNB) uses Downlink Control Information (DCI) together with over-heard macrocell user ACK/NAK feedbacks and CQI reports to assign its own downlink resources to mitigate downlink interference to MUEs. Since the HeNB receives outdated DCI due to backhaul delay, we derive a likelihood metric for each resource unit being either empty or assigned to a (low-interference) outdoor MUE based on a finite-state Markov chain model for each resource unit. By dynamically separating MUE and HUE assignments, the enhanced shadow chasing can better control the downlink interference to MUEs for QoS assurance. It effectively reduces the probability of resource collision and MUE interference compared to schemes not considering backhaul delay effect or user feedbacks.

In this paper, we propose an enhancement to a resource allocation scheme known as shadow chasing for interference mitigation in heterogeneous networks composed of Macrocell User Equipments (MUEs) and Home User Equipments (HUEs). In shadow chasing, the Home eNB (HeNB) uses Downlink Control Information (DCI) together with the overheard ACK/NAK feedback and CQI reports to assign its HUEs the Physical Resource Blocks (PRBs) that are assigned to outdoor MUEs. Since the HeNB receives an outdated DCI due to backhaul delay, our solution is to derive a likelihood metric for each PRB being either empty or assigned to an outdoor MUE by using a MC model for the state of each PRB. By dynamically separating the MUE and HUE PRB assignments, enhanced shadow chasing can better constrain the downlink interference to the MUE. Our results show effective reduction of probability of PRB assignment collision and MUE interference compared to schemes that do not integrate the delay effect or exploit user feedbacks.

In this paper1, we study interference mitigation in cellular networks with femtocells. We propose to use adaptive distributed beamforming to mitigate downlink interference between femtocell users, known as Home User Equipments (HUEs), and macrocell users, known as Macrocell User Equipments (MUEs). We develop three MEVIO beamforming schemes for interference mitigation that take into account the QoS requirement of femtocell and macrocell clients. These new heterogeneous MIMO precoding strategies improve flexibility in resource provisioning and signaling requirement in response to QoS need. The proposed schemes minimize the interference at the MUE or maximize the throughput at the HUE depending on the network traffic and QoS constraints. We also analyze the MUE mean throughput by applying order statistics theory.

Cell search has to be performed by the user equipment in 3GPP Long Term Evolution (LTE) systems to obtain cell identity before connecting to the cell. The cell identity is transmitted using the Primary Synchronization Symbol (PSS) and the Secondary Synchronization Symbol (SSS). The PSS carries one of three possible values for the sector identity whereas the SSS carries one of 168 possible values for the physical cell identity. Straightforward implementation of SSS detection incurs a large area due to the large number of required correlators. This paper presents a complete design and hardware implementation for the detection of the SSS. The fixed point representation of the proposed system design is optimized to use the smallest word lengths at various system nodes while maintaining the overall performance within acceptable limits. Different architectures are studied, compared and an all-serial architecture is chosen to minimize the design area and power consumption, while achieving an acceptable acquisition time.